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Load Data from Freshdesk to Redshift in 2 East Steps
Are you looking to load data from Freshdesk to Redshift for deeper analysis? Or are you looking to simply create a backup of this data in your warehouse? Whatever be the use case, deciding to move data from Freshdesk to Redshift is a step in the right direction. This blog highlights the broad approaches and steps that one would need to take to reliably load data from Freshdesk to Redshift.What is Freshdesk?
Freshdesk is a cloud-based customer support platform owned by Freshworks. It integrates support platforms such as emails, live chat, phone and social media platforms like Twitter and Facebook.
Freshworks allows you to keep track of all ongoing tickets and manage all support-related communications across all platforms. Freshdesk generates reports that allow you to understand your team’s performance and gauge the customers’ satisfaction level.
Freshdesk offers well-defined and rich REST (Representation State Transfer) API. Using Freshdesk’s REST API, data on Freshdesk tickets, customer support, team’s performance, etc. can be extracted and loaded onto Redshift for deeper analysis.
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What is Amazon Redshift?
Amazon Redshift is a data warehouse owned and maintained by amazon web services (AWS) and forms a large part of the AWS cloud computing platform. It is built using MPP (massively parallel processing) architecture. Its ability to handle analytical workloads on a large volume of data sets stored in the column-oriented DBMS principles makes it different from Amazon’s other hosted database offerings.
Redshift makes it possible to query megabytes of structured and non-structured data using SQL. You can save the results back to your S3 data lake using formats like Apache Parquet. This allows you to further analyze from other analytical services like Amazon Athena, Amazon EMR, and Amazon SageMaker.
Find out more on Amazon Redshift Data Warehouse here.
Methodsto Load Data from Freshdesk to Redshift
This can be done in two ways:
Method 1: Loading Data from Freshdesk to Redshift Using Custom ETL Scripts
This would need you to invest in the engineering team’s bandwidth to build a custom solution. The process involves the following steps broadly. Getting data out using Freshdesk API, preparing Freshdesk data, and finally loading data into Redshift.
Method 2: Load Data from Freshdesk to Redshift Using LIKE.TG
LIKE.TG comes with out-of-the-box integration with Freshdesk (Free Data Source) and loads data to Redshift without having to write any code. LIKE.TG ’s ability to reliably load data in real-time combined with its ease of use makes it a great alternative to Method 1.
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Methodsto Load Data from Freshdesk to Redshift
Method 1: Loading Data from Freshdesk to Redshift Using Custom ETL ScriptsMethod 2: Load Data from Freshdesk to Redshift Using LIKE.TG
This article will provide an overview of both the above approaches. This will allow you to analyze the pros and cons of all approaches and select the best method as per your use case.
Method 1: Loading Data from Freshdesk to Redshift Using Custom ETL Scripts
Step 1: Getting Data from Freshdesk
The REST API provided by Freshdesk allows you to get data on agents, tickets, companies and any other information from their back-end. Most of the API calls are simple, for example, you could call GET /api/v2/tickets to list all tickets. Optional filters such as company ID, and updated date could be used to limit retrieved data. The include parameter could also be used to fetch fields that are not sent by default.
Freshdesk Sample Data
The information is returned in JSON format. Each JSON object may contain more than one attribute which should be parsed before loading the data in your data warehouse. Below is an example of the API call response made to return all tickets.
{
"cc_emails" : ["[email protected]"],
"fwd_emails" : [ ],
"reply_cc_emails" : ["[email protected]"],
"email_config_id" : null,
"fr_escalated" : false,
"group_id" : null,
"priority" : 1,
"requester_id" : 1,
"responder_id" : null,
"source" : 2,
"spam" : false,
"status" : 2,
"subject" : "",
"company_id" : 1,
"id" : 20,
"type" : null,
"to_emails" : null,
"product_id" : null,
"created_at" : "2015-08-24T11:56:51Z",
"updated_at" : "2015-08-24T11:59:05Z",
"due_by" : "2015-08-27T11:30:00Z",
"fr_due_by" : "2015-08-25T11:30:00Z",
"is_escalated" : false,
"description_text" : "Not given.",
"description" : "<div>Not given.</div>",
"custom_fields" : {
"category" : "Primary"
},
"tags" : [ ],
"requester": {
"email": "[email protected]",
"id": 1,
"mobile": null,
"name": "Rachel",
"phone": null
},
"attachments" : [ ]
}
Step 2: Freshdesk Data Preparation
You should create a data schema to store the retrieved data. Freshdesk documentation provides the data types to use, for example, INTEGER, FLOAT, DATETIME, etc.
Some of the retrieved data may not be “flat” – they maybe list. Therefore, to capture unpredictable cardinality in each of the records, additional tables may need to be created.
Step 3: Loading Data to Redshift
When you have high volumes of data to be stored, you should load data into Amazon S3 and load into Redshift using the copy command. Often times when dealing with low volumes of data, you may think of loading the data using the INSERT statement. This will load the data row by row and slow the process because Redshift isn’t optimized to load data in this way.
Freshdesk to Redshift Using Custom Code: Limitations and Challenges
Accessing Freshdesk Data in Real-time: At this stage, you have successfully created a program that loads data into the data warehouse. The challenge of loading new or updated data is not solved yet. You could decide to replicate data in real-time, each time a new or updated record is created. This process will be slow and resource-intensive. You will need to write additional code and build cron jobs to run this in a continuous loop to get new and updated data as it appears in the Freshdesk.Infrastructure Maintainance: Always remember that any code that is written should be maintained because Freshdesk may modify its API or a datatype that your script doesn’t recognize may be sent by the API.
Method 2: Load Data from Freshdesk to Redshift Using LIKE.TG
A more elegant, hassle-free alternative to loading data from Freshdesk (Free Data Source) to Redshift would be to use a Data Integration Platform like LIKE.TG (14-day free trial) that works out of the box. Being a no-code platform, LIKE.TG can overcome all the limitations mentioned above and seamlessly and securely more Freshdesk data to Redshift in just two steps:
Authenticate and Connect Freshdesk Data SourceConfigure the Redshift Data warehouse where you need to move the data
Sign up here for a 14-Day Free Trial!
Advantages of Using LIKE.TG
The LIKE.TG data integration platform lets you move data from Freshdesk (Free Data Source) to Redshift seamlessly. Here are some other advantages:
No Data Loss – LIKE.TG ’s fault-tolerant architecture ensures that data is reliably moved from Freshdesk to Redshift without data loss.100’s of Out of the Box Integrations – In addition to Freshdesk, LIKE.TG can bring data from 100+ Data Sources (Including 30+ Free Data Sources)into Redshift in just a few clicks. This will ensure that you always have a reliable partner to cater to your growing data needs.Minimal Setup – Since LIKE.TG is a fully managed, setting up the platform would need minimal effort and bandwidth from your end.Automatic schema detection and mapping – LIKE.TG automatically scans the schema of incoming Freshdesk data. If any changes are detected, it handles this seamlessly by incorporating this change on Redshift.Exceptional Support – LIKE.TG provides 24×7 support to ensure that you always have Technical support for LIKE.TG is provided on a 24/7 basis over both email and Slack.
As an alternate option, if you use Google BigQuery, you can also load your data from Freshdesk to Google BigQuery using this guide here.
Conclusion
This article teaches you how to set up Freshdesk to Redshift Data Migration with two methods. It provides in-depth knowledge about the concepts behind every step to help you understand and implement them efficiently.
The first method, however, can be challenging especially for a beginner this is where LIKE.TG saves the day.LIKE.TG Data, a No-code Data Pipeline, helps you transfer data from a source of your choice in a fully-automated and secure manner without having to write the code repeatedly.
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LIKE.TG , with its strong integration with100+ sources BI tools, allows you to not only export load data but also transform enrich your data make it analysis-ready in a jiff.
Want to take LIKE.TG for a spin?Sign Up here for the 14-day free trialand experience the feature-rich LIKE.TG suite first hand.
Tell us about your experience of setting up Freshdesk to Redshift Data Transfer! Share your thoughts in the comments section below!
Google Analytics to PostgreSQL: 2 Easy Methods
Even though Google provides a comprehensive set of analysis tools to work with data, most organizations will need to pull the raw data into their on-premise database. This is because having it in their control allows them to combine it with their customer and product data to perform a much deeper analysis. This post is about importing data from Google Analytics to PostgreSQL – one of the very popular relational databases in the market today. This blog covers two approaches for integrating GA with PostgreSQL – The first approach talks about using an automation tool extensively. Alternatively, the blog also covers the manual method for achieving the integration.
Methods to Connect Google Analytics to PostgreSQL
Method 1: Using LIKE.TG Data to Connect Google Analytics to PostgreSQL
LIKE.TG is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates flexible data pipelines to your needs. With integration with 150+ Data Sources (40+ free sources), including Google Analytics, we help you not only export data from sources load data to the destinations but also transform enrich your data, make it analysis-ready.
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Method 2: Using Manual ETL Scripts to Connect Google Analytics to PostgreSQL
Manually coding custom ETL (extract, transform, load) scripts enables precise customization of the data transfer process, but requires more development effort compared to using automated tools.
Method 1: Using LIKE.TG Data to Connect Google Analytics to PostgreSQL
The best way to connect Google Analytics to PostgreSQL is to use a Data Pipeline Platform like LIKE.TG (14-day free trial) that works out of the box. LIKE.TG can help you import data from Google Analytics to PostgreSQL for free in two simple steps:
Step 1: Connect LIKE.TG to Google Analytics to set it up as your source by filling in the Pipeline Name, Account Name, Property Name, View Name, Metrics, Dimensions, and the Historical Import Duration.
Step 2: Load data from Google Analytics to Postgresql by providing your Postgresql databases credentials like Database Host, Port, Username, Password, Schema, and Name along with the destination name.
LIKE.TG will do all the heavy lifting to ensure that your data is securely moved from Google Analytics to PostgreSQL. LIKE.TG automatically handles all the schema changes that may happen at Google Analytics’ end. This ensures that you have a dependable infrastructure that delivers error-free data in PostgreSQL at all points.
Here are a few benefits of using LIKE.TG :
Easy-to-use Platform: LIKE.TG has a straightforward and intuitive UI to configure the jobs.
Transformations: LIKE.TG provides preload transformations through Python code. It also allows you to run transformation code for each event in the Data Pipelines you set up. You need to edit the event object’s properties received in the transform method as a parameter to carry out the transformation. LIKE.TG also offers drag and drop transformations like Date and Control Functions, JSON, and Event Manipulation to name a few. These can be configured and tested before putting them to use.
Real-time Data Transfer: Support for real-time synchronization across a variety of sources and destinations.
Automatic Schema Mapping: LIKE.TG can automatically detect your source’s schema type and match it with the schema type of your destination.
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Method 2: Using Manual ETL Scripts to Connect Google Analytics to PostgreSQL
In this method of moving data from Google Analytics to PostgreSQL, you will first need to get data from Google Analytics followed by accessing Google Reporting API V4 as mentioned in the following section.
Getting data from Google Analytics
Click event data from Google Analytics can be accessed through Reporting API V4. There are two sets of Rest APIs in Reporting API V4 tailor-made for specific use cases.
Metrics API – These APIs allow users to get aggregated analytics information on user behavior based on available dimensions. Dimensions are the attributes based on which metrics are aggregated. For example, if time is a dimension and the number of users in a specific time will be a metric.
User Activity API – This API allows you to access information about the activities of a specific user. Knowledge of the user ID is required in this case. To get the user IDs of people accessing your page, you will need to modify some bits in the client-side Google Analytics function that you are going to use and capture the client ID. This information is not exactly available in the Google developer documentation, but there is ample online documentation about it. Ensure you consult the laws and restrictions in your local country before attempting this since its legality will depend on the country’s privacy laws. After changing the client script, you must also register the user ID as a custom dimension in the Google Analytics dashboard.
Google Analytics APIs use oAuth 2.0 as the authentication protocol. Before accessing the APIs, the user first needs to create a service account in the Google Analytics dashboard and generate authentication tokens. Let us review how this can be done.
Go to the Google service accounts page and select a project. If you have not already created a project, please create one.
Click on Create Service Account.
You can ignore the permissions for this exercise.
On the ‘Grant users access to this service account’ section, click Create key.
Select JSON as the format for your key.
Click create a key and you will be prompted with a dialogue to save the key on your local computer. Save the key.
We will be using the information from this step when we actually access the API.
Accessing Google Reporting API V4
Google provides easy-to-use libraries in Python, Java, and PHP to access its reporting APIs. These libraries are the preferred method to download the data since the authentication procedure and the complex JSON response format makes it difficult to access these APIs using command-line tools like CURL. Detailed documentation of this API can be found here. Here the python library is used to access the API. The following steps and code snippets explain the procedure to load data from Google Analytics to PostgreSQL:
Step 1: Installing the Python GA Library to Your Environment
Step 2: Importing the Required Libraries
Step 3: Initializing the Required Variables for OAuth Authentication
Step 4: Building the Required Objects
Step 5: Executing the Method to Get Data
Step 6: Parsing JSON and Writing the Contents to a CSV File
Step 7: Loading CSV File to PostgreSQL
Step 1: Installing the Python GA Library to Your Environment
sudo pip install --upgrade google-api-python-client
Before this step, please ensure the python programming environment is already installed and works fine. We will now start writing the script for downloading the data as a CSV file.
Step 2: Importing the Required Libraries
from apiclient.discovery import build
from oauth2client.service_account import ServiceAccountCredentials
Step 3: Initializing the Required Variables for OAuth Authentication
credentials = ServiceAccountCredentials.from_json_keyfile_name(KEY_FILE_LOCATION, SCOPES)
# Build the service object.
analytics = build('analyticsreporting', 'v4', credentials=credentials)
Replace the key file location and view ID with what we obtained in the first service creation step. View ids are the views from which you will be collecting the data. To get the view ID of a particular view that you have already configured, go to the admin section, click on the view that you need, and go to view settings.
Step 4: Building the Required Objects
credentials = ServiceAccountCredentials.from_json_keyfile_name(KEY_FILE_LOCATION, SCOPES)#Build the service object
analytics = build('analyticsreporting', 'v4', credentials=credentials)
Step 5: Executing the Method to Get Data
In this step, you need to execute the method to get the data. The below query is for getting the number of users aggregated by country from the last 7 days.
response = analytics.reports().batchGet(body={
'reportRequests': [
{
'viewId': VIEW_ID,
'dateRanges': [{'startDate': '7daysAgo', 'endDate': 'today'}],
'metrics': [{'expression': 'ga:sessions'}],
'dimensions': [{'name': 'ga:country'}]
}]
}
).execute()
Step 6: Parsing JSON and Writing the Contents to a CSV File
import pandas as pd from pandas.io.json
import json_normalize
reports = response['reports'][0]
columnHeader = reports['columnHeader']['dimensions']
metricHeader = reports['columnHeader']['metricHeader']['metricHeaderEntries'] columns = columnHeader for metric in metricHeader:
columns.append(metric['name'])
data = json_normalize(reports['data']['rows'])
data_dimensions = pd.DataFrame(data['dimensions'].tolist())
data_metrics = pd.DataFrame(data['metrics'].tolist())
data_metrics = data_metrics.applymap(lambda x: x['values'])
data_metrics = pd.DataFrame(data_metrics[0].tolist())
result = pd.concat([data_dimensions, data_metrics], axis=1, ignore_index=True)
result.to_csv('reports.csv')
Save the script and execute it. The result will be a CSV file with the following column:
Id , ga:country, ga:sessions
Step 7: Loading CSV File to PostgreSQL
This file can be directly loaded to a PostgreSQL table using the below command. Please ensure the table is already created
COPY sessions_tableFROM 'reports.csv' DELIMITER ',' CSV HEADER;
The above command assumes you have already created a table named sessions_table.
You now have your google analytics data in your PostgreSQL table. Now that we know how to do get the Google Analytics data using custom code, let’s look into the limitations of using this method.
Limitations of using Manual ETL Scripts to Connect Google Analytics to PostgreSQL
The above method requires you to write a lot of custom code. Google’s output JSON structure is a complex one and you may have to make changes to the above code according to the data you query from the API.
This approach is fine for a one-off data load to PostgreSQL, but in a lot of cases, organizations need to do this periodically and merge the data point every day while handling duplicates. This will force you to write a very complex import tool just for Google Analytics.
The above method addresses only one API that is available for Google Analytics. There are many other available APIs from Google analytics that provide different types of data. An example is a real-time API. All these APIs come with a different output JSON structure and the developers will need to write separate parsers.
The APIs are rate limited which means the above approach will lead to errors if complex logic is not implemented to throttle the API calls.
A solution to all the above problems is to use a completely managed ETL solution like LIKE.TG which provides a simple click and execute interface to move data from Google Analytics to PostgreSQL.
Use Cases to transfer your Google Analytics 4 (GA4) data to Postgres
There are several advantages to integrating Google Analytics 4 (GA4) data with Postgres. A few use cases are as follows:
Advanced Analytics: With Postgres’ robust data processing features, you can extract insights from your Google Analytics 4 (GA4) data that are not feasible with Google Analytics 4 (GA4) alone. You can execute sophisticated queries and data analysis on your data.
Data Consolidation: Syncing to Postgres enables you to centralize your data for a comprehensive picture of your operations and to build up a change data capturing procedure that ensures there are never any inconsistencies in your data again if you’re utilizing Google Analytics 4 (GA4) together with many other sources.
Analysis of Historical Data: Historical data in Google Analytics 4 (GA4) is limited. Data sync with Postgres enables long-term data storage and longitudinal trend analysis.
Compliance and Data Security: Strong data security protections are offered by Postgres. Syncing Google Analytics 4 (GA4) data with Postgres enables enhanced data governance and compliance management while guaranteeing the security of your data.
Scalability: Growing enterprises with expanding Google Analytics 4 (GA4) data will find Postgres to be an appropriate choice since it can manage massive amounts of data without compromising speed.
Machine Learning and Data Science: You may apply machine learning models to your data for predictive analytics, consumer segmentation, and other purposes if you have Google Analytics 4 (GA4) data in Postgres.
Reporting and Visualization: Although Google Analytics 4 (GA4) offers reporting capabilities, more sophisticated business intelligence alternatives may be obtained by connecting to Postgres using data visualization tools like Tableau, PowerBI, and Looker (Google Data Studio). Airbyte can automatically convert your Google Analytics 4 (GA4) table to a Postgres table if needed.
Conclusion
This blog discusses the two methods you can deploy to connect Google Analytics to PostgreSQL seamlessly. While the custom method gives the user precise control over data, using automation tools like LIKE.TG can solve the problem easily.
Visit our Website to Explore LIKE.TG
While Google Analytics used to offer free website analytics, it’s crucial to remember that the program is currently built on a subscription basis. Presently, the free version is called Google Analytics 360, and it still offers insightful data on user behavior and website traffic. In addition to Google Analytics, LIKE.TG natively integrates with many other applications, including databases, marketing and sales applications, analytics applications, etc., ensuring that you have a reliable partner to move data to PostgreSQL at any point.
Want to take LIKE.TG for a ride? Sign Up for a 14-day free trial and simplify your Data Integration process. Do check out the pricing details to understand which plan meets all your business needs.
Tell us in the comments about your experience of connecting Google Analytics to PostgreSQL!
Loading Data to Redshift: 4 Best Methods
Amazon Redshift is a petabyte-scale Cloud-based Data Warehouse service. It is optimized for datasets ranging from a hundred gigabytes to a petabyte can effectively analyze all your data by allowing you to leverage its seamless integration support for Business Intelligence tools Redshift offers a very flexible pay-as-you-use pricing model, which allows the customers to pay for the storage and the instance type they use. Increasingly, more and more businesses are choosing to adopt Redshift for their warehousing needs. In this article, you will gain information about one of the key aspects of building your Redshift Data Warehouse: Loading Data to Redshift. You will also gain a holistic understanding of Amazon Redshift, its key features, and the different methods for loading Data to Redshift. Read along to find out in-depth information about Loading Data to Redshift.
Methods for Loading Data to Redshift
There are multiple ways of loading data to Redshift from various sources. On a broad level, data loading mechanisms to Redshift can be categorized into the below methods:
Method 1: Loading an Automated Data Pipeline Platform to Redshift Using LIKE.TG ’s No-code Data Pipeline
LIKE.TG ’s Automated No Code Data Pipeline can help you move data from 150+ sourcesswiftly to Amazon Redshift. You can set up the Redshift Destination on the fly, as part of the Pipeline creation process, or independently. The ingested data is first staged in LIKE.TG ’s S3 bucket before it is batched and loaded to the Amazon Redshift Destination. LIKE.TG can also be used to perform smooth transitions to Redshift such as DynamoDB load data from Redshift and to load data from S3 to Redshift.
LIKE.TG ’s fault-tolerant architecture will enrich and transform your data in a secure and consistent manner and load it to Redshift without any assistance from your side. You can entrust us with your data transfer process by both ETL and ELT processes to Redshift and enjoy a hassle-free experience.
LIKE.TG Data focuses on two simple steps to get you started:
Step 1: Authenticate Source
Connect LIKE.TG Data with your desired data source in just a few clicks. You can choose from a variety of sources such as MongoDB, JIRA, Salesforce, Zendesk, Marketo, Google Analytics, Google Drive, etc., and a lot more.
Step 2: Configure Amazon Redshift as the Destination
You can carry out the following steps to configure Amazon Redshift as a Destination in LIKE.TG :
Clickon the “DESTINATIONS”option in theAsset Palette.
Clickthe “+ CREATE”option in theDestinations List View.
On theAdd Destinationpage, selectthe Amazon Redshift option.
In theConfigure your Amazon Redshift Destinationpage, specify the following: Destination Name, Database Cluster Identifier, Database Port, Database User, Database Password, Database Name, Database Schema.
Clickthe Test Connectionoption to test connectivity with the Amazon Redshift warehouse.
After the is successful, clickthe “SAVE DESTINATION” button.
Here are more reasons to try LIKE.TG :
Integrations: LIKE.TG ’s fault-tolerant Data Pipeline offers you a secure option to unify data from150+ sources(including 40+ free sources)and store it in Redshift or any other Data Warehouse of your choice. This way you can focus more on your key business activities and let LIKE.TG take full charge of the Data Transfer process.
Schema Management:LIKE.TG takes away the tedious task of schema management automatically detects the schema of incoming data and maps it to yourRedshift schema.
Quick Setup: LIKE.TG with its automated features, can be set up in minimal time. Moreover, with its simple and interactive UI, it is extremely easy for new customers to work on and perform operations.
LIKE.TG Is Built To Scale:As the number of sources and the volume of your data grows, LIKE.TG scales horizontally, handling millions of records per minute with very little latency.
Live Support:The LIKE.TG team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
With continuous Real-Time data movement, LIKE.TG allows you to assemble data from multiple data sources and seamlessly load it to Redshift with a no-code, easy-to-setup interface. Try our 14-day full-feature access free trial!
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Seamlessly Replicate Data from 150+ Data Sources in minutes
LIKE.TG Data, an AutomatedNo-code Data Pipeline, helps you load data to Amazon Redshift in real-time and provides you with a hassle-free experience. You can easily ingest data using LIKE.TG ’s Data Pipelines and replicate it to your Redshift warehouse without writing a single line of code.
Get Started with LIKE.TG for Free
LIKE.TG supports direct integrations of 150+ sources (including 40+ free sources) and its Data Mapping feature works continuously to replicate your data to Redshift and builds a single source of truth for your business. LIKE.TG takes full charge of the data transfer process, allowing you to focus your resources and time on other key business activities.
Experience an entirely automated hassle-free process of loading data to Redshift. Try our 14-day full access free trial today!
Method 2: Loading Data to Redshift using the Copy Command
The Redshift COPY command is the standard way of loading bulk data TO Redshift. COPY command can use the following sources for loading data.
DynamoDB
Amazon S3 storage
Amazon EMR cluster
Other than specifying the locations of the files from where data has to be fetched, the COPY command can also use manifest files which have a list of file locations. It is recommended to use this approach since the COPY command supports the parallel operation and copying a list of small files will be faster than copying a large file. This is because, while loading data from multiple files, the workload is distributed among the nodes in the cluster.
Download the Cheatsheet on How to Set Up High-performance ETL to Redshift
Learn the best practices and considerations for setting up high-performance ETL to Redshift
COPY command accepts several input file formats including CSV, JSON, AVRO, etc.
It is possible to provide a column mapping file to configure which columns in the input files get written to specific Redshift columns.
COPY command also has configurations to simple implicit data conversions. If nothing is specified the data types are converted automatically to Redshift target tables’ data type.
The simplest COPY command for loading data from an S3 location to a Redshift target table named product_tgt1 will be as follows. A redshift table should be created beforehand for this to work.
copy product_tgt1
from 's3://productdata/product_tgt/product_tgt1.txt'
iam_role 'arn:aws:iam::<aws-account-id>:role/<role-name>'
region 'us-east-2';
Method 3: Loading Data to Redshift using Insert Into Command
Redshift’s INSERT INTO command is implemented based on the PostgreSQL. The simplest example of the INSERT INTO command for inserting four values into a table named employee_records is as follows.
INSERT INTO employee_records(emp_id,department,designation,category)
values(1,’admin’,’assistant’,’contract’);
It can perform insertions based on the following input records.
The above code snippet is an example of inserting single row input records with column names specified with the command. This means the column values have to be in the same order as the provided column names.
An alternative to this command is the single row input record without specifying column names. In this case, the column values are always inserted into the first n columns.
INSERT INTO command also supports multi-row inserts. The column values are provided with a list of records.
This command can also be used to insert rows based on a query. In that case, the query should return the values to be inserted into the exact columns in the same order specified in the command.
Even though the INSERT INTO command is very flexible, it can lead to surprising errors because of the implicit data type conversions. This command is also not suitable for the bulk insert of data.
Method 4: Loading Data to Redshift using AWS Services
AWS provides a set of utilities for loading data To Redshift from different sources. AWS Glue and AWS Data pipeline are two of the easiest to use services for loading data from AWS table.
AWS Data Pipeline
AWS data pipeline is a web service that offers extraction, transformation, and loading of data as a service. The power of the AWS data pipeline comes from Amazon’s elastic map-reduce platform. This relieves the users of the headache to implement a complex ETL framework and helps them focus on the actual business logic. To have a comprehensive knowledge of AWS Data Pipeline, you can also visit here.
AWS Data pipeline offers a template activity called RedshiftCopyActivity that can be used to copy data from different kinds of sources to Redshift. RedshiftCopyActivity helps to copy data from the following sources.
Amazon RDS
Amazon EMR
Amazon S3 storage
RedshiftCopyActivity has different insert modes – KEEP EXISTING, OVERWRITE EXISTING, TRUNCATE, APPEND.
KEEP EXISTING and OVERWRITE EXISTING considers the primary key and sort keys of Redshift and allows users to control whether to overwrite or keep the current rows if rows with the same primary keys are detected.
AWS Glue
AWS Glue is an ETL tool offered as a service by Amazon that uses an elastic spark backend to execute the jobs. Glue has the ability to discover new data whenever they come to the AWS ecosystem and store the metadata in catalogue tables.You can explore in detail the importance of AWS Glue from here.
Internally Glue uses the COPY and UNLOAD command to accomplish copying data to Redshift. For executing a copying operation, users need to write a glue script in its own domain-specific language.
Glue works based on dynamic frames. Before executing the copy activity, users need to create a dynamic frame from the data source. Assuming data is present in S3, this is done as follows.
connection_options = {"paths": [ "s3://product_data/products_1", "s3://product_data/products_2"]}
df = glueContext.create_dynamic_frame_from_options("s3_source", connection-options)
The above command creates a dynamic frame from two S3 locations. This dynamic frame can then be used to execute a copy operation as follows.
connection_options = {
"dbtable": "redshift-target-table",
"database": "redshift-target-database",
"aws_iam_role": "arn:aws:iam::account-id:role/role-name"
}
glueContext.write_dynamic_frame.from_jdbc_conf(
frame = s3_source,
catalog_connection = "redshift-connection-name",
connection_options = connection-options,
redshift_tmp_dir = args["TempDir"])
The above method of writing custom scripts may seem a bit overwhelming at first. Glue can also auto-generate these scripts based on a web UI if the above configurations are known.
Benefits of Loading Data to Redshift
Some of the benefits of loading data to Redshift are as follows:
1) It offers significant Query Speed Upgrades
Amazon’s Massively Parallel Processing allows BI tools that use the Redshift connector to process multiple queries across multiple nodes at the same time, reducing workloads.
2) It focuses on Ease of use and Accessibility
MySQL (and other SQL-based systems) continue to be one of the most popular and user-friendly database management interfaces. Its simple query-based system facilitates platform adoption and acclimation. Instead of creating a completely new interface that would require significant resources and time to learn, Amazon chose to create a platform that works similarly to MySQL, and it has worked extremely well.
3) It provides fast Scaling with few Complications
Redshift is a cloud-based application that is hosted directly on Amazon Web Services, the company’s existing cloud infrastructure. One of the most significant advantages this providesRedshift is a scalable architecture that can scale in seconds to meet changing storage requirements.
4) It keeps Costs relatively Low
Amazon Web Services bills itself as a low-cost solution for businesses of all sizes. In line with the company’s positioning, Redshift offers a similar pricing model that provides greater flexibility while enabling businesses to keep a closer eye on their data warehousing costs. This pricing capability stems from the company’s cloud infrastructure and its ability to keep workloads to a minimum on the majority of nodes.
5) It gives you Robust Security Tools
Massive data sets frequently contain sensitive data, and even if they do not, they contain critical information about their organisations. Redshift provides a variety of encryption and security tools to make warehouse security even easier.
These all features make Redshift one of the best Data Warehouses to securely and efficiently load data in. A No-Code Data Pipeline such asLIKE.TG Data provides you with a smooth and hassle-free process for loading data to Redshift.
Conclusion
The above sections detail different ways of copying data to Redshift. The first two methods of COPY and INSERT INTO command use Redshift’s native ability, while the last two methods build abstraction layers over the native methods. Other than this, it is also possible to build custom ETL tools based on the Redshift native functionality. AWS’s own services have some limitations when it comes to data sources outside the AWS ecosystem. All of this comes at the cost of time and precious engineering resources.
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LIKE.TG Datais the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines that are flexible to your needs. With integration with 150+ Data Sources such as PostgreSQL, MySQL, and MS SQL Server, we help you not only export data from sources load data to the destinations but also transform enrich your data, make it analysis-ready.
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Share your experience of understanding Loading data to Redshift in the comment section below! We would love to hear your thoughts.
SQS to S3: Move Data Using AWS Lambda and AWS Firehose
AWS Simple Queue Service is a completely managed message queue service offered by Amazon. Queue services are typically used to decouple systems and services in the microservice architecture. In that sense, SQS is a software-as-a-service alternative for queue systems like Kafka, RabbitMQ, etc. AWS S3 or Simple Storage Service is another software-as-a-service offered by Amazon. S3 is a complete solution for any kind of storage needs for up to 5 terabytes. SQS and S3 form an integral part of applications exploiting cloud-based microservices architecture and it is very common to have a requirement of transferring messages from SQS to S3 to keep a historical record of everything that is coming through the queue. This post is about the methods to accomplish this transfer.
What is SQS?
SQS frees the developers from the complexity and effort associated with developing, maintaining, and operating a highly reliable queue layer. It helps to send, receive and store messages between software systems. The standard size of messages is capped at 256 KBs. But with the extended AWS SDK, a message size of up to 2 GB is supported. Messages greater than 256KB in size will by default be using S3 as the internal storage. One of the greatest advantages of using SQS instead of traditional queue systems like Kafka is that it allows virtually unlimited scaling without the customer having to worry about capacity planning or pre-provisioning.
AWS offers a very flexible pricing plan for SQS based on the pay-as-you-go model and it provides significant cost savings when compared to the always-on model.
Behind the scenes, SQS messages are stored in distributed SQS servers for redundancy. SQS offers two types of queues – A standard queue and a FIFO queue. Standard queue offers at least one guarantee which means that occasionally duplicate messages might reach the receiver. The FIFO queue is designed for applications where the order of the events and uniqueness of the messages is critical. It provides an exactly-once guarantee.
SQS offers a dead-letter queue for routing problematic or erroneous messages that can not be processed in normal conditions. Amazon offers a standard queue at .40$ per 1 million requests and the FIFO queue at .50$ per 1 million requests. The total cost of ownership will also include data storage costs.
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What is S3?
AWS S3 is a completely managed object storage service that can be used for a variety of use cases like hosting data, backup and archiving, data warehousing, etc. Amazon handles all operation and maintenance activities related to scaling, provisioning, etc. and the customers only need to pay for the storage that they use. It offers fine-grained access controls to meet any kind of organizational and business compliance requirements through an easy-to-use management user interface. S3 also supports analytics through the use of AWS Athena and AWS Redshift Spectrum which enables users to execute SQL scripts on the stored data. S3 data is encrypted by default at rest.
S3 achieves state-of-the-art availability by storing the data across distributed servers. A caveat to this approach is that there is normally a propagation delay and S3 only guarantees eventual consistency. That said, the writes are atomic; which means at any point, the API will return either the old data or new data and never a corrupted response. Conceptually S3 is organized as buckets and objects.
A bucket is the highest level S3 namespace and acts as a container for storing objects. They have a critical role in access control and usage reporting is always aggregated at the bucket level. An object is the fundamental storage entity and consists of the actual object as well as the metadata. An object is uniquely identified by a unique key and a version identifier. Customers can choose the AWS regions in which their buckets need to be located according to their cost and latency requirements.
A point to note here is that objects do not support locking and if two PUTs come at the same time, the request with the latest timestamp will win. This means if there is concurrent access, users will have to implement some kind of locking mechanism on their own.
Steps to Load data fromSQS to S3
The most straightforward approach to transfer data from SQS to S3 is to use standard AWS services like Lambda functions and AWS firehose. AWS Lambda functions are serverless functions that allow users to execute arbitrary logic using amazon’s infrastructure. These functions can be triggered based on specific events or scheduled based on required intervals.
It is pretty straightforward to write a Lambda function to execute based on messages from SQS and write it to S3. The caveat is that this will create an S3 object for every message that is received and this is not always the ideal outcome. To create files in S3 after buffering the SQS messages for a fixed interval of time, there are two approaches for SQS to S3 data transfer:
Through a Scheduled Lambda FunctionUsing a Triggered Lambda Function and AWS Firehose
1) Through a Scheduled Lambda Function
A scheduled Lambda function for SQS to S3 transfer is executed in predefined intervals and can consume all the SQS messages that were produced during that specific interval. Once it processes all the messages, it can create a multi-part S3 upload using API calls. To schedule a Lambda function that transfers data from SQS to S3, execute the below steps.
Sign in to the AWS console and go to the Lambda console.Choose to create a function.For the execution role, select create a new execution role with Lambda permissions.Choose to use a blueprint. Blueprints are prototype code snippets that are already implemented to provide examples for users. Search for hello-world blueprint in the search box and choose it.
Click create function. On the next page, click to add a trigger.
In the trigger search menu, search and select CloudWatch events. CloudWatch events are used to schedule Lambda functions.Click create a new rule and select rule type as scheduled expression. Scheduled expression takes a Cron expression. You can enter a valid Cron expression corresponding to your execution strategy.
The Lambda function will contain code to access the SQS and to execute a multi-part upload to S3. S3 mandates that all single file uploads greater than 500 MB should be multipart.Choose create a function to activate the Lambda function.Once this is configured, AWS CloudWatch will generate events according to the cron expression, schedule, and trigger the Lambda function.
A problem with this approach is that Lambda functions have an execution time ceiling of 15 minutes and a usable memory ceiling of 3008 MB. If there are a large number of SQS events, you can run out of time and memory limits leading to dropping messages.
2) Using a Triggered Lambda Function and AWS Firehose
A deterrent to using a triggered Lambda function to move data from SQS to S3 was that it would create an S3 object per message leading to a large number of destination files. A workaround to avoid this problem is to use a buffered delivery stream that can write to S3 in predefined intervals. This approach involves the following broad set of steps.
Step 1: Create a triggered Lambda function
To create a triggered Lambda function for SQS to S3 data transfer, follow the same steps from the first approach. Instead of selecting a schedule expression select triggers. Amazon will provide you with a list of possible triggers. Select the SQS trigger and click create function. In the Lambda function write a custom code to redirect the SQS messages to Kinesis Firehose Delivery Stream.
Step 2: Create a Firehose Delivery Stream
To create a delivery stream, go to the AWS console and select the Kinesis Data Firehose Console.
Choose the destination as S3. In the configuration options, you will be presented with options to select the buffer size and buffer interval.
Buffer size is the amount of data up to which kinesis firehose will buffer the messages before writing to S3 as an object. You can have any value from 1 MB to 128 MB here.Buffer interval is the amount of time up to which the firehose will wait before it writes to S3. You can select any value from 60 seconds to 900 seconds here. After selecting the buffer size and buffer interval, you can leave the other parameters as default and click on create. That completes the pipeline to transfer data from SQS to S3.
The main limitation of this approach is that the user does not have close control over when to write to S3 beyond the buffer interval and buffer size limits imposed by Amazon. These limits are not always practical in real scenarios.
What Makes Your Data Integration Experience With LIKE.TG Unique?
These are some benefits of having LIKE.TG Data as your Data Automation Partner:
Secure: LIKE.TG has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.Auto Schema Mapping: LIKE.TG takes away the tedious task of schema management automatically detects the schema of incoming data and maps it to the S3 schema.Integrate With Custom Sources:LIKE.TG allows businesses to move data from 100+ Data Sources straight to thier desired destination.Quick Setup: LIKE.TG with its automated features, can be set up in minimal time. Moreover, with its simple and interactive UI, it is extremely easy for new customers to work on and perform operations using just 3 simple steps.LIKE.TG Is Built To Scale: As the number of sources and the volume of your data grows, LIKE.TG scales horizontally, handling millions of records per minute with very little latency.Incremental Data Load: LIKE.TG allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.Live Support: The LIKE.TG team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
With continuous real-time data movement, ETL your data seamlessly from your data sources to a destination of your choice with LIKE.TG ’s easy-to-setup and No-code interface. Try our14-dayfull access free trial!
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SQS to S3: Limitations of the Custom-Code Approach
Both the approaches mentioned for SQS to S3 data transfer use AWS-provided functions. An obvious advantage here is that you can implement the whole pipeline staying inside the AWS ecosystem. But these approaches have a number of limitations as mentioned below.
Both approaches require a lot of custom coding and knowledge of AWS proprietary configurations. Some of these configurations are very confusing and can lead to a significant amount of time and effort expense.AWS imposes multiple limits for execution time, run time memory, and storage memory in case of the services that we used to accomplish this transfer. This is not always practical in real scenarios.
Conclusion
In this blog, you learned how to move data from SQS to S3 using AWS Lambda and AWS Firehouse. You also went through the limitations of using custom code for SQS to S3 data migration. The AWS Lambda and Firehouse-based approach for loading data from SQS to S3 will consume a significant amount of time and resources. Moreover, it will be an error-prone method and you will be required to debug and maintain the data transfer process regularly.
LIKE.TG Data provides an Automated No-code Data Pipeline that empowers you to overcome the above-mentioned limitations. LIKE.TG caters to 100+ data sources (40+ free sources). Furthermore, LIKE.TG ’s fault-tolerant architecture ensures a consistent and secure transfer of your data to a Data Warehouse. Using LIKE.TG will make your life easier and make Data Transfer hassle-free.
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Share your experience of loading data from SQS to S3 in the comment section below.
HubSpot to Snowflake Integration: 2 Easy Methods
The advent of the internet and the cloud has paved the way for SaaS companies like Shopify to simplify the cumbersome task of setting up and running a business online. The businesses that use Shopify have crucial data about their customers, products, catalogs, orders, etc. within Shopify and would often need to extract this data out of Shopify into a central database and combine this with their advertising, ads, etc. to derive meaningful insights. PostgreSQL has emerged as a top ORDBMS (object-relational database management system) that is highly extensible with technical standards compliance. PostgreSQL’s ease of set up and
Shopify to BigQuery: 2 Easy Methods
You have your complete E-Commerce store set up on Shopify. You Collect data on the orders placed, Carts abandoned, Products viewed, and so on. You now want to move all of this data on Shopify to a robust Data Warehouse such as Google BigQuery so that you can combine this information with data from many other sources and gain deep insights. Well, you have landed on the right blog. This blog will discuss 2 step-by-step methods for moving data from Shopify to BigQuery for analytics. First, it will provide a brief introduction to Shopify and
Amazon S3 to Redshift: 3 Easy Methods
You have your complete E-Commerce store set up on Shopify. You Collect data on the orders placed, Carts abandoned, Products viewed, and so on. You now want to move all of this data on Shopify to a robust Data Warehouse such as Google BigQuery so that you can combine this information with data from many other sources and gain deep insights. Well, you have landed on the right blog. This blog will discuss 2 step-by-step methods for moving data from Shopify to BigQuery for analytics. First, it will provide a brief introduction to Shopify and
The Best Data Pipeline Tools List for 2024
Businesses today generate massive amounts of data. This data is scattered across different systems used by the business: Cloud Applications, databases, SDKs, etc. To gain valuable insight from this data, deep analysis is required. As a first step, companies would want to move this data to a single location for easy access and seamless analysis. This article introduces you to Data Pipeline Tools and the factors that drive a Data Pipeline Tools Decision. It also provides the difference between Batch vs. Real-Time Data Pipeline, Open Source vs. Proprietary Data Pipeline, and On-premise vs. Cloud-native Data Pipeline Tools.
Before we dive into the details, here is a snapshot of what this post covers:
What is a Data Pipeline Tool?
Dealing with data can be tricky. To be able to get real insights from data, you would need to perform ETL:
Extract data from multiple data sources that matter to you.
Transform and enrich this data to make it analysis-ready.
Load this data to a single source of truth more often a Data Lake or Data Warehouse.
Each of these steps can be done manually. Alternatively, each of these steps can be automated using separate software tools too.
However, during the process, many things can break. The code can throw errors, data can go missing, incorrect/inconsistent data can be loaded, and so on. The bottlenecks and blockers are limitless.
Often, a Data Pipeline tool is used to automate this process end-to-end efficiently, reliably, and securely. Data Pipeline software has many advantages, including the guarantee of a consistent and effortless migration from various data sources to a destination, often a Data Lake or Data Warehouse.
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Types of Data Pipeline Tools
Depending on the purpose, different types of Data Pipeline tools are available. The popular types are as follows:
Batch vs Real-time Data Pipeline Tools
Open source vs Proprietary Data Pipeline Tools
On-premise vs Cloud-native Data Pipeline Tools
1) Batch vs. Real-time Data Pipeline Tools
Batch Data Pipeline tools allow you to move data, usually a very large volume, at a regular interval or batches. This comes at the expense of real-time operation. More often than not, these type of tools is used for on-premise data sources or in cases where real-time processing can constrain regular business operation due to limited resources. Some of the famous Batch Data Pipeline tools are as follows:
Informatica PowerCenter
IBM InfoSphere DataStage
Talend
Pentaho
The real-time ETL tools are optimized to process data in real-time. Hence, these are perfect if you are looking to have analysis ready at your fingertips day in-day out. These tools also work well if you are looking to extract data from a streaming source, e.g. the data from user interactions that happen on your website/mobile application. Some of the famous real-time data pipeline tools are as follows:
LIKE.TG Data
Confluent
Estuary Flow
StreamSets
2) Open Source vs. Proprietary Data Pipeline Tools
Open Source means the underlying technology of the tool is publicly available and therefore needs customization for every use case. This type of Data Pipeline tool is free or charges a very nominal price. This also means you would need the required expertise to develop and extend its functionality as needed. Some of the known Open Source Data Pipeline tools are:
Talend
Apache Kafka
Apache Airflow
The Proprietary Data Pipeline tools are tailored as per specific business use, therefore require no customization and expertise for maintenance on the user’s part. They mostly work out of the box. Here are some of the best Proprietary Data Pipeline tools that you should explore:
LIKE.TG Data
Blendo
Fly Data
3) On-premises vs. Cloud-native Data Pipeline Tools
Previously, businesses had all their data stored in On-premise systems. Hence, a Data Lake or Data Warehouse also had to be set up On-premise. These Data Pipeline tools clearly offer better security as they are deployed on the customer’s local infrastructure. Some of the platforms that support On-premise Data Pipelines are:
Informatica Powercenter
Talend
Oracle Data Integrator
Cloud-native Data Pipeline tools allow the transfer and processing of Cloud-based data to Data Warehouses hosted in the cloud. Here the vendor hosts the Data Pipeline allowing the customer to save resources on infrastructure. Cloud-based service providers put a heavy focus on security as well. The platforms that support Cloud Data Pipelines are as follows:
LIKE.TG Data
Blendo
Confluent
The choice of a Data Pipeline that would suit you is based on many factors unique to your business. Let us look at some criteria that might help you further narrow down your choice of Data Pipeline Tool.
Factors that Drive Data Pipeline Tool Decision
With so many Data Pipeline tools available in the market, one should consider a couple of factors while selecting the best-suited one as per the need.
Easy Data Replication: The tool you choose should allow you to intuitively build a pipeline and set up your infrastructure in minimal time.
Maintenance Overhead: The tool should have minimal overhead and work out of the box.
Data Sources Supported: It should allow you to connect to numerous and various data sources. You should also consider support for those sources you may need in the future.
Data Reliability: It should transfer and load data without error or dropped packet.
Realtime Data Availability: Depending on your use case, decide if you need data in real-time or in batches will be just fine.
Customer Support: Any issue while using the tool should be solved quickly and for that choose the one offering the most responsive and knowledgeable customer sources
Scalability: Check whether the data pipeline tool can handle your current and future data volume needs.
Security: Access if the tool you are choosing can provide encryption and other necessary regulations for data protection.
Documentation: Look out if the tool has proper documentation or community to help when any need for troubleshooting arises.
Cost: Check the costs of license and maintenance of the data pipeline tool that you are choosing, along with its features to ensure that it is cost-effective for you.
Here is a list of use cases for the different Data Pipeline Tools mentioned in this article:
LIKE.TG , No-code Data Pipeline Solution
LIKE.TG is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines from 150+ sources that are flexible to your needs.
For the rare times things do go wrong, LIKE.TG ensures zero data loss. To find the root cause of an issue, LIKE.TG also lets you monitor your workflow so that you can address the issue before it derails the entire workflow. Add 24*7 customer support to the list, and you get a reliable tool that puts you at the wheel with greater visibility. Check LIKE.TG ’s in-depth documentation to learn more.
LIKE.TG offers a simple, and transparent pricing model. LIKE.TG has 3 usage-based pricing plans starting with a free tier, where you can ingest upto 1 million records.
What makes LIKE.TG amazing:
Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer.
Schema Management: LIKE.TG can automatically detect the schema of the incoming data and maps it to the destination schema.
Incremental Data Load: LIKE.TG allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
LIKE.TG was the most mature Extract and Load solution available, along with Fivetran and Stitch but it had better customer service and attractive pricing. Switching to a Modern Data Stack with LIKE.TG as our go-to pipeline solution has allowed us to boost team collaboration and improve data reliability, and with that, the trust of our stakeholders on the data we serve.
– Juan Ramos, Analytics Engineer, Ebury
Check out how LIKE.TG empowered Ebury to build reliable data products here.
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Business Challenges That Data Pipelines Mitigates:
Data Pipelines face the following business challenges and overcome them while serving your organization:
Operational Efficiency
It is difficult to orchestrate and manage complex data workflows. You can improve the operational efficiency of your workflow using data pipelines through automated workflow orchestration tools.
Real-time Decision-Making
Sometimes there is a delay in decision-making because of traditional batch processing. Data pipelines enable real-time data processing and speed up an organization’s decision-making.
Scalability
Traditional systems cannot handle large volumes of data, which can strain their performance. Data pipelines that are cloud-based provide scalable infrastructure and optimized performance.
Data Integration
The organizations usually have data scattered across various sources, which poses challenges. Data pipelines, through the ETL process, can ensure the consolidation of data in a central repository.
Conclusion
The article introduced you to Data Pipeline Tools and the factors that drive Data Pipeline Tools decisions.
It also provided the difference between Batch vs. Real-Time Data Pipeline, Open Source vs. Proprietary Data Pipeline, and On-premise vs. Cloud-native Data Pipeline Tools.
Now you can also read about LIKE.TG ’s Inflight Transformation feature and know how it improves your ELT data pipeline productivity. A Data Pipeline is the mechanism by which ETL processes occur. Now you can learn more about the best ETL tools that simplify the ETL process.
Visit our Website to Explore LIKE.TG
Want to take LIKE.TG for a spin? Sign Up for a 14-day free trial and experience the feature-rich LIKE.TG suite first hand.
Share your experience of finding the Best Data Pipeline Tools in the comments section below!
Shopify to Redshift: 2 Easy Methods
Software As A Service offerings like Shopify has revolutionized the way businesses step up their Sales channels. Shopify provides a complete set of tools to aid in setting up an e-commerce platform in a matter of a few clicks. Shopify comes bundles with all the configurations to support a variety of payment gateways and customizable online shop views. Bundles with this package are also the ability to run analysis and aggregation over the customer data collected through Shopify images. Even with all these built-in Shopify capabilities, organizations sometimes need to import the data from Shopify to their Data Warehouse since that allows them to derive meaningful insights by combining the Shopify data with their organization data. Doing this also means they get to use the full power of a Data Warehouse rather than being limited to the built-in functionalities of Shopify Analytics. This post is about the methods in which data can be loaded from Shopify to Redshift, one of the most popular cloud-based data warehouse.
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Shopify to Redshift: Approaches to Move Data
This blog covers two methods for migrating data from Shopify to Redshift:
Method 1: Using Shopify APIs to connect Shopify to Redshift
Making use of Shopify APIs to connect with Redshift is one such way. Shopify provides multiple APIs such as Billing, Customer, Inventory, etc., and can be accessed through its RESTful endpoints. This method makes use of custom code to connect with Shopify APIs and uses it to connect Shopify to Redshift.
Method 2: Using LIKE.TG Data, a No-code Data Pipeline to Connect Shopify to Redshift
Get started with LIKE.TG for free
A fully managed,No-code Data Pipeline platformlikeLIKE.TG Data, helps you load data from Shopify (among 40+ Free Sources) to Redshift in real-time, in an effortless manner. LIKE.TG with its minimal learning curve can be set up in a matter of minutes making the users ready to load data without compromising performance. Its strong integration with various sources such as Databases, Files, Analytics Engine, etc gives users the flexibility to bring in data of all different kinds in a way that’s as smooth as possible, without having to write a single line of code. It helps transfer data fromShopifyto a destination of your choice forfree.
Get started with LIKE.TG !
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Methods to connect Shopify to Redshift
There are multiple methods that can be used to connect Shopify to Redshift and load data easily:
Method 1: Using Shopify APIs to connect Shopify to RedshiftMethod 2: Using LIKE.TG Data, a No-code Data Pipeline to Connect Shopify to Redshift
Method 1: Using Shopify APIs to connect Shopify to Redshift
Since Redshift supports loading data to tables using CSV, the most straightforward way to accomplish this move is to use the CSV export feature of Shopify Admin. But this is not always practical since this is a manual process and is not suitable for the kind of frequent sync that typical organizations need. We will focus on the basics of accomplishing this in a programmatic way which is much better suited for typical requirements.
Shopify provides a number of APIs to access the Product, Customer, and Sales data. For this exercise, we will use the Shopify Private App feature. A Private App is an app built to access only the data of a specific Shopify Store. To create a Private App script, we first need to create a username and password in the Shopify Admin. Once you have generated the credentials, you can proceed to access the APIs. We will use the product API for reference in this post.
Use the below snippet of code to retrieve the details of all the products in the specified Shopify store.
curl --user shopify_app_user:shopify_app_password GET /admin/api/2019-10/products.json?limit=100
The important parameter here is the Limit parameter. This field is there because the API is paginated and it defaults to 50 results in case the Limit parameter is not provided. The maximum pagination limit is 250 results per second.
To access the full data, Developers need to buffer the id of the last item in the previous request and use that to form the next curl request. The next curl request would look like as below.
curl --user shopify_app_user:shopify_app_password GET /admin/api/2019-10/products.json? limit=100since_id=632910392 -o products.json
You will need a loop to execute this. From the above steps, you will have a set of JSON files that should be imported to Redshift to complete our objective. Fortunately, Redshift provides a COPY command which works well with JSON data. Let’s create a Redshift table before we export the data.
create table products( product_id varchar(25) NOT NULL, type varchar(25) NOT NULL, vendor varchar(25) NOT NULL, handle varchar(25) NOT NULL, published_scope varchar(25) NOT NULL )
Once the table is created, we can use the COPY command to load the data. Before copying ensure that the JSON files are loaded into an S3 bucket since we will be using S3 as the source for COPY command. Assuming data is already in S3, let’s proceed to the actual COPY command. The challenge here is that the Shopify API result JSON is a very complex nested JSON that has a large number of details. To map the appropriate keys to Redshift values, we will need a json_path file that Redshift uses to map fields in JSON to the Redshift table. The command will look as below.
copy products from ‘s3://products_bucket/products.json’ iam_role ‘arn:aws:iam:0123456789012:role/MyRedshiftRole' json ‘s3://products_bucket/products_json_path.json’ The json_path file for the above command will be as below. { "jsonpaths": [ "$['id']", "$['product_type']", "$[‘vendor’]", "$[‘handle’]", "$[‘published_scope’]" ] }
This is how you can connect Shopify to Redshift. Please note that this was a simple example and oversimplifies many of the actual pitfalls in the COPY process from Shopify to Redshift.
Limitations of migrating data using Shopify APIs
The Developer needs to implement a logic to accommodate the pagination that is part of the API results.Shopify APIs are rate limited. The requests are throttled based on a Leaky Bucket algorithm with a bucket size of 40 and 2 requests per second leak in case of admin APIs. So your custom script will need a logic to handle this limit in case your data volume is high.In case you need to Clean, Transform, Filter data before loading it to the Warehouse, you will need to build additional code to achieve this.The above approach works for a one-off load but if frequent sync which also handles duplicates is needed, additional logic needs to be developed using a Redshift Staging Table.In case you want to copy details that are inside the nested JSON structure or arrays in Shopify format, the json_path file development will take some development time.
Method 2: Using LIKE.TG Data, a No-code Data Pipeline to Connect Shopify to Redshift
LIKE.TG Data,a No-code Data Pipeline can help you move data from 100+ Data Sources including Shopify (among 40+ Free sources) swiftly to Redshift. LIKE.TG is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss. It helps transfer data fromShopifyto a destination of your choice forfree.
Steps to use LIKE.TG Data:
LIKE.TG Data focuses on two simple steps to get you started:
Configure Source:Connect LIKE.TG Data with Shopify by simply providing the API key and Pipeline name.
IntegrateData:Load data from Shopify to Redshift by simply providing your Redshift database credentials. Enter a name for your database, the host and port number for your Redshift database and connect in a matter of minutes.
Advantages of using LIKE.TG Data Platform:
Real-Time Data Export:LIKE.TG with its strong integration with 100+ sources, allows you to transfer data quickly efficiently. This ensures efficient utilization of bandwidth on both ends.Live Support:The LIKE.TG team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.Schema Management:LIKE.TG takes away the tedious task of schema management automatically detects schema of incoming data and maps it to the destination schema.Minimal Learning:LIKE.TG with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.Secure: LIKE.TG has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.Live Monitoring: LIKE.TG allows you to monitor the data flow so you can check where your data is at a particular point in time.
About Shopify
Shopify is a powerful e-commerce platform designed to allow people or businesses to sell their offerings/products online. Shopify helps you set up an online store and also offers a Point Of Sale (POS) to sell the products in person. Shopify provides you with Payment Gateways, Customer Engagement techniques, Marketing, and even Shipping facilities to help you get started.
Various product or services that you can sell on the Shopify:
Physical Products:Shopify allows you to perform the door-step delivery of the products you’ve manufactured that can be door-shipped to the customer. These include anything like Printed Mugs/T-Shirt, Jewellery, Gifts, etc.Digital Products:Digital Products can include E-Books, Audios, Course Material, etc.Services and Consultation:If you’re providing services like Life Consultation, Home-Cooked delicacies, Event Planning, or anything else, Shopify has got you covered.Memberships:Various memberships such as Gym memberships, Yoga-classes membership, Event Membership, etc. can be sold to the customers.Experiences:Event-based experiences like Adventurous Sports and Travel, Mountain Trekking, Wine Tasting, events, and hands-on workshops. You can use Shopify to sell tickets for these experiences as well.Rentals:If you’re running rental services like Apartment rentals, rental Taxis, or Gadgets, you can use Shopify to create Ads and engage with the customer.Classes:Online studies, Fitness classes can be advertised here.
Shopify allows you to analyze Trends and Customer Interaction on their platform. However, for advanced Analytics, you may need to store the data into some Database or Data Warehouse to perform in-depth Analytics and then move towards a Visualization tool to create appealing reports that can demonstrate these Trends and Market positioning.
For further information on Shopify, you can check theofficial site here.
About Redshift
Redshiftis a columnar Data Warehouse managed by Amazon Web Services (AWS). It is designed to run complex Analytical problems in a cost-efficient manner. It can store petabyte-scale data and enable fast analysis. Redshift’s completely managed warehouse setup, combined with its powerful MPP (Massively Parallel Processing) have made it one of the most famous Cloud Data Warehouse options among modern businesses.You can read more about the features of Redshift here.
Conclusion
In this blog, you were introduced to the key features of Shopify and Amazon Redshift. You learned about two methods to connect Shopify to Redshift. The first method is connecting using Shopify API. However, you explored some of the limitations of this manual method. Hence, an easier alternative, LIKE.TG Data was introduced to you to overcome the challenges faced by previous methods. You can seamlessly connect Shopify to Redshift with LIKE.TG for free.
visit our website to explore LIKE.TG
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sign up for a 14-day free trialand experience the feature-rich LIKE.TG suite first hand. Have a look at our unbeatablepricing, which will help you choose the right plan for you.
What are your thoughts on moving data from Shopify to Redshift? Let us know in the comments.
Data Automation: Conceptualizing Industry-driven Use Cases
As the data automation industry goes under a series of transformations, thanks to new strategic autonomous tools at our disposal, we now see a shift in how enterprises operate, cultivate, and sell value-driven services. At the same time, product-led growth paves the way for a productivity-driven startup ecosystem for better outcomes for every stakeholder.So, as one would explain, data automation is an autonomous process to collect, transfigure, or store data. Data automation technologies are in the use to execute time-consuming tasks that are recurring and replaceable to increase efficiency and minimize cost.
Innovative use of data automation can enable enterprises to provide a superior user experience, inspired by custom and innovative use to cater to pressure points in the customer lifecycle. To cut a long story short, data automation can brush up user experience and drive better outcomes.
In this article, we will talk about how data automation and its productivity-led use cases are transforming industries worldwide. We will discuss how data automation improves user experience and at the same time drive better business outcomes.
Why Data Automation?
Data automation has been transforming the way work gets done. Automation has helped companies empower teams by increasing productivity and nudging data transfer passivity. By automating bureaucratic activities from enterprises across vertices, we increase productivity, revenue, and customer satisfaction — quicker than before. Today, data automation has gained enough momentum that you just simply can’t execute without it.
As one would expect, data automation has come with its own unique sets of challenges. But it’s the skill lag and race to save cost that contradicts and creates major discussion in the data industry today. Some market insights are as follows:
A 2017 McKinsey report says, “half of today’s work activities could be automated by the end of 2055” — Cost reduction is prioritized.
A 2017 Unit4 study revealed, “office workers spent 69 days in a year on administrative tasks, costing companies $5 trillion a year” — a justification to automate.
And another research done by McKinsey estimated its outcome by surveying 1500 executives across industries and regions, out of which 66% of respondents believed that “addressing potential skills gaps related to automation/digitization was a top-ten priority” — data literacy is crucial in a data-driven environment.
What is Data Warehouse Automation?
A data warehouse is a single source of data truth, it works as a centralized repository for data generated from multiple sources. Each set of data has its unique use cases. The stored data helps companies generate business insights that are data predictive to help mitigate early signs of market nudges.
Using Data Warehouse Automation (DWA) we automate data flow, from third-party sources to the data warehouses such as Redshift, Snowflake, and BigQuery. But shifting trends tell us another story — a shift in reverse. We have seen an increased demand for data-enriching applications like LIKE.TG Activate — to transfer the data from data warehouses to CRMs like Salesforce and HubSpot.
Nevertheless, an agile data warehouse automation solution with a unique design, quick deployment settings, and no-code stock experience will lead its way. Let’s list out some of the benefits:
Data Warehouse Automation solutions provide real-time, source to destination, ingestion, and update services.
Automated and continuous refinements facilitate better business outcomes by simplifying data warehouse projects.
Automated ETL processes eliminate any reoccurring steps through auto-mapping and job scheduling.
Easy-to-use user interfaces and no-code platforms are enhancing user experience.
Empower Success Teams With Customer-data Analytics Using LIKE.TG Activate
LIKE.TG Activate helps you unify directly transfer data from data warehouses and other SaaS Product Analytics platforms like Amplitude, to CRMs such as Salesforce HubSpot, in a hassle-free automated manner.
LIKE.TG Activate manages automates the process of not only loading data from your desired source but also enrich transform data into an analysis-ready format — without having to write a single line of code. LIKE.TG Activate takes care of pre-processing data needs and allows you to focus on key business activities, to draw compelling insights into your product’s performance, customer journey, high-quality leads, and customer retention through a personalized experience.
Check out what makes LIKE.TG Activate amazing.
Real-Time Data Transfer: LIKE.TG Activate, with its strong integration with 100+ sources, allows you to transfer data quickly efficiently. This ensures efficient utilization of bandwidth on both ends.Secure: LIKE.TG Activate has a fault-tolerant architecture that ensures data is handled safely and cautiously with zero data loss.Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer.Tremendous Connector Availability: LIKE.TG Activate houses a diverse set of connectors that authorize you to bring data in from multiple data sources such as Google Analytics, Amplitude, Jira, and Oracle. And even data-warehouses such as Redshift and Snowflake are in an integrated and analysis-ready format.
Live Support:The LIKE.TG Activate team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
Get Customer-Centric with LIKE.TG Activate today!Sign up herefor exclusive early access into Activate!
Customer Centricity Benefiting From Data Automation
Today’s enterprises prefer tools that help customer-facing staff achieve greater success. Assisting customers on every twist and turn with unique use cases and touchpoints is now the name of the game. In return, the user touchpoint data is analyzed, to better engage customer-facing staff.
Data automation makes customer data actionable. As data is available for the teams to explore, now companies can offer users competent customer service, inspired by unique personalized experiences.
A train of thought: Focusing on everyday data requests from sales, customer success, and support teams, we can ensure success and start building a sophisticated CRM-centric data automation technology. Enriching the CRM software with simple data requests from teams mentioned above, can, in fact, make all the difference.
Customer and Data Analytics Enabling Competitive Advantage
Here, data automation has a special role to play. The art and science of data analytics are entangled with high-quality data collection and transformation abilities. Moving lightyears ahead from survey-based predictive analytics procedures, we now have entered a transition period, towards data-driven predictive insights and analytics.
Thanks to better analytics, we can better predict user behavior, build cross-functional teams, minimize user churn rate, and focus first on the use cases that drive quick value.
Four Use Cases Disrupting Legacy Operations Today
1. X-Analytics
We can’t limit today’s autonomous tools to their primitive use cases as modern organizations generate data that is both unstructured and structured. Setting the COVID-19 pandemic an example of X-Analytics’s early use case: X-Analytics helped medical and public health experts by analyzing terabytes of data in the form of videos, research papers, social media posts, and clinical trials data.
2. Decision Intelligence
Decision intelligence helps companies gain quick, actionable insights using customer/product data. Decision intelligence can amplify user experience and improve operations within the companies.
3. Blockchain in Data Analytics
Smart contracts, with the normalization of blockchain technology, have evolved. Smart contracts increase transparency, data quality, and productivity. For instance, a process in a smart contract is initiated only when certain predetermined conditions are met. The process is designed to remove any bottlenecks that might come in between while officializing an agreement.
4. Augmented Data Management:
As the global service industry inclines towards outsourcing the data storage and management needs, getting insights will become more complicated and time-consuming. Using AI and ML to automate lackluster tasks can reduce manual data management tasks by 45%.
Data Automation is Changing the Way Work Gets Done
Changing user behavior and customer buying trends are altering market realities today. At the same time, the democratization of data within organizations has enabled customer-facing staff to generate better results. Now, teams are encouraged, by design, to take advantage of data, to make compelling, data-driven decisions.
Today, high-quality data is an integral part of a robust sales and marketing flywheel. Hence, keeping an eye on the future, treating relationships like partnerships and not just one-time transactional tedium, generates better results.
Conclusion
Alas, the time has come to say goodbye to our indulgence in recurring data transfer customs, as we embrace change happening in front of our eyes. Today, data automation has cocooned out of its early use cases and has aww-wittingly blossomed to benefit roles that are, in practice, the first touchpoint in any customers’ life cycle. And what about a startup’s journey to fully calibrate the product’s offering — how can we forget!?
Today’s data industry has fallen sick of unstructured data silos, and wants an unhindered flow of analytics-ready data to facilitate business decisions– small or big, doesn’t matter. Now, with LIKE.TG Activate, directly transfer data from data warehouses such as Snowflake or any other SaaS application to CRMs like HubSpot, Salesforce, and others, in a fully secure and automated manner.
LIKE.TG Activate has taken advantage of its robust analytics engine that powers a seamless flow of analysis-ready customer and product data. But, integrating this complex data from a diverse set of customers product analytics platforms is challenging; hence LIKE.TG Activate comes into the picture. LIKE.TG Activate has strong integration with other data sources that allows you to extract data make it analysis-ready. Now, become customer-centric and data-driven like never before!
Give LIKE.TG Activate a try bysigning up for a 14-day free trial today.
Connecting DynamoDB to Redshift – 2 Easy Methods
DynamoDB is Amazon’s document-oriented, high-performance, NoSQL Database. Given it is a NoSQL Database, it is hard to run SQL queries to analyze the data. It is essential to move data from DynamoDB to Redshift, convert it into a relational format for seamless analysis.This article will give you a comprehensive guide to set up DynamoDB to Redshift Integration. It will also provide you with a brief introduction to DynamoDB and Redshift. You will also explore 2 methods to Integrate DynamoDB and Redshift in the further sections. Let’s get started.
Prerequisites
You will have a much easier time understanding the ways for setting up DynamoDB to Redshift Integration if you have gone through the following aspects:
An active AWS (Amazon Web Service) account.Working knowledge of Database and Data Warehouse.A clear idea regarding the type of data is to be transferred.Working knowledge of Amazon DynamoDB and Amazon Redshift would be an added advantage.
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Introduction to Amazon DynamoDB
Fully managed by Amazon, DynamoDB is a NoSQL database service that provides high-speed and highly scalable performance. DynamoDB can handle around 20 million requests per second. Its serverless architecture and on-demand scalability make it a solution that is widely preferred.
To know more about Amazon DynamoDB, visit this link.
Introduction to Amazon Redshift
A widely used Data Warehouse, Amazon Redshift is an enterprise-class RDBMS. Amazon Redshift provides a high-performance MPP, columnar storage set up, highly efficient targeted data compression encoding schemes, making it a natural choice for Data Warehousing and analytical needs.
Amazon Redshift has excellent business intelligence abilities and a robust SQL-based interface. Amazon Redshift allows you to perform complex data analysis queries, complex joins with other tables in your AWS Redshift cluster and queries can be used in any reporting application to create dashboards or reports.
To know more about Amazon Redshift, visit this link.
Methods to Set up DynamoDb to Redshift Integration
This article delves into both the manual and using LIKE.TG methods in depth. You will also see some of the pros and cons of these approaches and would be able to pick the best method based on your use case.Below are the two methods:
Method 1: Using Copy Utility to Manually Set up DynamoDB to Redshift IntegrationMethod 2: Using LIKE.TG Data to Set up DynamoDB to Redshift Integration
Method 1: Using Copy Utility to Manually Set up DynamoDB to Redshift Integration
As a prerequisite, you must have a table created in Amazon Redshift before loading data from the DynamoDB table to Redshift. As we are copying data from NoSQL DB to RDBMS, we need to apply some changes/transformations before loading it to the target database. For example, some of the DynamoDB data types do not correspond directly to those of Amazon Redshift. While loading, one should ensure that each column in the Redshift table is mapped to the correct data type and size. Below is the step-by-step procedure to set up DynamoDB to Redshift Integration.
Step 1: Before you migrate data from DynamoDB to Redshift create a table in Redshift using the following command as shown by the image below.
Step 2: Create a table in DynamoDB by logging into the AWS console as shown below.
Step 3: Add data into DynamoDB Table by clicking on Create Item.
Step 4: Use the COPY command to copy data from DynamoDB to Redshift in the Employee table as shown below.
copy emp.emp from 'dynamodb://Employee' iam_role 'IAM_Role' readratio 10;
Step 5: Verify that data got copied successfully.
Limitations of using Copy Utility to Manually Set up DynamoDB to Redshift Integration
There are a handful of limitations while performing ETL from DynamoDB to Redshift using the Copy utility. Read the following:
DynamoDB table names can contain up to 255 characters, including ‘.’ (dot) and ‘-‘ (dash) characters, and are case-sensitive. However, Amazon Redshift table names are limited to 127 characters, cannot include dots or dashes, and are not case-sensitive. Also, we cannot use Amazon Redshift reserved words. Unlike SQL Databases, DynamoDB does not support NULL. Interpretation of empty or blank attribute values in DynamoDB should be specified to Redshift. In Redshift, these can be treated as either NULLs or empty fields.Following data parameters are not supported alongwith COPY from DynamoDB:FILLRECORDESCAPEIGNOREBLANKLINESIGNOREHEADERNULLREMOVEQUOTESACCEPTINVCHARSMANIFESTENCRYPT
However, apart from the above-mentioned limitations, the COPY command leverages Redshift’s massively parallel processing(MPP) architecture to read and stream data in parallel from an Amazon DynamoDB table. By leveraging Redshiftdistribution keys, you can make the best out of Redshift’s parallel processing architecture.
Method 2: Using LIKE.TG Data to Set up DynamoDB to Redshift Integration
LIKE.TG Data, a No-code Data Pipeline, helps you directly transfer data from Amazon DynamoDB and100+ other data sourcesto Data Warehouses such as Amazon Redshift, Databases, BI tools, or a destination of your choice in a completely hassle-free automated manner. LIKE.TG is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.
LIKE.TG Data takes care of all your data preprocessing needs and lets you focus on key business activities and draw a much powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent reliable solution to manage data in real-time and always have analysis-ready data in your desired destination.
Loading data into Amazon Redshift using LIKE.TG is easier, reliable, and fast. LIKE.TG is a no-code automated data pipeline platform that solves all the challenges described above. You move data from DynamoDB to Redshift in the following two steps without writing any piece of code.
Authenticate Data Source: Authenticate and connect your Amazon DynamoDB account as a Data Source.
To get more details about Authenticating Amazon DynamoDB with LIKE.TG Data visit here.
Configure your Destination: Configure your Amazon Redshift account as the destination.
To get more details about Configuring Redshift with LIKE.TG Data visit thislink.
You now have a real-time pipeline for syncing data from DynamoDB to Redshift.
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Here are more reasons to try LIKE.TG :
Secure: LIKE.TG has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.Schema Management: LIKE.TG takes away the tedious task of schema management automatically detects the schema of incoming data and maps it to the destination schema.Minimal Learning: LIKE.TG , with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.LIKE.TG Is Built To Scale: As the number of sources and the volume of your data grows, LIKE.TG scales horizontally, handling millions of records per minute with very little latency.Incremental Data Load: LIKE.TG allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.Live Support: The LIKE.TG team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.Live Monitoring: LIKE.TG allows you to monitor the data flow and check where your data is at a particular point in time.
Methods to Set up DynamoDB to Redshift Integration
Method 1: Using Copy Utility to Manually Set up DynamoDB to Redshift Integration
This method involves the use of COPY utility to set up DynamoDB to Redshift Integration. This process of writing custom code to perform DynamoDB to Redshift replication is tedious and needs a whole bunch of precious engineering resources invested in this. As your data grows, the complexities will grow too, making it necessary to invest resources on an ongoing basis for monitoring and maintenance.
Method 2: Using LIKE.TG Data to Set up DynamoDB to Redshift Integration
LIKE.TG Data is an automated Data Pipeline platform that can move your data from Optimizely to MySQL very quickly without writing a single line of code. It is simple, hassle-free, and reliable.
Moreover, LIKE.TG offers a fully-managed solution to set up data integration from100+ data sources(including 30+ free data sources)and will let you directly load data to a Data Warehouse such as Snowflake, Amazon Redshift, Google BigQuery, etc. or the destination of your choice. It will automate your data flow in minutes without writing any line of code. Its Fault-Tolerant architecture makes sure that your data is secure and consistent. LIKE.TG provides you with a truly efficient and fully automated solution to manage data in real-time and always have analysis-ready data.
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Conclusion
The process of writing custom code to perform DynamoDB to Redshift replication is tedious and needs a whole bunch of precious engineering resources invested in this. As your data grows, the complexities will grow too, making it necessary to invest resources on an ongoing basis for monitoring and maintenance. LIKE.TG handles all the aforementioned limitations automatically, thereby drastically reducing the effort that you and your team will have to put in.
Visit our Website to Explore LIKE.TG
Businesses can use automated platforms like LIKE.TG Data to set this integration and handle the ETL process. It helps you directly transfer data from a source of your choice to a Data Warehouse, Business Intelligence tools, or any other desired destination in a fully automated and secure manner without having to write any code and will provide you a hassle-free experience.
Want to take LIKE.TG for a spin? Sign Up for a 14-day free trial and experience the feature-rich LIKE.TG suite first hand. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.
Share your experience of setting up DynamoDB to Redshift Integration in the comments section below!
Google Ads to Redshift Simplified: 2 Easy Methods
Your business uses Google Ads heavily to acquire more customers and build your brand. Given the importance of this data, moving data from Google Ads to a robust Data Warehouse Redshift for advanced analytics is a step in the right direction. Google Ads is an Advertising Platform from Google that provides you the tools for launching Ad Campaigns, Product Listing, or Videos to your users. On the other hand, Amazon Redshift is a Cloud-based Data Warehousing solution from Amazon Web Services (AWS).This blog will introduce you to Google Ads and Amazon Redshift. It will also discuss 2 approaches so that you can weigh your options and choose wisely while loading data from Google Ads to Redshift. The 1st method is completely manual and demands technical proficiency while the 2nd method uses LIKE.TG Data.
Introduction to Google Ads
Google Ads is an Online Advertising Platform that allows businesses to showcase highly personalized ads in various formats such as Text Ads, Video Ads, Image Ads. Advertising copy is placed on pages where Google Ads things are relevant. Businesses can choose to pay Google basis a flexible model (Pay Per Click or Pay for the advertisement shown).
Given the reach that Google has, this has become one of the most favorite advertising channels for modern Marketers.
For more information on Google Ads, click here.
Introduction to Amazon Redshift
AWS Redshift is a Data Warehouse managed by Amazon Web Services (AWS). It is built using MPP (massively parallel processing) architecture and has the capacity to store large sets of data and perform advanced analytics. Designed to run complex analytical workloads in a cost-efficient fashion, Amazon Redshift has emerged to be a popular Cloud Data Warehouse choice for modern data teams.
For more information on Amazon Redshift, click here.
Methods to Load Data from Google Ads to Redshift
Method 1: Load Data from Google Ads to Redshift by Building ETL ScriptsThis method would need a huge investment on the engineering side. A group of engineers would need to understand both Google Ads and Redshift ecosystems and hand code a custom solution to move data.Method 2: Load Data from Google Ads to Redshift using LIKE.TG DataLIKE.TG comes pre-built with integration for both Google Ads and Redshift. With a few simple clicks, a sturdy Data Replication setup can be created from Google Ads to Redshift for free. Since LIKE.TG is a managed platform, you would not need to invest in engineering resources. LIKE.TG will handle the groundwork while your analysts can work with Redshift to uncover insights.
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Methods to Load Data from Google Ads to Redshift
Majorly there are 2 methods through which you can load your data from Google Ads to Redshift:
Method 1: Load Data from Google Ads to Redshift by Building ETL ScriptsMethod 2: Load Data from Google Ads to Redshift using LIKE.TG Data
This section will discuss the above 2 approaches in detail. In the end, you will have a deep understanding of both and you will be able to make the right decision by weighing the pros and cons of each. Now, let’s walk through these methods one by one.
Method 1: Load Data from Google Ads to Redshift by Building ETL Scripts
This method includes Manual Integration between Google Ads and Redshift. It demands technical knowledge and experience in working with Google Ads and Redshift. Following are the steps to integrate and load data from Google Ads to Redshift:
Step 1: Extracting Data from Google AdsStep 2: Loading Google Ads Data to Redshift
Step 1: Extracting Data from Google Ads
Applications interact with the Google Ads platform using Google Ads API. The Google Ads API is implemented using SOAP (Simple Object Access Protocol) and doesn’t support RESTful implementation.
A number of different libraries are offered that could be used with many programming languages. The following languages and frameworks are officially supported.
PythonPHPJAVA.NETRubyPERL
Google Ads API is quite complex and exposes many functionalities to the user. One can pull out a number of reports using Google Ads API. The granularity of the results you would need can also be specified by passing specific parameters. You can decide the data you want to get in 2 ways.
By using an AWQL-based report definitionBy using XML-based report definition
Most Google Ads APIs are queried using AWQL which is similar to SQL. The following output formats are supported.
CSV – Comma separated values formatCSV FOR EXCEL – MS excel compatible formatTSV – Tab separated valueXML – Extensible markup language formatGZIPPED-CSV – Compressed csvGZIPPED-XML – Compressed xml
You can read more about Data Extraction from Google Ads here.
Once you have the necessary data extracted from Google Ads, the next step would be to load it into Redshift.
Step 2: Loading Google Ads Data to Redshift
As a prerequisite, you will need to create a Redshift table and map the schema from the extracted Google Ads data. When mapping the schema, you should be careful to map each attribute to the right data types supported by Redshift. Redshift supports the following data types:
INTSMALLINTBIGINTDECIMALVARCHARCHARDATETIMESTAMPREALDOUBLE PRECISIONBOOLEAN
Design a schema and map the data from the source. Follow the best practicespublished by Amazon when designing the Redshift database.
While Redshift allows us to directly insert data into its tables, this is not the most recommended approach. Avoid using the INSERT command as it loads the data row by row. This slows the process because Redshift is not optimized to load data in this way. Instead, load the data to Amazon S3 and use the copy command to load it to Redshift. This is very useful, especially when handling large volumes of data.
Limitations of Loading Data from Google Ads to Redshift Using Custom Code
Accessing Google Ads Data in Real-time: After successfully creating a program that loads data from Google ads to the Redshift warehouse, you will be required to deal with the challenge of loading new and updated data. You may decide to replicate the data in real-time each time a new row or updated data is created. This process is slower and resource-intensive. Therefore, you will be required to write additional code and build cron jobs to run this in a continuous loop.Infrastructure Maintenance: Google ads may update their APIs or something may break at Redshift’s end unexpectedly. In order to save your business from irretrievable data loss, you will be required to constantly maintain the code and monitor the health of the infrastructure. Ability to Transform: The above approach only allows you to move data from Google Ads to Redshift as is. In case you are looking to clean/transform the data before loading to the warehouse – say you want to convert currencies or standardize time zones in which ads were run, this would not be possible using the previous approach.
Method 2: Load Data from Google Ads to Redshift using LIKE.TG Data
LIKE.TG Data, a No-code Data Pipeline helps to Load Data from any data source such as Databases, SaaS applications, Cloud Storage, SDKs, and Streaming Services and simplifies the ETL process. It supports 100+ data sources(including 40+ free sources) including Google Ads, etc., for free and is a 3-step process by just selecting the data source, providing valid credentials, and choosing the destination. LIKE.TG loads the data onto the desired Data Warehouse, enriches the data, and transforms it into an analysis-ready form without writing a single line of code.
Its completely automated pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different Business Intelligence (BI) tools as well.
LIKE.TG can move data from Google Ads to Redshift seamlessly in 2 simple steps:
Step 1: Configuring the Source
Navigate to the Asset Palette and click on Pipelines.Now, click on the +CREATE button and select Google Ads as the source for data migration.In theConfigure your Google Adspage, click+ ADD GOOGLE ADS ACCOUNT which will redirect you to the Google Ads login page.Login to your Google Ads account and click on Allow to authorize LIKE.TG to access your Google Ads data.
In theConfigure your Google Ads Sourcepage, fill all the required fields
Step 2: Configuring the Destination
Once you have configured the source, it’s time to manage the destination. navigate to the Asset Palette and click on Destination.Click on the +CREATE button and select Amazon Redshift as the destination.In theConfigure your Amazon Redshift Destinationpage, specify all the necessary details.
LIKE.TG will now take care of all the heavy-weight lifting to move data from Google Ads to Redshift.
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Advantages of Using LIKE.TG
Listed below are the advantages of using LIKE.TG Data over any other Data Pipeline platform:
Secure: LIKE.TG has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.Schema Management: LIKE.TG takes away the tedious task of schema management automatically detects the schema of incoming data and maps it to the destination schema.Minimal Learning: LIKE.TG , with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.LIKE.TG Is Built To Scale: As the number of sources and the volume of your data grows, LIKE.TG scales horizontally, handling millions of records per minute with very little latency.Incremental Data Load: LIKE.TG allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.Live Support: The LIKE.TG team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.Live Monitoring: LIKE.TG allows you to monitor the data flow and check where your data is at a particular point in time.
Conclusion
The article introduced you to Google Ads and Amazon Redshift. It provided 2 methods that you can use for loading data from Google Ads to Redshift. The 1st method includes Manual Integration while the 2nd method uses LIKE.TG Data.
With the complexity involves in Manual Integration, businesses are leaning more towards Automated and Continous Integration. This is not only hassle-free but also easy to operate and does not require any technical proficiency. In such a case, LIKE.TG Data is the right choice for you! It will help simplify the Marketing Analysis. LIKE.TG Data supports platforms like Google Ads, etc., for free.
Visit our Website to Explore LIKE.TG
In order to do Advanced Data Analytics effectively, you will require to have reliable and updated Google Ads data.
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What are your thoughts on moving data from Google Ads to Redshift? Let us know in the comments.
MongoDB to Redshift Data Transfer: 2 Easy Methods
If you are looking to move data from MongoDB to Redshift, I reckon that you are trying to upgrade your analytics set up to a modern data stack. Great move!Kudos to you for taking up this mammoth of a task! In this blog, I have tried to share my two cents on how to make the data migration from MongoDB to Redshift easier for you.
Before we jump to the details, I feel it is important to understand a little bit on the nuances of how MongoDB and Redshift operate. This will ensure you understand the technical nuances that might be involved in MongoDB to Redshift ETL. In case you are already an expert at this, feel free to skim through these sections or skip them entirely.
What is MongoDB?
MongoDB distinguishes itself as a NoSQL database program. It uses JSON-like documents along with optional schemas. MongoDB is written in C++. MongoDB allows you to address a diverse set of data sets, accelerate development, and adapt quickly to change with key functionalities like horizontal scaling and automatic failover.
MondoDB is a best RDBMS when you have a huge data volume of structured and unstructured data. It’s features make scaling and flexibility smooth. These are available for data integration, load balancing, ad-hoc queries, sharding, indexing, etc.
Another advantage is that MongoDB also supports all common operating systems (Linux, macOS, and Windows). It also supports C, C++, Go, Node.js, Python, and PHP.
What is Amazon Redshift?
Amazon Redshift is essentially a storage system that allows companies to store petabytes of data across easily accessible “Clusters” that you can query in parallel. Every Amazon Redshift Data Warehouse is fully managed which means that the administrative tasks like maintenance backups, configuration, and security are completely automated.
Suppose, you are a data practitioner who wants to use Amazon Redshift to work with Big Data. It will make your work easily scalable due to its modular node design. It also us you to gain more granular insight into datasets, owing to the ability of Amazon Redshift Clusters to be further divided into slices. Amazon Redshift’s multi-layered architecture allows multiple queries to be processed simultaneously thus cutting down on waiting times. Apart from these, there are a few more benefits of Amazon Redshift you can unlock with the best practices in place.
Main Features of Amazon Redshift
When you submit a query, Redshift cross checks the result cache for a valid and cached copy of the query result. When it finds a match in the result cache, the query is not executed. On the other hand, it uses a cached result to reduce runtime of the query.
You can use the Massive Parallel Processing (MPP) feature for writing the most complicated queries when dealing with large volume of data.
Your data is stored in columnar format in Redshift tables. Therefore, the number of disk I/O requests to optimize analytical query performance is reduced.
Why perform MongoDB to Redshift ETL?
It is necessary to bring MongoDB’s data to a relational format data warehouse like AWS Redshift to perform analytical queries. It is simple and cost-effective to efficiently analyze all your data by using a real-time data pipeline. MongoDB is document-oriented and uses JSON-like documents to store data.
MongoDB doesn’t enforce schema restrictions while storing data, the application developers can quickly change the schema, add new fields and forget about older ones that are not used anymore without worrying about tedious schema migrations. Owing to the schema-less nature of a MongoDB collection, converting data into a relational format is a non-trivial problem for you.
In my experience in helping customers set up their modern data stack, I have seen MongoDB be a particularly tricky database to run analytics on. Hence, I have also suggested an easier / alternative approach that can help make your journey simpler.
In this blog, I will talk about the two different methods you can use to set up a connection from MongoDB to Redshift in a seamless fashion: Using Custom ETL Scripts and with the help of a third-party tool, LIKE.TG .
What Are the Methods to Move Data from MongoDB to Redshift?
These are the methods we can use to move data from MongoDB to Redshift in a seamless fashion:
Method 1: Using Custom Scripts to Move Data from MongoDB to Redshift
Method 2: Using an Automated Data Pipeline Platform to Move Data from MongoDB to Redshift
Integrate MongoDB to RedshiftGet a DemoTry it
Method 1: Using Custom Scripts to Move Data from MongoDB to Redshift
Following are the steps we can use to move data from MongoDB to Redshift using Custom Script:
Step 1: Use mongoexport to export data.
mongoexport --collection=collection_name --db=db_name --out=outputfile.csv
Step 2: Upload the .json file to the S3 bucket.2.1: Since MongoDB allows for varied schema, it might be challenging to comprehend a collection and produce an Amazon Redshift table that works with it. For this reason, before uploading the file to the S3 bucket, you need to create a table structure.2.2: Installing the AWS CLI will also allow you to upload files from your local computer to S3. File uploading to the S3 bucket is simple with the help of the AWS CLI. To upload.csv files to the S3 bucket, use the command below if you have previously installed the AWS CLI. You may use the command prompt to generate a table schema after transferring.csv files into the S3 bucket.
AWS S3 CP D:\outputfile.csv S3://S3bucket01/outputfile.csv
Step 3: Create a Table schema before loading the data into Redshift.
Step 4: Using the COPY command load the data from S3 to Redshift.Use the following COPY command to transfer files from the S3 bucket to Redshift if you’re following Step 2 (2.1).
COPY table_name
from 's3://S3bucket_name/table_name-csv.tbl'
'aws_iam_role=arn:aws:iam::<aws-account-id>:role/<role-name>'
csv;
Use the COPY command to transfer files from the S3 bucket to Redshift if you’re following Step 2 (2.2). Add csv to the end of your COPY command in order to load files in CSV format.
COPY db_name.table_name
FROM ‘S3://S3bucket_name/outputfile.csv’
'aws_iam_role=arn:aws:iam::<aws-account-id>:role/<role-name>'
csv;
We have successfully completed MongoDB Redshift integration.
For the scope of this article, we have highlighted the challenges faced while migrating data from MongoDB to Amazon Redshift. Towards the end of the article, a detailed list of advantages of using approach 2 is also given. You can check out Method 1 on our other blog and know the detailed steps to migrate MongoDB to Amazon Redshift.
Limitations of using Custom Scripts to Move Data from MongoDB to Redshift
Here is a list of limitations of using the manual method of moving data from MongoDB to Redshift:
Schema Detection Cannot be Done Upfront: Unlike a relational database, a MongoDB collection doesn’t have a predefined schema. Hence, it is impossible to look at a collection and create a compatible table in Redshift upfront.
Different Documents in a Single Collection: Different documents in single collection can have a different set of fields. A document in a collection in MongoDB can have a different set of fields.
{
"name": "John Doe",
"age": 32,
"gender": "Male"
}
{
"first_name": "John",
"last_name": "Doe",
"age": 32,
"gender": "Male"
}
Different documents in a single collection can have incompatible field data types. Hence, the schema of the collection cannot be determined by reading one or a few documents.
2 documents in a single MongoDB collection can have fields with values of different types.
{
"name": "John Doe",
"age": 32,
"gender": "Male"
"mobile": "(424) 226-6998"
}
{
"name": "John Doe",
"age": 32,
"gender": "Male",
"mobile": 4242266998
}
The fieldmobile is a string and a number in the above documents respectively. It is a completely valid state in MongoDB. In Redshift, however, both these values either will have to be converted to a string or a number before being persisted.
New Fields can be added to a Document at Any Point in Time: It is possible to add columns to a document in MongoDB by running a simple update to the document. In Redshift, however, the process is harder as you have to construct and run ALTER statements each time a new field is detected.
Character Lengths of String Columns: MongoDB doesn’t put a limit on the length of the string columns. It has a 16MB limit on the size of the entire document. However, in Redshift, it is a common practice to restrict string columns to a certain maximum length for better space utilization. Hence, each time you encounter a longer value than expected, you will have to resize the column.
Nested Objects and Arrays in a Document: A document can have nested objects and arrays with a dynamic structure. The most complex of MongoDB ETL problems is handling nested objects and arrays.
{
"name": "John Doe",
"age": 32,
"gender": "Male",
"address": {
"street": "1390 Market St",
"city": "San Francisco",
"state": "CA"
},
"groups": ["Sports", "Technology"]
}
MongoDB allows nesting objects and arrays to several levels. In a complex real-life scenario is may become a nightmare trying to flatten such documents into rows for a Redshift table.
Data Type Incompatibility between MongoDB and Redshift: Not all data types of MongoDB are compatible with Redshift. ObjectId, Regular Expression, Javascript are not supported by Redshift. While building an ETL solution to migrate data from MongoDB to Redshift from scratch, you will have to write custom code to handle these data types.
Method 2: Using Third Pary ETL Tools to Move Data from MongoDB to Redshift
White using the manual approach works well, but using an automated data pipeline tool like LIKE.TG can save you time, resources and costs. LIKE.TG Data is a No-code Data Pipeline platform that can help load data from any data source, such as databases, SaaS applications, cloud storage, SDKs, and streaming services to a destination of your choice. Here’s how LIKE.TG overcomes the challenges faced in the manual approach for MongoDB to Redshift ETL:
Dynamic expansion for Varchar Columns: LIKE.TG expands the existing varchar columns in Redshift dynamically as and when it encounters longer string values. This ensures that your Redshift space is used wisely without you breaking a sweat.
Splitting Nested Documents with Transformations: LIKE.TG lets you split the nested MongoDB documents into multiple rows in Redshift by writing simple Python transformations. This makes MongoDB file flattening a cakewalk for users.
Automatic Conversion to Redshift Data Types: LIKE.TG converts all MongoDB data types to the closest compatible data type in Redshift. This eliminates the need to write custom scripts to maintain each data type, in turn, making the migration of data from MongoDB to Redshift seamless.
Here are the steps involved in the process for you:
Step 1: Configure Your Source
Load Data from LIKE.TG to MongoDB by entering details like Database Port, Database Host, Database User, Database Password, Pipeline Name, Connection URI, and the connection settings.
Step 2: Intgerate Data
Load data from MongoDB to Redshift by providing your Redshift databases credentials like Database Port, Username, Password, Name, Schema, and Cluster Identifier along with the Destination Name.
LIKE.TG supports 150+ data sources including MongoDB and destinations like Redshift, Snowflake, BigQuery and much more. LIKE.TG ’s fault-tolerant and scalable architecture ensures that the data is handled in a secure, consistent manner with zero data loss.
Give LIKE.TG a try and you can seamlessly export MongoDB to Redshift in minutes.
GET STARTED WITH LIKE.TG FOR FREE
For detailed information on how you can use the LIKE.TG connectors for MongoDB to Redshift ETL, check out:
MongoDB Source Connector
Redshift Destination Connector
Additional Resources for MongoDB Integrations and Migrations
Stream data from mongoDB Atlas to BigQuery
Move Data from MongoDB to MySQL
Connect MongoDB to Snowflake
Connect MongoDB to Tableau
Conclusion
In this blog, I have talked about the 2 different methods you can use to set up a connection from MongoDB to Redshift in a seamless fashion: Using Custom ETL Scripts and with the help of a third-party tool, LIKE.TG .
Outside of the benefits offered by LIKE.TG , you can use LIKE.TG to migrate data from an array of different sources – databases, cloud applications, SDKs, and more. This will provide the flexibility to instantly replicate data from any source like MongoDB to Redshift.
More related reads:
Creating a table in Redshift
Redshift functions
You can additionally model your data, build complex aggregates and joins to create materialized views for faster query executions on Redshift. You can define the interdependencies between various models through a drag and drop interface with LIKE.TG ’s Workflows to convert MongoDB data to Redshift.
Aurora to Redshift Replication: 4 Easy Steps
AWS Data Pipeline is a data movement and data processing service provided by Amazon. Using Data Pipeline you can perform data movement and processing as per your requirement. Data pipeline also supports scheduling of Pipeline processing. You can also perform data movement residing on on-prem.Data Pipeline provides you various options to customize your resources, activities, scripts, failure handling, etc. In the Pipeline you just need to define the sequence of data sources, destinations along data processing activities depending on your business logic and the data pipeline will take care of data processing activities.
Similarly, you can perform Aurora to Redshift Replication using AWS Data Pipeline. This article introduces you to Aurora and Amazon Redshift. It also provides you the steps to perform Aurora to Redshift Replication using AWS Data Pipeline.
Method 1: Using an Automated Data Pipeline Platform
You can easily move your data from Aurora to Redshift using LIKE.TG ’s automated data pipeline platform.
Step 1: Configure Aurora as a Source
Step 2: Configure Redshift as a destination
LIKE.TG is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines that are flexible to your needs. With integration with 150+ Data Sources such as PostgreSQL, MySQL, and MS SQL Server, we help you not only export data from sources load data to the destinations but also transform enrich your data, make it analysis-ready.The unique combination of features differentiates LIKE.TG from its competitors, including Fivetran.
Method 2: Steps to Perform Aurora to Redshift Replication Using AWS Data Pipeline
This is a method that demands technical proficiency and experience in working with Aurora and Redshift. This is a Manual Integration using AWS Data Pipeline.
Follow the steps below to perform Aurora to Redshift Replication using AWS Data Pipeline:
Step 1: Select the Data from Aurora
Step 2: Create an AWS Data Pipeline to Perform Aurora to Redshift Replication
Step 3: Activate the Data Pipeline to Perform Aurora to Redshift Replication
Step 4: Check the Data in Redshift
Step 1: Select the Data from Aurora
Select the data that you want for Aurora to Redshift Replication as shown in the image below.
Step 2: Create an AWS Data Pipeline to Perform Aurora to Redshift Replication
For MySQL/Aurora MySQL to Redshift, AWS Data Pipeline provides an inbuilt template to build the Data Pipeline. You will reuse the template and provide the details as shown in the image below.
Note: Check all the pre and post conditions in the Data Pipeline before activating the Pipeline for performing Aurora to Redshift Replication.
Step 3: Activate the Data Pipeline to Perform Aurora to Redshift Replication
Data Pipeline internally generates the following activities automatically:
RDS to S3 Copy Activity (to stage data from Amazon Aurora)
Redshift Table Create Activity (create Redshift Table if not present)
Move data from S3 to Redshift
Perform the cleanup from S3 (Staging)
Step 4: Check the Data in Redshift
Pros of Performing Aurora to Redshift Replication Using AWS Data Pipeline
AWS Data Pipeline is quite flexible as it provides a lot of built-in options for data handling.
You can control the instance and cluster types while managing the Data Pipeline hence you have complete control.
Data pipeline has already provided inbuilt templates in AWS Console which can be reused for similar pipeline operations.
Depending upon your business logic, condition check and job logic are user-friendly.
While triggering the EMR cluster you can leverage other engines other than Apache Spark i.e. Pig, Hive, etc.
Cons of Performing Aurora to Redshift Replication Using AWS Data Pipeline
The biggest disadvantage with the approach is that it is not serverless and the pipeline internally triggers other instance/clusters which runs behind the scene. In case, they are not handled properly, it may not be cost-effective.
Another disadvantage with this approach is similar to the case of copying Aurora to Redshift usingGlue, data pipeline is available in limited regions. For the list of supported regions, refer AWS website.
Job handling for complex pipelines sometimes may become very tricky in handling unless. This still requires proper development/pipeline preparation skills.
AWS Data Pipeline sometimes gives non-meaningful exception errors, which makes it difficult for a developer to troubleshoot. Requires a lot of improvement on this front.
Simplify Data Analysis using LIKE.TG ’s No-code Data Pipeline
LIKE.TG Data, a No-code Data Pipeline helps to Load Data from any data source such as Databases, SaaS applications, Cloud Storage, SDKs, and Streaming Services and simplifies the ETL process. It supports 150+ data sources, including Aurora, etc., and is a 3-step process by just selecting the data source, providing valid credentials, and choosing the destination. LIKE.TG loads the data onto the desired Data Warehouse, enriches the data, and transforms it into an analysis-ready form without writing a single line of code.
Get Started with LIKE.TG for free
Check out why LIKE.TG is the Best:
Secure: LIKE.TG has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
Schema Management: LIKE.TG takes away the tedious task of schema management automatically detects the schema of incoming data and maps it to the destination schema.
Minimal Learning: LIKE.TG , with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
LIKE.TG Is Built To Scale: As the number of sources and the volume of your data grows, LIKE.TG scales horizontally, handling millions of records per minute with very little latency.
Incremental Data Load: LIKE.TG allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
Live Support: The LIKE.TG team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
Live Monitoring: LIKE.TG allows you to monitor the data flow and check where your data is at a particular point in time.
Sign up here for a 14-day Free Trial!
Conclusion
The article introduced you to Amazon Aurora and Amazon Redshift. It provided you a step-by-step guide to replicate data from Aurora to Redshift using AWS Data Pipeline. Furthermore, it also provided you the pros and cons to go with AWS Data Pipeline.
Amazon Aurora to Redshift Replication using AWS Data Pipeline is convenient during the cases where you want to have full control over your resources and environment. It is a good service for the people who are competent at implementing ETL solution logic. However, in our opinion, this service has not been effective and not that much success as compared to other data movement services.
This service has been launched quite a long back and is still available in a few regions. However, having said that since AWS data pipeline support multi-region data movement, you can Select Pipeline in the nearest region and perform the data movement operation using resources of the region for you movement (be careful about security and compliance).
With the complexity involves in Manual Integration, businesses are leaning more towards Automated and Continous Integration. This is not only hassle-free but also easy to operate and does not require any technical proficiency. In such a case, LIKE.TG Data is the right choice for you! It will help simplify the Marketing Analysis. LIKE.TG Data supports platforms like Aurora, etc.
While you rest, LIKE.TG will take responsibility for fetching the data and moving it to your destination warehouse. Unlike AWS Data pipeline, LIKE.TG provides you with an error-free, completely controlled setup to transfer data in minutes.
Visit our Website to Explore LIKE.TG
Want to take LIKE.TG for a spin? Sign Up for a 14-day free trial and experience the feature-rich LIKE.TG suite first hand.
Share your experience of setting up Aurora to Redshift Integration in the comments section below!
Loading Data from Oracle to Redshift: 2 Easy Methods
Is your Oracle server getting too slow for analytical queries now? Or do you think you are paying too much money to increase the storage capacity or compute power of your Oracle instance? Or are you looking to join and combine data from multiple databases seamlessly? Whatever the case may be, Amazon Redshift offers amazing solutions to the above problems. Hence there is little to think about before moving your data from an Oracle to Amazon Redshift cluster.This article covers the basic idea behind the two architectures and the detailed steps you need to follow to migrate data from Oracle to Redshift. Additionally, it also covers why you should consider implementing an ETL solution such as LIKE.TG Data to make the migration smooth and efficient.
Overview on Oracle and Amazon Redshift
Oracle is fundamentally a Proprietary, Multi-Model, Relational Database System used for Data Warehousing and Online Transaction Processing (OLTP). However, the most recent versions include features similar to cloud-based solutions (such as Amazon Redshift) like columnar storage, on-cloud deployment, etc.
Amazon Redshift is a PostgreSQL standard-based, efficiently scalable, entirely managed, on-cloud database optimized for Online Analytical Processing (OLAP) and data warehousing. One can get things started very quickly in just two steps –
Launch a Redshift cluster via simple API calls or through the AWS Management Console.Connect the local SQL client to this Redshift instance.
There are many advantages of Redshift’s Unique Architecture.Deciding to move data from Oracle to Redshift is the right step in stepping up your data analytics infrastructure.
Methods to Load Data from Oracle to Redshift
Method 1: Custom ETL Scripts to Load Data from Oracle to Redshift
Hand code ETL scripts and configure jobs to move Oracle data to Redshift
Method 2: Setting Up Oracle to Redshift Integration using LIKE.TG Data
LIKE.TG Data, a No-code Data Pipeline, provides you a fully automated platform to set up Oracle to Redshift Integrationforfree. It is a hassle-free solution to directly connect Oracle to Redshift when you don’t have technical expertise in this field.
Sign up here for a 14-day Free Trial!
Methods to Load Data from Oracle to Redshift
There are majorly 2 methods of loading data from Oracle to Redshift:
Method 1: Custome ETL Scripts to Load Data from Oracle to RedshiftMethod 2: Setting Up Oracle to Redshift Integration using LIKE.TG Data
Let’s walk through these methods one by one.
Method 1: Custome ETL Scripts to Load Data from Oracle to Redshift
It is a really easy and straightforward way to move data from Oracle to Amazon Redshift. This method involves 4 major steps:
Step 1: Exporting Data from an Oracle Table via SpoolStep 2: Copying a Flat File onto an AWS S3 BucketStep 3: Creating an Empty Table and Loading Data from the AWS S3 Bucket
These steps are illustrated in technical detail via an example in the following section.
Step 1: Exporting Data from an Oracle Table via Spool
One of the most common ways to export Oracle data onto a flat-file is using the Spool command. Here’s an example of how to do it –
SPOOL c:oracleorgemp.csv
SELECT employeeno || ',' ||
employeename || ',' ||
job || ',' ||
manager || ',' ||
TO_CHAR(hiredate,'YYYY-MM-DD') AS hiredate || ',' ||
salary || ',' ||
FROM employee
ORDER BY employeeno;
SPOOL OFF
The above code exports all records available in employees into the emp.csv file under the org folder as mentioned. The CSV file could then be zipped (using “$ gzip emp.csv”) for compression before moving to the AWS S3 Bucket.
Step 2: Copying a Flat File onto an AWS S3 Bucket
AWS provides S3 Buckets to store files that could be loaded into an Amazon Redshift instance using the COPY command. To drop a local file into an AWS S3 Bucket, you could run a ‘COPY command’ on the AWS Command Line Interface. Here’s how you would do it –
aws s3 cp //oracle/org/emp.csv s3://org/empl/emp.csv.gz
However, if you’d prefer the Graphical User Interface (GUI) way, you could go over to your AWS S3 console https://console.aws.amazon.com/s3/home, and copy-paste your “emp.csv” file into the desired Amazon S3 Bucket.
Step 3: Creating an Empty Table and Loading Data from the AWS S3 Bucket
Before running the COPY command, an empty table must be created in the database to absorb the “emp.csv” file now available on the Amazon S3 Bucket.
The employee table on Redshift can be created using the following code:
SET SEARCH_PATH TO PUBLIC; // selecting the schema
CREATE TABLE EMPLOYEE (
cmployeeno INTEGER NOT NULL,
employeename VARCHAR,
job VARCHAR,
manager VARCHAR,
hiredate DATE,
salary INTEGER
DISTKEY(hiredate)
SORTKEY(employeeno)
)
The flat file copied over to S3 can be loaded into the above table using the following :
SET SEARCH_PATH TO PUBLIC;
COPY EMPLOYEE
FROM 's3://org/empl/emp.csv.gz'
'AWS_ACCESS_KEY_ID=MY_ACCESS_KEY AWS_SECRET_ACCESS_KEY=MY_SECRET_KEY'
GZIP;
Once you are done with the above steps, you need to increment the load from Oracle to Redshift. So, keep reading!
Incremental Load from Oracle to Redshift
The above is an example to demonstrate the process of moving data from Oracle to Redshift. In reality, this would be performed, typically every day, on an entire database consisting of 10s or 100s of tables in a scheduled and automated fashion. Here is how this is done.
Step 1: Iterative Exporting of TablesStep 2: Copying CSV Files to AWS S3Step 3: Importing AWS S3 Data into Redshift
Step 1: Iterative Exporting of Tables
The following script will go through each table one by one. Next, it will export the data in each of them into a separate CSV file with the filename as the *name of the table*_s3.
begin
for item in (select table_name from user_tables)
loop
dbms_output.put_line('spool '||item.table_name||'_s3.csv');
dbms_output.put_line('select * from'||item.table_name||’;’);
dbms_output.put_line('spool off');
end loop;
end;
Step 2: Copying CSV Files to AWS S3
The exported .csv files can be uploaded to an S3 bucket using the following command:
aws s3 cp <your directory path> s3://<your bucket name> --grants read=uri=http://acs.amazonaws.com/groups/global/AllUsers --recursive
Step 3: Importing AWS S3 Data into Redshift
As mentioned before, this process is typically done every 24 hours on a whole lot of data. Hence, you must ensure that there is no data loss as well as no duplicate data. The 2nd part (duplicate data) is particularly relevant when copying data over to Redshift because Redshift doesn’t enforce Primary Key constraints.
Now, you can drop all the data in your Redshift instance and load the entire Oracle Database every time you are performing the data load. However, this is quite risky in regards to data loss and also very inefficient and computationally intensive. Hence, a good way to efficiently perform the data loads while ensuring data consistency would be to:
Copy the AWS S3 flat file data into a temp table: This is achieved by running the ‘COPY’ command the same way as explained in “Step 3” before.Compare the temp table data with the incoming data (the .csv files): See the section Data Loads: SCD Type 1 and Type Resolve any data inconsistency issues: See the section Data Loads: SCD Type 1 and Type 2Remove data from the Parent Table and copy the new and clean up data from the Temp Table. Run the following commands:
begin;
delete from employee where *condition* (depends on what data is available in the temp table)
insert into employee select * from emp_temp_table;
end;
Data Loads – SCD Type 1 and Type 2
Generally, while comparing the existing table data with the new stream of data (S3 bucket data, in this case) one or both of the following methods is used to complete the data load:
Type 1 or Upsert: A new record is either inserted or updated. The update happens only when the primary key of the incoming record matches with the primary key of an existing record. Here is an example:
Existing Record:
Incoming Record:
Final Table (After Upsert):
Type 2 or Maintain History: In this scenario, if the primary key of the incoming record matches with the primary key of an existing record, the existing record is end dated or flagged to reflect that it is a past record. Here is the Type 2 for the above example –
Existing Record:
Incoming Record:
Final Table (After Type 2):
Limitations of Using Custom ETL Scripts to Load Data from Oracle to Redshift
Although a Custom Script (or more likely a combination of Custom Scripts) written to execute the above steps will work, it will be tedious to ensure the smooth functioning of such a system due to the following reasons:
There are many different kinds of steps that are needed to be executed in a dependent fashion without failure.The incremental load is especially difficult to code and execute in such a way as to ensure there is no data loss and/or data inconsistencies. Doing a full load every time puts a lot of load on the Oracle database.As mentioned, this is typically done once every day. Lately, however, people want to look at more real-time data. Hence, this will have to be executed a lot more frequently than once in 24 hours. That is going to test the robustness and thoroughness of your solution a lot more.
Method 2: Setting Up Oracle to Redshift Integration using LIKE.TG Data
LIKE.TG Data, a No-code Data Pipeline helps to Load Data from any data source such as Databases, SaaS applications, Cloud Storage, SDKs, and Streaming Services and simplifies the ETL process. It supports 100+ data sources including Oracle, etc., and is a 3-step process by just selecting the data source, providing valid credentials, and choosing the destination. LIKE.TG loads the data onto the desired Data Warehouse, enriches the data, and transforms it into an analysis-ready form without writing a single line of code.
Its completely automated pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different Business Intelligence (BI) tools as well.
Get Started with LIKE.TG for free
Check out why LIKE.TG is the Best:
Secure: LIKE.TG has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.Schema Management: LIKE.TG takes away the tedious task of schema management automatically detects the schema of incoming data and maps it to the destination schema.Minimal Learning: LIKE.TG , with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.LIKE.TG Is Built To Scale: As the number of sources and the volume of your data grows, LIKE.TG scales horizontally, handling millions of records per minute with very little latency.Incremental Data Load: LIKE.TG allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.Live Support: The LIKE.TG team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.Live Monitoring: LIKE.TG allows you to monitor the data flow and check where your data is at a particular point in time.
Sign up here for a 14-day Free Trial!
Conclusion
Furthermore, LIKE.TG has an intuitive user interface that lets, even the not-so-technical people, easily tweak the parameters of your data load settings. This would come in super handy once you have everything up and running.
With LIKE.TG , you can achieve seamless and accurate data replication from Oracle to Redshift. With its fault-tolerant architecture, LIKE.TG ensures that no data is lost while loading. This empowers you to focus on the right projects instead of worrying about data availability.
Visit our Website to Explore LIKE.TG
Want to take LIKE.TG for a spin? Sign Up for a 14-day free trial and experience the feature-rich LIKE.TG suite first hand.
Share your experience of loading data from Oracle to Redshift in the comments section below!
Asana to Redshift: 2 Easy Methods
Asana is a Work Management Platform available as a Cloud Service that helps users to organize, track and manage their tasks. Asana helps to plan and structure work in a way that suits organizations. All activities involved in a typical organization, right from Project Planning to Task Assignment, Risk Forecasting, Assessing Roadblocks, and changing plans can be handled on the same platform. All this comes at a very flexible pricing plan based on the number of users per month.There is also a free version available for teams up to a size of 15. Almost 70000 organizations worldwide use asana for managing their work. Since the platform is closely coupled with the day-to-day activities of the organizations, it is only natural that the organizations will want to have the data imported into their Data Warehouse for analysis and building insights. This is where Amazon Redshift comes into play.
In this blog post, you will learn to load data from Asana to Redshift Data Warehouse which is one of the most widely used completely managed Data Warehouse services.
Introduction to Asana
Asana is a Project Management Software that provides a comprehensive set of APIs to build applications using their platform. Not only does it allow to access data, but it also has APIs to insert, update and delete data related to any item in the platform. It is important to understand the object hierarchy followed by Asana before going into detail on how to access the APIs.
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Asana Object Hierarchy
Objects in Asana are organized as the below basic units.
Tasks: Tasks represent the most basic unit of action in Asana.Projects: Tasks are organized into projects. A project represents a collection of tasks that can be viewed as a Board, List, or Timeline.Portfolio: A portfolio is a collection of projects.Sections: Tasks can also be grouped as sections. Sections usually represent something lower in the hierarchy than projects.Subtasks: Tasks can be represented as a collection of subtasks. Subtasks are similar to the task, except that they have a parent task.
Users in Asana are organized as workspaces, organizations, and teams. Workspaces are the highest-level units. Organizations are special workspaces that represent an actual company. Teams are a group of users who collaborate on projects.
Asana API Access
Asana allows API access through two mechanisms
OAuth: This method requires an application to be registered in the Asana admin panel and user approval to allow data access through his account. This is meant to be used while implementing applications using the Asana platform.Personal Access Token: A personal access token can be created from the control panel and used to successfully execute API calls using an authorization header key. This is meant to be used while implementing simple scripts. We will be using this method to access Asana data.
Asana API Rate Limits
Asana API enforces rate limits to maintain the stability of its systems. Free users can make up to 150 requests per minute and premium users can make up to 1500 requests per minute. There is also a limit to the concurrent number of requests. Users can make up to 50 read requests and 15 write requests concurrently.
There is also a limit based on the cost of a request. Some of the API requests may be costly at the back end since Asana will have to traverse a large nested graph to provide the output. Asana does not explicitly mention the exact cost quota but emphasizes that if you make too many costly requests, you could get an error as a response.
For more information on Asana, click here.
Introduction to Amazon Redshift
Amazon Redshift is a completely managed database offered as a cloud service by Amazon Web Services (AWS). It offers a flexible pricing plan with the users only having to pay for the resources they use. A detailed article on Amazon Redshift’s pricing plan can be found here. AWS takes care of all the activities related to maintaining a highly reliable and stable Data Warehouse. The customers can thus focus on their business logic without worrying about the complexities of managing a large infrastructure.
Amazon Redshift is designed to run complex queries over large amounts of data and provide quick results. It accomplishes this through the use of Massively Parallel Processing (MPP) architecture. Amazon Redshift works based on a cluster of nodes. One of the nodes is designated as a Leader Node and others are known as Compute Nodes. Leader Node handles client communication, query optimization, and task assignment.
Redshift can be scaled seamlessly by adding more nodes or upgrading existing nodes. Redshift can handle up to a PB of data. Redshift’s concurrency scaling feature can automatically scale the cluster up and down during high load times while staying within the customer’s budget constraints. Users can enjoy a free hour of concurrency scaling for every 24 hours of a Redshift cluster staying operational.
For more information on Amazon Redshift, click here.
Methods to Replicate Data from Asana to Redshift
Method 1: Build a Custom Code to Replicate Data from Asana to Redshift
You will invest engineering bandwidth to hand-code scripts to get data from Asana’s API to S3 and then to Redshift. Additionally, you will also need to monitor and maintain this setup on an ongoing basis so that there is a consistent flow of data.
Method 2: Replicate Data from Asana to Redshift using LIKE.TG Data
Bringing data from Asana works pretty much out of the box while using a solution likethe LIKE.TG Data Integration Platform.With minimal tech involvement, the data can be reliably streamed from Asana to Redshift in real-time.
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Methods to Replicate Data from Asana to Redshift
Broadly, there are 2 methods to replicate your data from Asana to Redshift. Those methods are listed here:
Method 1: Build a Custom Code to Replicate Data from Asana to RedshiftMethod 2: Replicate Data from Asana to Redshift using LIKE.TG Data
Now, let’s go through these methods one by one.
Method 1: Build a Custom Code to Replicate Data from Asana to Redshift
The objective here is to import a list of projects from Asana to Redshift. You will be using the project API in Asana to accomplish this. To access the API, you will first need a personal access token. Let us start by learning how to generate the personal access token. Follow the steps below to build a custom code to replicate data from Asana to Redshift:
Step 1: Access the Personal Access TokenStep 2: Access the API using Personal Access TokenStep 3: Convert JSON to CSVStep 4: Copy the CSV File to AWS S3 BucketStep 5: Copy Data to Amazon Redshift
Step 1: Access the Personal Access Token
Follow the steps below to access the Personal Access Token:
Go to the “Developer App Management” page in Asana and click on the “My Profile” settings.Go to “Apps” and then to “Manage Developer Apps” and then click on “Create New Personal Access Token”.Add a description and click on “Create”.Copy the token that is displayed.Note: This token will only be shown once and if you do not copy it, you will need to create another one.
Step 2: Access the API using Personal Access Token
With the Personal Access Token that you copied in your last step, access the API as follows.
curl -X GET https://app.asana.com/api/1.0/projects -H 'Accept: application/json' -H 'Authorization: Bearer {access-token}'
The response will be a JSON in the following format.
{ "data": [ { "gid": "12345", "resource_type": "project", "name": "Stuff to buy", "created_at": "2012-02-22T02:06:58.147Z", "archived": false, "color": "light-green", … }, {....}
The response will contain a key named data. The value for the “data”. Key will be a list of project details formatted as a nested JSON. Save the file as “projects.json”.
Step 3: Convert JSON to CSV
Use the command-line JSON processor utility jq to convert the JSON to CSV for loading to MySQL. For simplicity, you will only convert the name, created_at, and due_date attributes of project details.
jq -r '.data[] | [.name, .start_on, .due_on] | @csv' projects.json > projects.csv
Note: name, start_on, and due_on are fields contained in the response JSON. If you need different fields, you will have to modify the above code as per your requirement.
Step 4: Copy the CSV File to AWS S3 Bucket
Copy the CSV file to an AWS S3 Bucket location using the following code.
aws s3 cp projects.csv s3://my_bucket/projects/
Step 5: Copy Data to Amazon Redshift
Login to “AWS Management Console” and type the following command in the Query Editor in the Redshift console and execute.
copy target_table_name from ‘s3://my_bucket/projects/ credentials access_key_id <access_key_id> secret_access_key <secret_access_key>
Note: access_key_id and secret_access_key represent the IAM credentials.
That concludes the effort. You have successfully copied data from Asana to Redshift. However, you still have only imported 1 table into your Amazon Redshift. For that table, you have only ingested 3 columns. Asana has a large number of objects in its database with each object having numerous columns. To accommodate all these in our current approach, you will need to implement a complex script using a programming language.
Drawbacks of Building a Custom Code to Replicate Data from Asana to Redshift
Listed below are the drawbacks of building a custom code to replicate data from Asana to Redshift:
Asana provides some of the critical information related to objects like authors, workspaces, etc are available inside nested JSON structures in the original JSON. These can only be extracted by implementing a lot of custom logic.In most cases, the import function will need to be executed periodically to maintain a recent copy. Such jobs will need mechanisms to handle duplicates and scheduled operations.The above method uses a complete overwrite of the Redshift table. This may not be always practical. In case incremental load is required, the Redshift INSERT INTO command will be required. But this command does not manage duplicates on its own. So a temporary table and related logic to handle duplicates will be required.The current approach does not have any mechanism to handle the rate limits by Asana.Any further improvement to the current approach will need the developers to have a lot of domain knowledge in Asana and their object hierarchy structure.
A wise choice would be using a simple ETL tool like LIKE.TG and do not worry about all these complexities in implementing a custom ETL from Asana to Redshift
Towards the end, the blog discusses the drawbacks of this approach and highlights simpler alternatives to achieve the same objective.
Method 2: Replicate Data from Asana to Redshift using LIKE.TG Data
LIKE.TG Data, a No-code Data Pipeline helps to Load Data from any data source such as Databases, SaaS applications, Cloud Storage, SDKs, and Streaming Services and simplifies the ETL process. It supports 100+ data sources including Asana, etc., and is a 3-step process by just selecting the data source, providing valid credentials, and choosing the destination. LIKE.TG loads the data onto the desired Data Warehouse, enriches the data, and transforms it into an analysis-ready form without writing a single line of code.
Its completely automated pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different Business Intelligence (BI) tools as well.
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Check out why LIKE.TG is the Best:
Secure: LIKE.TG has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.Schema Management: LIKE.TG takes away the tedious task of schema management automatically detects the schema of incoming data and maps it to the destination schema.Minimal Learning: LIKE.TG , with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.LIKE.TG Is Built To Scale: As the number of sources and the volume of your data grows, LIKE.TG scales horizontally, handling millions of records per minute with very little latency.Incremental Data Load: LIKE.TG allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.Live Support: The LIKE.TG team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.Live Monitoring: LIKE.TG allows you to monitor the data flow and check where your data is at a particular point in time.
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Conclusion
The article introduced you to Asana and Amazon Redshift. It also provided 2 methods that you can use to replicate data from Asana to Redshift. The 1st method involves Manual Integration while the 2nd method involves Automated Continous Integration.
With the complexity involves in Manual Integration, businesses are leaning more towards Automated Integration. This is not only hassle-free but also easy to operate and does not require any technical proficiency. In such a case, LIKE.TG Data is the right choice for you! It will help simplify the Web Analysis process by setting up Asana to Redshift Integration.
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AWS Aurora to Redshift: 9 Easy Steps
AWS Data Migration Service (DMS) is a Database Migration service provided by Amazon. Using DMS, you can migrate your data from one Database to another Database. It supports both, Homogeneous and Heterogeneous Database Migration. DMS also supports migrating data from the on-prem Database to AWS Database services. As a fully managed service, Amazon Aurora saves you time by automating time-consuming operations like provisioning, patching, backup, recovery, and failure detection and repair.
Amazon Redshift is a cloud-based, fully managed petabyte-scale data warehousing service. Starting with a few hundred gigabytes of data, you may scale up to a petabyte or more. This allows you to gain fresh insights for your company and customers by analyzing your data.
In this article, you will be introduced to AWS DMS. You will understand the steps to load data from Amazon Aurora to Redshift using AWS DMS. You also explore the pros and cons associated with this method. So, read along to gain insights and understand the loading of data from Aurora to Redshift using AWS DMS.
What is Amazon Aurora?
Amazon Aurora is a popular database engine with a rich feature set that can import MySQL and PostgreSQL databases with ease. It delivers enterprise-class performance while automating all common database activities. As a result, you won’t have to worry about managing operations like data backups, hardware provisioning, and software updates manually.
Amazon Aurora offers great scalability and data replication across various zones thanks to its multi-deployment tool. As a result, consumers can select from a variety of hardware specifications to meet their needs. The server-less functionality of Amazon Aurora also controls database scalability and automatically upscales or downscales storage as needed. You will only be charged for the time the database is active in this mode.
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Key Features of Amazon Aurora
Amazon Aurora’s success is aided by the following features:
Exceptional Performance: The Aurora database engine takes advantage of Amazon’s CPU, memory, and network capabilities thanks to software and hardware improvements. As a result, Aurora considerably exceeds its competition.
Scalability: Based on your database usage, Amazon Aurora will automatically scale from a minimum of 10 GB storage to 64 TB storage in increments of 10 GB at a time. This will have no effect on the database’s performance, and you won’t have to worry about allocating storage space as your business expands.
Backups: Amazon Aurora offers automated, incremental, and continuous backups that don’t slow down your database. This eliminates the need to take data snapshots on a regular basis in order to keep your data safe.
High Availability and Durability: Amazon RDS continuously monitors the health of your Amazon Aurora database and underlying Amazon Elastic Compute Cloud (Amazon EC2) instance. In the event of a database failure, Amazon RDS will automatically resume the database and associated activities. With Amazon Aurora, you don’t need to replay database redo logs for crash recovery, which cuts restart times in half. Amazon Aurora also isolates the database buffer cache from the database process, allowing it to survive a database restart.
High Security: Aurora is integrated with AWS Identity and Access Management (IAM), allowing you to govern what your AWS IAM users and groups may do with specific Aurora resources (e.g., DB Instances, DB Snapshots, DB Parameter Groups, DB Event Subscriptions, DB Options Groups). You can also use tags to restrict what activities your IAM users and groups can take on groups of Aurora resources with the same tag (and tag value).
Fully Managed: Amazon Aurora will keep your database up to date with the latest fixes. You can choose whether and when your instance is patched with DB Engine Version Management. You can manually stop and start an Amazon Aurora database with a few clicks. This makes it simple and cost-effective to use Aurora for development and testing where the database does not need to be up all of the time. When you suspend your database, your data is not lost.
Developer Productivity: Aurora provides machine learning capabilities directly from the database, allowing you to add ML-based predictions to your applications using the regular SQL programming language. Thanks to a simple, efficient, and secure connectivity between Aurora and AWS machine learning services, you can access a wide range of machine learning algorithms without having to build new integrations or move data around.
What is Amazon Redshift?
Amazon Redshift is a petabyte-scale data warehousing service that is cloud-based and completely managed. It allows you to start with a few gigabytes of data and work your way up to a petabyte or more. Data is organised into clusters that can be examined at the same time via Redshift. As a result, Redshift data may be rapidly and readily retrieved. Each node can be accessed individually by users and apps.
Many existing SQL-based clients, as well as a wide range of data sources and data analytics tools, can be used with Redshift. It features a stable architecture that makes it simple to interface with a wide range of business intelligence tools.
Each Redshift data warehouse is fully managed, which means administrative tasks like backup creation, security, and configuration are all automated.
Because Redshift was designed to handle large amounts of data, its modular design allows it to scale easily. Its multi-layered structure enables handling several inquiries at once simple.
Slices can be created from Redshift clusters, allowing for more granular examination of data sets.
Key Features of Amazon Redshift
Here are some of Amazon Redshift’s important features:
Column-oriented Databases: In a database, data can be organised into rows or columns. Row-orientation databases make up a large percentage of OLTP databases. In other words, these systems are built to perform a huge number of minor tasks such as DELETE, UPDATE, and so on. When it comes to accessing large amounts of data quickly, a column-oriented database like Redshift is the way to go. Redshift focuses on OLAP operations. The SELECT operations have been improved.
Secure End-to-end Data Encryption: All businesses and organisations must comply with data privacy and security regulations, and encryption is one of the most important aspects of data protection. Amazon Redshift uses SSL encryption for data in transit and hardware-accelerated AES-256 encryption for data at rest. All data saved to disc is encrypted, as are any backup files. You won’t need to worry about key management because Amazon will take care of it for you.
Massively MPP (Multiple Processor Parallelization): Redshift, like Netezza, is an MPP appliance. MPP is a distributed design approach for processing large data sets that employs a “divide and conquer” strategy among multiple processors. A large processing work is broken down into smaller tasks and distributed among multiple compute nodes. To complete their calculations, the compute node processors work in parallel rather than sequentially.
Cost-effective: Amazon Redshift is the most cost-effective cloud data warehousing alternative. The cost is projected to be a tenth of the cost of traditional on-premise warehousing. Consumers simply pay for the services they use; there are no hidden costs. You may discover more about pricing on the Redshift official website.
Scalable: Amazon Redshift, a petabyte-scale data warehousing technology from Amazon, is scalable. Redshift from Amazon is simple to use and scales to match your needs. With a few clicks or a simple API call, you can instantly change the number or kind of nodes in your data warehouse, and scale up or down as needed.
What is AWS Data Migration Service (DMS)?
Using AWS Data Migration Service (DMS) you can migrate your tables from Aurora to Redshift. You need to provide the source and target Database endpoint details along with Schema Names. DMS uses a Replication Instance to process the Migration task. In DMS, you need to set up a Replication Instance and provide the source and target endpoint details. Replication Instance reads the data from the source and loads the data into the target. This entire processing happens in the memory of the Replication Instance. For migrating a high volume of data, it is recommended to use Replication Instances of higher instance classes.
To explore more about AWS DMS, visit here.
Seamlessly Move Data from Aurora to Redshift Using LIKE.TG ’s No Code Data Pipeline
Method 1: Move Data from Aurora to Redshift Using AWS DMS
This method requires you to manually write a custom script that makes use of AWS DMS to transfer data from Aurora to Redshift.
Method 2: Move Data from Aurora to Redshift Using LIKE.TG Data
LIKE.TG Data, an Automated No Code Data Pipelineprovides you a hassle-free solution for connectingAurora PostgreSQL to Amazon Redshiftwithin minutes with an easy-to-use no-code interface. LIKE.TG is fully managed and completely automates the process of not only loading data from Aurora PostgreSQL but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code.
LIKE.TG ’s fault-tolerant Data Pipeline offers a faster way to move data from databases or SaaS applications into your Redshift account. LIKE.TG ’s pre-built integration with Aurora PostgreSQL along with100+ other data sources(and 40+ free data sources)will take full charge of the data transfer process, allowing you to focus on key business activities
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Why Move Data from Amazon Aurora to Redshift?
Aurora is a row-based database, therefore it’s ideal for transactional queries and web apps. Do you need to check for a user’s name using their id? Aurora makes it simple. Do you want to count or average all of a user’s widgets? Redshift excels in this area. As a result, if you want to utilize any of the major Business Intelligence tools on the market today to analyze your data, you’ll need to employ a data warehouse like Redshift. You can use LIKE.TG for this to make the process easier.
Methods to Move Data from Aurora to Redshift
You can easily move your data from Aurora to Redshift using the following 2 methods:
Method 1: Move Data from Aurora to Redshift Using LIKE.TG Data
Method 2: Move Data from Aurora to Redshift Using AWS DMS
Method 1: Move Data from Aurora to Redshift Using LIKE.TG Data
LIKE.TG Data, an Automated Data Pipeline helps you directly transfer data fromAurora to Redshiftin a completely hassle-free automated manner.LIKE.TG is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. You can seamlessly ingest data from your Amazon Aurora PostgreSQL database using LIKE.TG Pipelines and replicate it to a Destination of your choice.
While you unwind, LIKE.TG will take care of retrieving the data and transferring it to your destination Warehouse. Unlike AWS DMS, LIKE.TG provides you with an error-free, fully managed setup to move data in minutes. You can check a detailed article to compare LIKE.TG vs AWS DMS.
Refer to these documentations for detailed steps for integration of Amazon Aurora to Redshift.
The following steps can be implemented to connect Aurora PostgreSQL to Redshift using LIKE.TG :
Step 1) Authenticate Source: Connect Aurora PostgreSQL as the source to LIKE.TG ’s Pipeline.
Step 2) Configure Destination: Configure your Redshift account as the destination for LIKE.TG ’s Pipeline.
Check out what makes LIKE.TG amazing:
Secure: LIKE.TG has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
Auto Schema Mapping: LIKE.TG takes away the tedious task of schema management automatically detects the schema of incoming data fromAurora PostgreSQLfilesand maps it to the destination schema.
Quick Setup: LIKE.TG with its automated features, can be set up in minimal time. Moreover, with its simple and interactive UI, it is extremely easy for new customers to work on and perform operations.
Transformations: LIKE.TG provides preload transformations through Python code. It also allows you to run transformation code for each event in the Data Pipelines you set up. You need to edit the event object’s properties received in the transform method as a parameter to carry out the transformation. LIKE.TG also offers drag and drop transformations like Date and Control Functions, JSON, and Event Manipulation to name a few. These can be configured and tested before putting them to use for aggregation.
LIKE.TG Is Built To Scale: As the number of sources and the volume of your data grows, LIKE.TG scales horizontally, handling millions of records per minute with very little latency.
Incremental Data Load: LIKE.TG allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
Live Support: The LIKE.TG team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
With continuous Real-Time data movement, LIKE.TG allows you to combine Aurora PostgreSQL data along with your other data sources and seamlessly load it to Redshift with a no-code, easy-to-setup interface. Try our 14-day full-feature access free trial!
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Method 2: Move Data from Aurora to Redshift Using AWS DMS
Using AWS DMS, perform the following steps to transfer your data from Aurora to Redshift:
Step 1: Let us create a table in Aurora (Table name redshift.employee). We will move the data from this table to Redshift using DMS.
Step 2: We will insert some rows in the Aurora table before we move the data from this table to Redshift.
Step 3: Go to the DMS service and create a Replication Instance.
Step 4: Create source and target endpoint and test the connection from the Replication Instance.
Once both the endpoints are created, it will look as shown below:
Step 5: Once Replication Instance and endpoints are created, create a Replication task. The Replication task will take care of your migration of data.
Step 6: Select the table name and schema, which you want to migrate. You can use % as wildcards for multiple tables/schema.
Step 7: Once setup is done, start the Replication task.
Step 8: Once the Replication task is completed, you can see the entire details along with the assessment report.
Step 9: Now, since the Replication task has completed its activity, let us check the data in Redshift to know whether the data has been migrated.
As shown in the steps above, DMS is pretty handy when it comes to Replicating data from Aurora to Redshift but it requires performing a few manual activities.
Pros of Moving Data from Aurora to Redshift using AWS DMS
Data movement is secure as Data Security is fully managed internally by AWS.
No Database downtime is needed during the Migration.
Replication task setup requires just a few seconds.
Depending upon the volume of Data Migration, users can select the Replication Instance type and the Replication task will take care of migrating the data.
You can migrate your data either in Full mode or in CDC mode. In case your Replication task is running, a change in the data in the source Database will automatically reflect in the target database.
DMS migration steps can be easily monitored and troubleshot using Cloudwatch Logs and Metrics. You can even generate notification emails depending on your rules.
Migrating data to Redshift using DMS is free for 6 months.
Cons of Moving Data from Aurora to Redshift using AWS DMS
While copying data from Aurora to Redshift using AWS DMS, it does not support SCT (Schema Conversion Tool) for your Automatic Schema conversion which is one of the biggest demerits of this setup.
Due to differences in features of the Aurora Database and Redshift Database, you need to perform a lot of manual activities for the setup i.e. DMS does not support moving Stored Procedures since in Redshift there is no concept of Stored Procedures, etc.
Replication Instance has a limitation on storage limit. It supports up to 6 TB of data.
You cannot migrate data from Aurora from one region to another region meaning both the Aurora Database and Redshift Database should be in the same region.
Conclusion
Overall the DMS approach of replicating data from Aurora to Redshift is satisfactory, however, you need to perform a lot of manual activities before the data movement. Few features that are not supported in Redshift have to be handled manually as SCT does not support Aurora to Redshift data movement.
In a nutshell, if your manual setup is ready and taken care of you can leverage DMS to move data from Aurora to Redshift. You can also refer to our other blogs where we have discussed Aurora to Redshift replication using AWS Glue and AWS Data Pipeline.
LIKE.TG Data provides an Automated No-code Data Pipeline that empowers you to overcome the above-mentioned limitations. LIKE.TG caters to 100+ data sources (including 40+ free sources) and can seamlessly transfer your data from Aurora PostgreSQL to Redshift within minutes. LIKE.TG ’s Data Pipeline enriches your data and manages the transfer process in a fully automated and secure manner without having to write any code. It will make your life easier and make data migration hassle-free.
Learn more about LIKE.TG
Want to take LIKE.TG for a spin? Sign Up for a 14-day free trial and experience the feature-rich LIKE.TG suite first hand.
You can also have a look at our unbeatable pricing that will help you choose the right plan for your business needs!
TokuDB to Snowflake: 2 Easy Methods
The need to store, transform, analyze and share data is growing exponentially, with demand for cloud-based data analytics and data warehouse solutions also on the rise. Using the cloud for data processing, analytics, and reporting has now become quite popular mainly due to its convenience and superior performance.In this blog post, we will go over a migration scenario where a fictional business is attempting to migrate their data from an on-prem TokuDB to Snowflake, a cloud-based data warehouse. To this aim, let’s first compare both solutions.Introduction to TokuDB
TokuDB is a highly scalable MySQL and MariaDB storage engine. It offers high data compression, fast insertions, and deletions, among many other features. This makes it a great solution for use in high-performance and write-intensive environments. It uses a fractal tree data structure and huge data pages to efficiently manage and read the data. However, concurrency, scale, resiliency, and security are some of the bottlenecks that limit TokuDB’s performance. It is available in an open-source version and an enterprise edition.
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Introduction to Snowflake
Snowflake is a cloud data warehouse that came out in 2015. It is primarily available on AWS and Azure. Snowflake is similar to BigQuery in that it stores data separately from where it does its compute. It stores the actual data of your tables in S3 and then it can provision any number of compute nodes to process that data.
In contrast, Snowflake offers instant access to unlimited resources (compute and storage) on-demand.
Snowflake Benefits:
Snowflake is specifically optimized for analytics workloads. It’s therefore ideal for businesses dealing with very complex data sets.Snowflake offers better performance both in terms of storage capacity and query performance.Snowflake also offers better security compared to an on-prem data warehouse. This is because cloud data warehouses are required to meet stringent security requirements.Migrating your data to the cloud is also cost-effective since there is no huge initial outlay and you don’t have to maintain physical infrastructure.
Moving Data from TokuDB to Snowflake
Method 1: Using Custom ETL Scripts to Connect TokuDB to Snowflake
This approach would need you to invest in heavy engineering resources. The broad steps in this approach would need you to understand the S3 data source, write code to extract data from S3, prepare the data, and finally copy it into Snowflake. The details and challenges of each step are described in the next sections.
Method 2: Using LIKE.TG to Connect TokuDB to Snowflake
LIKE.TG is a cloud data pipeline platform that seamlessly moves data from TokuDB to Snowflake in real-time without having to write any code. By deploying LIKE.TG , the data transfer can be completely automated and would not need any human intervention. This will allow you to direct your team’s bandwidth in extracting meaningful insights instead of wrangling with code.
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LIKE.TG ’s pre-built integration with TokuDB (among 100+ Sources) will take full charge of the data transfer process, allowing you to focus on key business activities.
Methods to Connect TokuDB to Snowflake
Here are the methods you can use to establish a connection from TokuDB to Snowflake:
Method 1: Using Custom ETL Scripts to Connect TokuDB to SnowflakeMethod 2: Using LIKE.TG to Connect TokuDB to Snowflake
Method 1: Using Custom ETL Scripts to Connect TokuDB to Snowflake
Here are the steps involved in using Custom ETL Scripts to connect TokuDB and Snowflake:
Step 1: Export TokuDB Tables to CSV FormatStep 2: Upload Source Data Files to Amazon S3Step 3: Create an Amazon S3 StageStep 4: Create a Table in SnowflakeStep 5: Loading Data to SnowflakeStep 6: Validating the Connection from TokuDB to Snowflake
Step 1: Export TokuDB Tables to CSV Format
There are multiple ways to backup a TokuDB database and we will be using a simple SQL command to perform a logical backup.
SELECT * FROM `database_name`.`table_name` INTO OUTFILE 'path_to_folder/filename.csv'
FIELDS ENCLOSED BY '"' TERMINATED BY ';' ESCAPED BY '"' LINES TERMINATED BY 'rn';"
This command dumps the data into CSV format which can then easily be imported into Snowflake.
Repeat this command for all tables and ensure that your TokuDB server has enough storage space to hold the CSV files.
Step 2: Upload Source Data Files to Amazon S3
After generating the CSV/TXT file, we need to upload this data to a place where Snowflake can access it. Install the AWS CLI on your system
How to install the AWS CLI
After that execute the following command.
aws s3 cp filename.csv s3://{YOUR_BUCKET_NAME}
Step 3: Create an Amazon S3 Stage
Using the SnowSQL CLI client, run this command:
create or replace stage my_csv_stage
file_format = mycsvformat
url = 's3://{YOUR_BUCKET_NAME}';
The example above creates an external stage named my_csv_stage.
Step 4: Create a Table in Snowflake
Create a table with your schema. You will load data into this table in the next step.
create or replace table {YOUR_TABLE_NAME}
('$TABLE_SCHEMA')
Step 5: Loading Data to Snowflake
Loading data requires a Snowflake compute cluster.Run the following command in the SnowSQL CLI client:
copy into {YOUR_TABLE_NAME}
from s3://{YOUR_BUCKET_NAME} credentials=(aws_key_id='$AWS_ACCESS_KEY_ID' aws_secret_key='$AWS_SECRET_ACCESS_KEY')
file_format = (type = csv field_delimiter = '|' skip_header = 1);
This command will load data from all CSV files in the S3 bucket.
Step 6: Validating the Connection from TokuDB to Snowflake
select * from {YOUR_TABLE_NAME} limit 10;
The above approach is effort-intensive. You would need to hand-code many steps that run coherently to achieve the objective.
Limitations of Using Custom ETL Scripts to Connect TokuDB to Snowflake
This method is ideal for a one-time bulk load. In case you are looking to stream data in real-time, you might have to configure cron jobs and write additional code to achieve this.More often than not, the use case to move data from TokuDB to Snowflake is not this straightforward. You might need to clean, transform and enrich the data to make it analysis-ready. This would not be easy to achieve.Since the data moved from TokuDB is critical to your business, you will need to constantly monitor the infrastructure to ensure that nothing breaks. Failure at any step would lead you to irretrievable data loss.
Method 2: Using LIKE.TG to Connect TokuDB to Snowflake
UsingLIKE.TG (Official Snowflake ETL partner)— a managed system that simplifies data migration. LIKE.TG is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.
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LIKE.TG takes care of all your data preprocessing to set up TokuDB Snowflake Integration and lets you focus on key business activities and draw a much powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent reliable solution to manage data in real-time and always have analysis-ready data in your desired destination.
Moving data from TokuDB to Snowflake requires just 2 steps:
Step 1: Connect to your TokuDB database by providing connection settings.
Step 2: Select the mode of replication you want: (a) Load the result set of a Custom Query (b) Full dump of tables (c) Load data via log
Step 3: Configure the Snowflake destination by providing the details like Destination Name, Account Name, Account Region, Database User, Database Password, Database Schema, and Database Name.
Check out what makes LIKE.TG amazing:
Real-Time Data Transfer: LIKE.TG with its strong Integration with 100+ sources, allows you to transfer data quickly efficiently. This ensures efficient utilization of bandwidth on both ends.Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer.Secure: LIKE.TG has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.Tremendous Connector Availability: LIKE.TG houses a large variety of connectors and lets you bring in data from numerous Marketing SaaS applications, databases, etc. such as Google Analytics 4, Google Firebase, Airflow, HubSpot, Marketo, MongoDB, Oracle, Salesforce, Redshift, etc. in an integrated and analysis-ready form.Simplicity: Using LIKE.TG is easy and intuitive, ensuring that your data is exported in just a few clicks.Completely Managed Platform: LIKE.TG is fully managed. You need not invest time and effort to maintain or monitor the infrastructure involved in executing codes.Live Support: The LIKE.TG team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
That is it, LIKE.TG will now take care of reliably loading data from TokuDB to Snowflake in real-time.
Conclusion
This blog talks about the two methods you can use to set up TokuDB Snowflake Integration in a seamless fashion: using custom ETL code and a third-party tool, LIKE.TG .
Extracting complex data from a diverse set of data sources can be a challenging task and this is where LIKE.TG saves the day!
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In addition to TokuDB, LIKE.TG can also bring data from a wide array of data sources into Snowflake. Database (MySQL, PostgreSQL, MongoDB and more), Cloud Applications (Google Analytics, Google Ads, Facebook Ads, Salesforce, and more). This allows LIKE.TG to scale on-demand as your data needs grow.
Sign Up for a full-feature free trial (14 days) to see the simplicity of LIKE.TG first-hand. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.
Google Analytics to Snowflake: 2 Easy Methods
Google Analytics is the most popular web analytics service on the market, used to gather crucial information on website events: web traffic, purchases, signups, and other aspects of browser/customer behavior. However, the vast amount of data that Analytics provides makes it necessary for many users to search for ways to more deeply analyze the information found within the platform. Enter Snowflake, a platform designed from the ground up to be a cloud-based data warehouse. You can read more about Snowflake here. For many users of Analytics, Snowflake is the ideal solution for their data analysis needs, and in this article, we will walk you through the process of moving your data from Google Analytics to Snowflake.Introduction to Google Analytics
Google Analytics (GA) is a Web Analytics service that offers Statistics and basic Analytical tools for your Search Engine Optimization (SEO) and Marketing needs. It’s free and part of Google’s Marketing Platform, so anyone with a Google account may take advantage of it.
Google Analytics is used to monitor website performance and gather visitor data. It can help organizations identify the most popular sources of user traffic, measure the success of their Marketing Campaigns and initiatives, track objective completion, discover patterns and trends in user engagement, and obtain other visitor information, such as demographics. To optimize Marketing Campaigns, increase website traffic, and better retain visitors, small and medium-sized retail websites commonly leverage Google Analytics.
Here are the key features of Google Analytics:
Conversion Tracking: Conversion points (such as a contact form submission, e-commerce sale, or phone call) can be tracked in Google Analytics once they have been recognized on your website. You’ll be able to observe when someone converted, the traffic source that referred them, and much more.Third-Party Referrals: A list of third-party websites that sent you traffic will be available. That way you’ll know which sites are worth spending more time on, as well as if any new sites have started linking to yours.Custom Dashboards: You can create semi-custom Dashboards for your analytics with Google Analytics. You can add Web Traffic, Conversions, and Keyword Referrals to your dashboard if they’re essential to you. To share your reports, you can export your dashboard into PDF or CSV format.Traffic Reporting: Google Analytics is essentially a traffic reporter. How many people visit your site each day will be revealed by the service’s statistics. You may also keep track of patterns over time, which can help you make better decisions about online Marketing.
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Introduction to Snowflake
Snowflake is a cloud data warehouse that came out in 2015. It is primarily available on AWS and Azure.Snowflakeis similar to BigQuery in that it stores data separately from where it does its compute. It stores the actual data of your tables in S3 and then it can provision any number of compute nodes to process that data.
In contrast, Snowflake offers instant access to unlimited resources (compute and storage) on-demand.
Snowflake Benefits:
Snowflake is specifically optimized for analytics workloads. It’s therefore ideal for businesses dealing with very complex data sets.Snowflake offers better performance both in terms of storage capacity and query performance.Snowflake also offers better security compared to an on-prem data warehouse. This is because cloud data warehouses are required to meet stringent security requirements.Migrating your data to the cloud is also cost-effective since there is no huge initial outlay and you don’t have to maintain physical infrastructure.
Methods to Move data from Google Analytics to Snowflake
Before we get started, there are essentially two ways to move your data from Google Analytics to Snowflake:
Method 1: Using Custom ETL Scripts to Move Data from Google Analytics to Snowflake
This would need you to understand the Google Analytics API, build a code to bring data from it, clean and prepare the data and finally, load it to Snowflake. This can be a time-intensive task and (let’s face it) not the best use of your time as a developer.
Method 2: Using LIKE.TG Data to Move Data from Google Analytics to Snowflake
LIKE.TG , a Data Integration Platform gets the same results in a fraction of time with none of the hassles. LIKE.TG can help you bring Google Analytics data to Snowflake in real-time for free without having to write a single line of code.
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LIKE.TG ’s pre-built integration with Google Analytics (among 100+ Sources) will take full charge of the data transfer process, allowing you to focus on key business activities.
This article provides an overview of both the above approaches. This will allow you to assess the pros and cons of both and choose the route that suits your use case best
Understanding the Methods to Connect Google Analytics to Snowflake
Here are the methods you can use to establish a connection from Google Analytics to Snowflake:
Method 1: Using Custom ETL Scripts to Move Data from Google Analytics to SnowflakeMethod 2: Using LIKE.TG Data to Move Data from Google Analytics to Snowflake
Method 1: Using Custom ETL Scripts to Move Data from Google Analytics to Snowflake
Here are the steps you can use to set up a connection from Google Analytics to Snowflake using Custom ETL Scripts:
Step 1: Accessing Data on Google AnalyticsStep 2: Transforming Google Analytics DataStep 3: Transferring Data from Google Analytics to SnowflakeStep 4: Maintaining Data on Snowflake
Step 1: Accessing Data on Google Analytics
The first step in moving your data is to access it, which can be done using the Google Analytics Reporting API. Using this API, you can create reports and dashboards, both for use in your Analytics account as well as in other applications, such as Snowflake. However, when using the Reporting API, it is important to remember that only those with a paid Analytics 360 subscription will be able to utilize all the features of the API, such as viewing event-level data, while users of the free version of Analytics can only create reports using less targeted aggregate data.
Step 2: Transforming Google Analytics Data
Before transferring data to Snowflake, the user must define a complete and well-ordered schema for all included data. In some cases, such as with JSON or XML data types, data does not need a schema in order to be transferred directly to Snowflake. However, many data types cannot be moved quite so readily, and if you are dealing with (for example) Microsoft SQL server data, more work is required on the part of the user to ensure that the data is compatible with Snowflake.
Google Analytics reports are conveniently expressed in the manner of a spreadsheet, which maps well to the similarly tabular data structures of Snowflake. On the other hand, it is important to remember that these reports are samples of primary data, and as such, may contain different values during separate report instances, even over the same time period sampled.
Because Analytics reports and Snowflake data profiles are so similarly structured, a common technique is to map each key embedded in a Report API endpoint response to a mirrored column on the Snowflake data table, thereby ensuring a proper conversion of necessary data types. Because data conversion is not automatic, it is incumbent on the user to revise data tables to keep up with any changes in primary data types.
Step 3: Transferring Data from Google Analytics to Snowflake
There are three primary ways of transferring your data to Snowflake:
COPY INTO – The COPY INTO command is perhaps the most common technique for data transferral, whereby data files (stored either locally or in a storage solution like Amazon S3 buckets) are copied into a data warehouse.PUT – The PUT command may also be used, which allows the user to stage files prior to the execution of the COPY INTO command.Upload – Data files can be uploaded into a service such as the previously mentioned Amazon S3, allowing for direct access of these files by Snowflake.
Step 4: Maintaining Data on Snowflake
Maintaining an accurate database on Snowflake is a never-ending battle; with every update to Google Analytics, older data on Snowflake must be analyzed and updated to ensure the integrity of the overarching data tables. This task is made somewhat easier by creating UPDATE statements in Snowflake, but you must also take care to identify and delete any duplicate records that appear in the database.
Overall, maintenance of your newly-created Snowflake database can be a time-consuming project, which is all the more reason to look for time-saving solutions such as LIKE.TG .
Limitations of Using Custom ETL Scripts to Connect Google Analytics to Snowflake
Although there are other methods of integrating data from Google Analytics to Snowflake, those not using LIKE.TG must be prepared to deal with a number of limitations:
Heavy Engineering Bandwidth: Building, testing, deploying, and maintaining the infrastructure necessary for proper data transfer requires a great deal of effort on the end user’s part.Not Automatic: Each time a change is made in Google Analytics, time must be taken to manually alter the code to ensure data integrity.Not Real-time: The steps as set out in this article must be performed every single time data is moved from Analytics to Snowflake. For most users, who will be moving data on a regular basis, following these steps every time will be a cumbersome, time-consuming ordeal.Possibility of Irretrievable Data Loss: If at any point during this process an error occurs say, something changes in Google Analytics API or on Snowflake, serious data corruption and loss can result.
Method 2: Using LIKE.TG Data to Move Data from Google Analytics to Snowflake
LIKE.TG is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.
Sign up here for a 14-Day Free Trial!
LIKE.TG takes care of all your data preprocessing to set up a connection from Google Analytics to Snowflake and lets you focus on key business activities and draw a much powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent reliable solution to manage data in real-time and always have analysis-ready data in your desired destination.
LIKE.TG being an official Snowflake partner, can connect Google Analytics to Snowflake in 2 simple steps:
Step 1: Connect LIKE.TG with Google Analytics 4 and all your data sources by simply logging in with your credentials.
Step 2: Configure the Snowflake destination by providing the details like Destination Name, Account Name, Account Region, Database User, Database Password, Database Schema, and Database Name.
LIKE.TG will now take care of all the heavy-weight lifting to move data from Google Analytics to Snowflake. Here are some of the benefits of LIKE.TG :
Reduced Time to Implementation: With a few clicks of a mouse, users can swiftly move their data from source to destination. This will drastically reduce time to insight and help your business make key decisions faster.End to End Management: The burden of overseeing the inessential minutiae of data migration is removed from the user, freeing them to make more efficient use of their time.A Robust System for Alerts and Notifications: LIKE.TG offers users a wide array of tools to ensure that changes and errors are detected and that the user is notified as to their presence.Complete, Consistent Data Transfer: Whereas some data migration solutions can lead to the loss of data as errors appear, LIKE.TG uses a proprietary staging mechanism to quarantine problematic data fields so that the user can fix errors on a case-to-case basis and move this data.Comprehensive Scalability: With LIKE.TG , it is no problem to incorporate new data sets, regardless of file size. In addition to Google Analytics, LIKE.TG is also able to interface with a number of other analytics, marketing, and cloud applications; LIKE.TG aims to be the one-source solution for all your data transfer needs.24/7 Support: LIKE.TG provides a team of product experts, ready to assist 24 hours a day, 7 days a week.
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Conclusion
For users who seek a more in-depth understanding of their web traffic, moving data from Google Analytics to their Snowflake data warehouse becomes an important feat.
However, sifting through this can be an arduous and time-intensive process, a process that a tool like LIKE.TG can streamline immensely, with no effort needed from the user’s end. Furthermore, LIKE.TG is compatible with a 100+ data sources, including 40+ Free Sources like Google Analytics allowing the user to interface with databases, cloud storage solutions, and more.
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Still not sure that LIKE.TG is right for you?
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Salesforce to BigQuery: 2 Easy Methods
Bringing your key sales, marketing and customer data from Salesforce to BigQuery is the right step towards building a robust analytics infrastructure. By merging this information with more data points available from various other data sources used by your business, you will be able to extract deep actionable insights that grow your business. Before we jump into the details, let us briefly understand each of these systems.Introduction to Salesforce
Salesforceis one of the world’s most renowned customer relationship management platforms. Salesforce comes with a wide range of features that allow you to manage your key accounts and sales pipelines.While Salesforce does provide analytics within the software, many businesses would want to extract this data, combine it with data from other sources such as marketing, product, and more to get deeper insights on the customer. By bringing the CRM data into a modern data warehouse like BigQuery, this can be achieved.
Key Features of Salesforce
Salesforce is one of the most popular CRM in the current business scenario and it is due to its various features. Some of these key features are:
Easy Setup:Unlike most CRMs, which usually take up to a year to completely get installed and deployed, Salesforce can be easily set up from scratch within few weeks only.
Ease of Use:Businesses usually have to spend more time putting it to use and comparatively much lesser time in understanding how Salesforce works.
Effective:Salesforce is convenient to use and can also be customized by businesses to meet their requirements. Due to this feature, users find the tool very beneficial.
Account Planning: Salesforce provides you with enough data about each Lead that your Sales Team can customize their approach for every potential Lead. This will increase their chance of success and the customer will also get a personalized experience.
Accessibility: Salesforce is a Cloud-based software, hence it is accessible from any remote location if you have an internet connection. Moreover, Salesforce has an application for mobile phones which makes it super convenient to use.
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LIKE.TG ’s no-code data pipeline platform lets you connect over 150+ sources in a matter of minutes to deliver data in near real-time to your warehouse. LIKE.TG is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines that are flexible to your needs.
All of this combined with transparent pricing and 24×7 support makes us the most loved data pipeline software in terms of user reviews.
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Introduction to Google BigQuery
Google BigQuery is a completely managed cloud data warehouse platform offered by Google. It is based on Google’s famous Dremel Engine. Since BigQuery is based on a serverless model, it provides you high level of abstraction. Since it is a completely managed warehouse, companies do not need to maintain any form of physical infrastructure and database administrators. BigQuery comes with a pay-as-you-go pricing model and allows to pay only for the queries run. is also very cost-effective as you only pay for the queries you run. These features together make BigQuery a very sought after data warehouse platform. You can read more about the key features of BigQuery here.
This blog covers two methods of loading data from Salesforce to Google BigQuery. The article also sheds light on the advantages/disadvantages of both approaches. This would give you enough pointers to evaluate them based on your use case and choose the right direction.
Methods to Connect Salesforce to BigQuery
There are several approaches to migrate Salesforce data to BigQuery. Salesforce bigquery connector is commonly integrated for the purpose of analyzing and visualizing Salesforce data in a BigQuery environment. Let us look at both the approaches to connect Salesforce to BigQuery in a little more detail:
Method 1: Move data from Salesforce to Google BigQuery using LIKE.TG
LIKE.TG , a No-code Data Pipeline, helps you directly transfer data from Salesforce and150+ other data sourcesto Data Warehouses such as BigQuery, Databases, BI tools, or a destination of your choice in a completely hassle-free automated manner.
Get Started with LIKE.TG for free
LIKE.TG Data takes care of all your data preprocessing needs and lets you focus on key business activities and draw a much powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent reliable solution to manage data in real-time and always have analysis-ready data in your desired destination.
LIKE.TG can integrate data from Salesforce to BigQuery in just 2 simple steps:
Step 1: Authenticate and configure your Salesforce data sourceas shown in the below image. To learn more about this step, visithere.
Learn more about configuring Salesforce from our documentation page.
To configure Salesforce as a source, perform the following steps;
Go to the Navigation Bar and click on the Pipelines.
In the Pipelines List View, click the + CREATE button.
Select Salesforce on the Select Source Type page.
Specify the necessary information in the Configure your Salesforce account page
Click on the Continue button to complete the source setup and proceed to configuring data ingestion and setting up the destination.
Step 2: CompleteSalesforce to BigQueryMigration by providing information about your Google BigQuery destination such as the authorized Email Address, Project ID, etc.
To configure Google BigQuery as your Destination, follow the steps;
Go to the Navigation Bar, and click the Destinations button.
In the Destinations List View, click the + CREATE button.
On the Add Destination page, select Google BigQuery as the Destination type
Specify the necessary information in the Configure your Google BigQuery warehouse page
Click on the Save Continue buttons to complete the destination setup.
Learn more about configuring BigQuery as destination here.
LIKE.TG ’s visual interface gives you a hassle-free means to quickly and easily migrate from Salesforce to BigQuery and also for free. Without any coding, all your Salesforce data will be ready for analysis within minutes.
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Method 2: Move Data from Salesforce to BigQuery using Custom Scripts
The first step would be to decide what data you need to extract from the API and Salesforce has an abundance of APIs.
Salesforce REST APIs
Salesforce Bulk APIs
Salesforce SOAP APIs
Salesforce Streaming APIsYou may want to use Salesforce’s streaming API, so your data is always current.
Transform your data:Once you have extracted the data using one of the above approaches, you would need to do the following:
BigQuery supports loading data in CSV and JSON formats. If the API you use returns data in formats other than these (eg: XML), you would need to transform them before loading.
You also need to make sure your data types are supported by BIgQuery. Use this link BigQuery data types to learn more about BigQuery data types.
Upload the prepared data to Google Cloud Storage
Load to BigQuery from your GCS bucket using BigQuery’s command-line tool or any cloud SDK.
Salesforce to BigQuery:Limitations in writing Custom Code
When writing and managing API scripts you need to have the resources for coding, code reviews, test deployments, and documentation.
Depending on the use cases developed by your organization, you may need to amend API scripts; change the schema in your data warehouse; and make sure data types in source and destination match.
You will also need to have a system for data validation. This would help you be at peace that data is being moved reliably.
Each of these steps can make a substantial investment of time and resources. In today’s work setting, there are very few places with ‘standby’ resources that can take up the slack when major projects need more attention.
In addition to the above, you would also need to:
Watch out for Salesforce API for changes
Monitor GCS/BigQuery for changes and outages
Retain skilled people to rewrite or update the code as needed
If all of this seems like a crushing workload you could look at alternatives like LIKE.TG . LIKE.TG frees you from inspecting data flows, examining data quality, and rewriting data-streaming APIs. LIKE.TG gives you analysis-ready data so you can spend your time getting real-time business insights.
Method 3: Using CSV/Avro
This method involves using CSV/Avro files to export data from Salesforce into BigQuery. The steps are:
Inside your Salesforce data explorer panel, select the table that you want to export your data.
Click on ‘Export to Cloud Storage’ and select CSV as the file type. Then, select the compression type as GZIP (GNU Zip) or go ahead with the default value.
Download that file to the system.
Login to your BigQuery account. In the Data Explorer section, select “import” and choose “Batch Ingestion”.
Choose the file type as CSV/Avro. You can enable schema auto-detection or specify it specifically.
Add dataset and table name, and select “Import”.
The limitation of the method is that it becomes complex if you have multiple tables/files to import. Same goes for more than one data sources with constantly varying data.
Use cases for Migrating Salesforce to BigQuery
Organizations use Salesforce’s Data Cloud along with Google’s BigQuery and Vertex AI to enhance their customer experiences and tailor interactions with them. Salesforce BigQuery Integration enables organizations to combine and analyze data from their Salesforce CRM system with the powerful data processing capabilities of BigQuery. Let’s understand some real-time use cases for migrating salesforce to bigquery.
Retail: Retail businesses can integrate CRM data with non-CRM data such as real-time online activity and social media sentiment in BigQuery to help you understand the complete customer journey and subsequently when you implement customized AI models to forecast customer tendency. The outcome involves delivering highly personalized recommendations to customers through optimal channels like email, mobile apps, or social media.
Healthcare Organizations: CRM data, including appointment history and patient feedback, can be integrated with non-CRM data, such as patient demographics and medical history in BigQuery. The outcome is the prediction of patients who are susceptible to readmission, allowing for the creation of personalized care plans. This proactive approach enhances medical outcomes through preemptive medical care.
Financial institutions: Financial institutions have the capability to integrate CRM data encompassing a customer’s transaction history, credit score, and financial goals with non-CRM data such as market analysis and economic trends. By utilizing BigQuery, these institutions can forecast customers’ spending patterns, investment preferences, and financial goals. This valuable insight informs the provision of personalized banking services and offers tailored to individual customer needs.
Use cases for migrating Salesforce to BigQuery
Organizations use Salesforce’s Data Cloud along with Google’s BigQuery and Vertex AI to enhance their customer experiences and tailor interactions with them. Salesforce BigQuery Integration enables organizations to combine and analyze data from their Salesforce CRM system with the powerful data processing capabilities of BigQuery. Let’s understand some real-time use cases for migrating salesforce to bigquery.
Retail: Retail businesses can integrate CRM data with non-CRM data such as real-time online activity and social media sentiment in BigQuery to help you understand the complete customer journey and subsequently when you implement customized AI models to forecast customer tendency. The outcome involves delivering highly personalized recommendations to customers through optimal channels like email, mobile apps, or social media.
Healthcare Organizations: CRM data, including appointment history and patient feedback, can be integrated with non-CRM data, such as patient demographics and medical history in BigQuery. The outcome is the prediction of patients who are susceptible to readmission, allowing for the creation of personalized care plans. This proactive approach enhances medical outcomes through preemptive medical care.
Financial institutions: Financial institutions have the capability to integrate CRM data encompassing a customer’s transaction history, credit score, and financial goals with non-CRM data such as market analysis and economic trends. By utilizing BigQuery, these institutions can forecast customers’ spending patterns, investment preferences, and financial goals. This valuable insight informs the provision of personalized banking services and offers tailored to individual customer needs.
Conclusion
The blog talks about the two methods you can use to move data from Salesforce to BigQuery in a seamless fashion. The idea of custom coding with its implicit control over the entire data-transfer process is always attractive. However, it is also a huge resource load for any organization.
A practical alternative is LIKE.TG – a fault-tolerant, reliable Data Integration Platform. LIKE.TG gives you an environment free from any hassles, where you can securely move data from any source to any destination.
See how easy it is to migrate data from Salesforce to BigQuery and that too for free.
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Want to take LIKE.TG for a spin? Sign Up for a 14-day free trial and experience the feature-rich LIKE.TG suite first hand. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.
MariaDB to Snowflake: 2 Easy Methods to Move Data in Minutes
Are you looking to move data from MariaDB to Snowflake for Analytics or Archival purposes? You have landed on the right post. This post covers two main approaches to move data from MariaDB to Snowflake. It also discusses some limitations of the manual approach. So, to overcome these limitations, you will be introduced to an easier alternative to migrate your data from MariaDB to Snowflake. How to Move Data from MariaDB to Snowflake?
Method 1: Implement an Official Snowflake ETL Partner such asLIKE.TG Data.
LIKE.TG is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines that are flexible to your needs. With integration with 150+ Data Sources (40+ free sources), we help you not only export data from sources load data to the destinations but also transform enrich your data, make it analysis-ready.
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Method 2: Build Custom ETL Scripts to move data from MariaDB to Snowflake
Organizations can enable scalable analytics, reporting, and machine learning on their valuable MariaDB data by customizing ETL scripts to integrate MariaDB transactional data seamlessly into Snowflake’s cloud data warehouse. However, the custom method can be challenging, which we will discuss later in the blog.
Method 1: MariaDB to Snowflake using LIKE.TG
Using a no-code data integration solution likeLIKE.TG (Official Snowflake ETL Partner), you can move data from MariaDB to Snowflake in real time. Since LIKE.TG is fully managed, the setup and implementation time is next to nothing. You can replicate MariaDB to Snowflake using LIKE.TG ’s visual interface in 2 simple steps:
Step 1: Connect to your MariaDB Database
ClickPIPELINESin theAsset Palette.
Click+ CREATEin thePipelines List View.
In theSelect Source Typepage, select MariaDB as your source.
In theConfigure yourMariaDBSourcepage, specify the following:
Step 2: Configure Snowflake as your Destination
ClickDESTINATIONSin theNavigation Bar.
Click+ CREATEin theDestinations List View.
In theAdd Destinationpage, selectSnowflakeas the Destination type.
In theConfigure yourSnowflake Warehousepage, specify the following:
To know more about MariabDB to Snowflake Integration, refer to LIKE.TG documentation:
MariaDB Source Connector
Snowflake as a Destination
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Method 2: Build Custom ETL Scripts to move data from MariaDB to Snowflake
Implementing MariaDB to Snowflake integration streamlines data flow and analysis, enhancing overall data management and reporting capabilities. At a high level, the data replication process can generally be thought of in the following steps:
Step 1: Extracting Data from MariaDB
Step 2: Data Type Mapping and Preparation
Step 3: Data Staging
Step 4: Loading Data into Snowflake
Step 1: Extracting Data from MariaDB
Data should be extracted based on the use case and the size of the data being exported.
If the data is relatively small, then it can be extracted using SQL SELECT statements into MariaDB’s MySQL command-line client.
Example:
mysql -u <name> -p <db> SELECT <columns> INTO OUTFILE 'path' FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY 'n' FROM <table>;
With the FIELDS TERMINATED BY, OPTIONALLY ENCLOSED BY and LINES TERMINATED BY clauses being optional
If a user is looking to export large amounts of data, then MariaDB provides another command-line tool mysqldump which is better suited to export tables, a database or databases into other database servers. mysqldump creates a backup by dumping database or table information into a text file, which is typically in SQL. However, it can also generate files in other formats like CSV or XML.A use case extracting a full backup of a database is shown below:
mysqldump -h [database host's name or IP address] -u [the database user's name] -p [the database name] > db_backup.sql
The resulting file will consist of SQL statements that will create the database specified above.
Example (snippet):
CREATE TABLE table1 ( ‘Column1’ bigint(10)....... )
Step 2: Data Type Mapping and Preparation
Once the data is exported, one has to ensure that the data types in the MariaDB export properly correlate with their corresponding data types in Snowflake.
Snowflake presents documentation on data preparation before the Staging process here.
In general, it should be noted that the BIT data type in MariaDB corresponds to the BOOLEAN in Snowflake. Also, Large Object types (both BLOB and CLOB) and ENUM are not supported in Snowflake. The complete documentation on the data types that are not supported by Snowflake can be found here.
Step 3: Data Staging
The data is ready to be imported into the Staging area after we have ensured that the data types are accurately mapped.
There are two types of stages that a user can create in Snowflake. These are:
Internal Stages
External Stages
Each of these stages can be created using the Snowflake GUI or with SQL code. For the scope of this blog, we have included the steps to do this using SQL code.
Loading Data to Internal Stage:
CREATE [ OR REPLACE ] [ TEMPORARY ] STAGE [ IF NOT EXISTS ] <internal_stage_name> [ FILE_FORMAT = ( { FORMAT_NAME = '<file_format_name>' | TYPE = { CSV | JSON | AVRO | ORC | PARQUET | XML } [ formatTypeOptions ] ) } ] [ COPY_OPTIONS = ( copyOptions ) ] [ COMMENT = '<string_literal>' ]
Loading Data to External Stage:
Here is the code to load data to Amazon S3:
CREATE STAGE “[Database Name]”, “[Schema]”,”[Stage Name]” URL=’S3://<URL> CREDENTIALS= (AWS_KEY_ID=<your AWS key ID>, AWS_SECRET_KEY= <your AWS secret key>) ENCRYPTION= (MASTER_KEY=<Master key if required>) COMMENT= ‘[insert comment]’
In case you are using Microsoft Azure as your external stage, here is how you can load data:
CREATE STAGE “[Database Name]”, “[Schema]”,”[Stage Name]” URL=’azure://<URL> CREDENTIALS= (AZURE_SAS_TOKEN=’< your token>‘) ENCRYPTION= (TYPE = “AZURE_CSE, MASTER_KEY=<Master key if required>) COMMENT= ‘[insert comment]’
There are other internal stage types namely the table stage and the user stage. However, these stages are automatically generated by Snowflake. The table stage is held within a table object and is best used for use cases that require the staged data to be only used exclusively for a specific table. The user table is assigned to each user by the system and cannot be altered or dropped. They are used as personal storage locations for users.
Step 4: Loading Data to Snowflake
In order to load the staged data to Snowflake, we use the COPY INTO DML statement through Snowflake’s SQL command-line interface – SnowSQL. Note that using the FROM clause in the COPY INTO statement is optional, as Snowflake will automatically check for files in the stage. You can connect MariaDB to Snowflake to provide smooth data integration, enabling effective data analysis and transfer between the two databases.
Loading Data from Internal Stages:
User Stage Type:
COPY INTO TABLE1 FROM @~/staged file_format=(format_name=’csv_format’)
Table Stage Type:
COPY INTO TABLE1 FILE_FORMAT=(TYPE CSV FIELD DELIMITER=’|’ SKIP_HEADER=1)
Internal Stage Created as per the previous step:
COPY INTO TABLE1 FROM @Stage_name
Amazon S3:
While you can load data directly from an Amazon S3 bucket, the recommended method is to first create an Amazon S3 external stage as described under the Data Stage section of this guide. The same applies to Microsoft Azure and GCP buckets too.
COPY INTO TABLE1 FROM s3://bucket CREDENTIALS= (AWS_KEY_ID='YOUR AWS ACCESS KEY' AWS_SECRET_KEY='YOUR AWS SECRET ACCESS KEY') ENCRYPTION= (MASTER_KEY = 'YOUR MASTER KEY') FILE_FORMAT = (FORMAT_NAME = CSV_FORMAT)
Microsoft Azure:
COPY INTO TABLE1 FROM azure://your account.blob.core.windows.net/container STORAGE_INTEGRATION=(Integration_name) ENCRYPTION= (MASTER_KEY = 'YOUR MASTER KEY') FILE_FORMAT = (FORMAT_NAME = CSV_FORMAT)
GCS:
COPY INTO TABLE1 FROM 'gcs://bucket’ STORAGE_INTEGRATION=(Integration_name) ENCRYPTION= (MASTER_KEY = 'YOUR MASTER KEY') FILE_FORMAT = (FORMAT_NAME = CSV_FORMAT)
Loading Data from External Stages:
Snowflake offers and supports many format options for data types like Parquet, XML, JSON, and CSV. Additional information can be found here.
This completes the steps to load data from MariaDB to Snowflake. The MariaDB Snowflake integration facilitates a smooth and efficient data exchange between the two databases, optimizing data processing and analysis.
While the method may look fairly straightforward, it is not without its limitations.
Limitations of Moving Data from MariaDB to Snowflake Using Custom Code
Significant Manual Overhead: Using custom code to move data from MariaDB to Snowflake necessitates a high level of technical proficiency and physical labor. The process becomes more labor- and time-intensive as a result.
Limited Real-Time Capabilities: Real-time data loading capabilities are absent from the custom code technique when transferring data from MariaDB to Snowflake. It is, therefore, inappropriate for companies that need the most recent data updates.
Limited Scalability: The custom code solution may not be scalable for future expansion as data quantities rise, and it may not be able to meet the increasing needs in an effective manner.
So, you can use an easier alternative: LIKE.TG Data – Simple to use Data Integration Platform that can mask the above limitations and move data from MariaDB to Snowflake instantly.
There are a number of interesting use cases for moving data from MariaDB to Snowflake that might yield big advantages for your company. Here are a few important situations in which this integration excels:
Improved Reporting and Analytics:
Quicker and more effective data analysis: Large datasets can be queried incredibly quickly using Snowflake’s columnar storage and cloud-native architecture—even with datasets that MariaDB had previously been thought to be too sluggish for.
Combine data from various sources with MariaDB: For thorough analysis, you may quickly and easily link your MariaDB data with information from other sources in Snowflake, such as cloud storage, SaaS apps, and data warehouses.
Enhanced Elasticity and Scalability:
Scaling at a low cost: You can easily scale computing resources up or down according on your data volume and query demands using Snowflake’s pay-per-use approach, which eliminates the need to overprovision MariaDB infrastructure.
Manage huge and expanding datasets: Unlike MariaDB, which may have scaling issues, Snowflake easily manages big and expanding datasets without causing performance reduction.
Streamlined Data Management and Governance:
Centralized data platform: For better data management and governance, combine your data from several sources—including MariaDB—into a single, cohesive platform with Snowflake.
Enhanced compliance and data security: Take advantage of Snowflake’s strong security features and compliance certifications to guarantee your sensitive data is private and protected.
Simplified data access and sharing: Facilitate safe data exchange and granular access control inside your company to promote teamwork and data-driven decision making.
Conclusion
In this post, you were introduced to MariaDB and Snowflake. Moreover, you learned the steps to migrate your data from MariaDB to Snowflake using custom code. You observed certain limitations associated with this method. Hence, you were introduced to an easier alternative – LIKE.TG to load your data from MariaDB to Snowflake.
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LIKE.TG moves your MariaDB data to Snowflake in a consistent, secure and reliable fashion. In addition to MariaDB, LIKE.TG can load data from a multitude of other data sources including Databases, Cloud Applications, SDKs, and more. This allows you to scale up on demand and start moving data from all the applications important for your business.
Want to take LIKE.TG for a spin?
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Share your experience of loading data from MariaDB to Snowflake in the comments section below!
MongoDB to Redshift ETL: 2 Easy Methods
If you are looking to move data from MongoDB to Redshift, I reckon that you are trying to upgrade your analytics set up to a modern data stack. Great move!Kudos to you for taking up this mammoth of a task! In this blog, I have tried to share my two cents on how to make the data migration from MongoDB to Redshift easier for you.
Before we jump to the details, I feel it is important to understand a little bit on the nuances of how MongoDB and Redshift operate. This will ensure you understand the technical nuances that might be involved in MongoDB to Redshift ETL. In case you are already an expert at this, feel free to skim through these sections or skip them entirely.
What is MongoDB?
MongoDB distinguishes itself as a NoSQL database program. It uses JSON-like documents along with optional schemas. MongoDB is written in C++. MongoDB allows you to address a diverse set of data sets, accelerate development, and adapt quickly to change with key functionalities like horizontal scaling and automatic failover.
MondoDB is a best RDBMS when you have a huge data volume of structured and unstructured data. It’s features make scaling and flexibility smooth. These are available for data integration, load balancing, ad-hoc queries, sharding, indexing, etc.
Another advantage is that MongoDB also supports all common operating systems (Linux, macOS, and Windows). It also supports C, C++, Go, Node.js, Python, and PHP.
What is Amazon Redshift?
Amazon Redshift is essentially a storage system that allows companies to store petabytes of data across easily accessible “Clusters” that you can query in parallel. Every Amazon Redshift Data Warehouse is fully managed which means that the administrative tasks like maintenance backups, configuration, and security are completely automated.
Suppose, you are a data practitioner who wants to use Amazon Redshift to work with Big Data. It will make your work easily scalable due to its modular node design. It also us you to gain more granular insight into datasets, owing to the ability of Amazon Redshift Clusters to be further divided into slices. Amazon Redshift’s multi-layered architecture allows multiple queries to be processed simultaneously thus cutting down on waiting times. Apart from these, there are a few more benefits of Amazon Redshift you can unlock with the best practices in place.
Main Features of Amazon Redshift
When you submit a query, Redshift cross checks the result cache for a valid and cached copy of the query result. When it finds a match in the result cache, the query is not executed. On the other hand, it uses a cached result to reduce runtime of the query.
You can use the Massive Parallel Processing (MPP) feature for writing the most complicated queries when dealing with large volume of data.
Your data is stored in columnar format in Redshift tables. Therefore, the number of disk I/O requests to optimize analytical query performance is reduced.
Why perform MongoDB to Redshift ETL?
It is necessary to bring MongoDB’s data to a relational format data warehouse like AWS Redshift to perform analytical queries. It is simple and cost-effective to efficiently analyze all your data by using a real-time data pipeline. MongoDB is document-oriented and uses JSON-like documents to store data.
MongoDB doesn’t enforce schema restrictions while storing data, the application developers can quickly change the schema, add new fields and forget about older ones that are not used anymore without worrying about tedious schema migrations. Owing to the schema-less nature of a MongoDB collection, converting data into a relational format is a non-trivial problem for you.
In my experience in helping customers set up their modern data stack, I have seen MongoDB be a particularly tricky database to run analytics on. Hence, I have also suggested an easier / alternative approach that can help make your journey simpler.
In this blog, I will talk about the two different methods you can use to set up a connection from MongoDB to Redshift in a seamless fashion: Using Custom ETL Scripts and with the help of a third-party tool, LIKE.TG .
What Are the Methods to Move Data from MongoDB to Redshift?
These are the methods we can use to move data from MongoDB to Redshift in a seamless fashion:
Method 1: Using Custom Scripts to Move Data from MongoDB to Redshift
Method 2: Using an Automated Data Pipeline Platform to Move Data from MongoDB to Redshift
Integrate MongoDB to RedshiftGet a DemoTry it
Method 1: Using Custom Scripts to Move Data from MongoDB to Redshift
Following are the steps we can use to move data from MongoDB to Redshift using Custom Script:
Step 1: Use mongoexport to export data.
mongoexport --collection=collection_name --db=db_name --out=outputfile.csv
Step 2: Upload the .json file to the S3 bucket.2.1: Since MongoDB allows for varied schema, it might be challenging to comprehend a collection and produce an Amazon Redshift table that works with it. For this reason, before uploading the file to the S3 bucket, you need to create a table structure.2.2: Installing the AWS CLI will also allow you to upload files from your local computer to S3. File uploading to the S3 bucket is simple with the help of the AWS CLI. To upload.csv files to the S3 bucket, use the command below if you have previously installed the AWS CLI. You may use the command prompt to generate a table schema after transferring.csv files into the S3 bucket.
AWS S3 CP D:\outputfile.csv S3://S3bucket01/outputfile.csv
Step 3: Create a Table schema before loading the data into Redshift.
Step 4: Using the COPY command load the data from S3 to Redshift.Use the following COPY command to transfer files from the S3 bucket to Redshift if you’re following Step 2 (2.1).
COPY table_name
from 's3://S3bucket_name/table_name-csv.tbl'
'aws_iam_role=arn:aws:iam::<aws-account-id>:role/<role-name>'
csv;
Use the COPY command to transfer files from the S3 bucket to Redshift if you’re following Step 2 (2.2). Add csv to the end of your COPY command in order to load files in CSV format.
COPY db_name.table_name
FROM ‘S3://S3bucket_name/outputfile.csv’
'aws_iam_role=arn:aws:iam::<aws-account-id>:role/<role-name>'
csv;
We have successfully completed MongoDB Redshift integration.
For the scope of this article, we have highlighted the challenges faced while migrating data from MongoDB to Amazon Redshift. Towards the end of the article, a detailed list of advantages of using approach 2 is also given. You can check out Method 1 on our other blog and know the detailed steps to migrate MongoDB to Amazon Redshift.
Limitations of using Custom Scripts to Move Data from MongoDB to Redshift
Here is a list of limitations of using the manual method of moving data from MongoDB to Redshift:
Schema Detection Cannot be Done Upfront: Unlike a relational database, a MongoDB collection doesn’t have a predefined schema. Hence, it is impossible to look at a collection and create a compatible table in Redshift upfront.
Different Documents in a Single Collection: Different documents in single collection can have a different set of fields. A document in a collection in MongoDB can have a different set of fields.
{
"name": "John Doe",
"age": 32,
"gender": "Male"
}
{
"first_name": "John",
"last_name": "Doe",
"age": 32,
"gender": "Male"
}
Different documents in a single collection can have incompatible field data types. Hence, the schema of the collection cannot be determined by reading one or a few documents.
2 documents in a single MongoDB collection can have fields with values of different types.
{
"name": "John Doe",
"age": 32,
"gender": "Male"
"mobile": "(424) 226-6998"
}
{
"name": "John Doe",
"age": 32,
"gender": "Male",
"mobile": 4242266998
}
The fieldmobile is a string and a number in the above documents respectively. It is a completely valid state in MongoDB. In Redshift, however, both these values either will have to be converted to a string or a number before being persisted.
New Fields can be added to a Document at Any Point in Time: It is possible to add columns to a document in MongoDB by running a simple update to the document. In Redshift, however, the process is harder as you have to construct and run ALTER statements each time a new field is detected.
Character Lengths of String Columns: MongoDB doesn’t put a limit on the length of the string columns. It has a 16MB limit on the size of the entire document. However, in Redshift, it is a common practice to restrict string columns to a certain maximum length for better space utilization. Hence, each time you encounter a longer value than expected, you will have to resize the column.
Nested Objects and Arrays in a Document: A document can have nested objects and arrays with a dynamic structure. The most complex of MongoDB ETL problems is handling nested objects and arrays.
{
"name": "John Doe",
"age": 32,
"gender": "Male",
"address": {
"street": "1390 Market St",
"city": "San Francisco",
"state": "CA"
},
"groups": ["Sports", "Technology"]
}
MongoDB allows nesting objects and arrays to several levels. In a complex real-life scenario is may become a nightmare trying to flatten such documents into rows for a Redshift table.
Data Type Incompatibility between MongoDB and Redshift: Not all data types of MongoDB are compatible with Redshift. ObjectId, Regular Expression, Javascript are not supported by Redshift. While building an ETL solution to migrate data from MongoDB to Redshift from scratch, you will have to write custom code to handle these data types.
Method 2: Using Third Pary ETL Tools to Move Data from MongoDB to Redshift
White using the manual approach works well, but using an automated data pipeline tool like LIKE.TG can save you time, resources and costs. LIKE.TG Data is a No-code Data Pipeline platform that can help load data from any data source, such as databases, SaaS applications, cloud storage, SDKs, and streaming services to a destination of your choice. Here’s how LIKE.TG overcomes the challenges faced in the manual approach for MongoDB to Redshift ETL:
Dynamic expansion for Varchar Columns: LIKE.TG expands the existing varchar columns in Redshift dynamically as and when it encounters longer string values. This ensures that your Redshift space is used wisely without you breaking a sweat.
Splitting Nested Documents with Transformations: LIKE.TG lets you split the nested MongoDB documents into multiple rows in Redshift by writing simple Python transformations. This makes MongoDB file flattening a cakewalk for users.
Automatic Conversion to Redshift Data Types: LIKE.TG converts all MongoDB data types to the closest compatible data type in Redshift. This eliminates the need to write custom scripts to maintain each data type, in turn, making the migration of data from MongoDB to Redshift seamless.
Here are the steps involved in the process for you:
Step 1: Configure Your Source
Load Data from LIKE.TG to MongoDB by entering details like Database Port, Database Host, Database User, Database Password, Pipeline Name, Connection URI, and the connection settings.
Step 2: Intgerate Data
Load data from MongoDB to Redshift by providing your Redshift databases credentials like Database Port, Username, Password, Name, Schema, and Cluster Identifier along with the Destination Name.
LIKE.TG supports 150+ data sources including MongoDB and destinations like Redshift, Snowflake, BigQuery and much more. LIKE.TG ’s fault-tolerant and scalable architecture ensures that the data is handled in a secure, consistent manner with zero data loss.
Give LIKE.TG a try and you can seamlessly export MongoDB to Redshift in minutes.
GET STARTED WITH LIKE.TG FOR FREE
For detailed information on how you can use the LIKE.TG connectors for MongoDB to Redshift ETL, check out:
MongoDB Source Connector
Redshift Destination Connector
Additional Resources for MongoDB Integrations and Migrations
Stream data from mongoDB Atlas to BigQuery
Move Data from MongoDB to MySQL
Connect MongoDB to Snowflake
Connect MongoDB to Tableau
Conclusion
In this blog, I have talked about the 2 different methods you can use to set up a connection from MongoDB to Redshift in a seamless fashion: Using Custom ETL Scripts and with the help of a third-party tool, LIKE.TG .
Outside of the benefits offered by LIKE.TG , you can use LIKE.TG to migrate data from an array of different sources – databases, cloud applications, SDKs, and more. This will provide the flexibility to instantly replicate data from any source like MongoDB to Redshift.
More related reads:
Creating a table in Redshift
Redshift functions
You can additionally model your data, build complex aggregates and joins to create materialized views for faster query executions on Redshift. You can define the interdependencies between various models through a drag and drop interface with LIKE.TG ’s Workflows to convert MongoDB data to Redshift.
Amazon Aurora to BigQuery: 2 Easy Methods
In this day businesses are generating a huge amount of data regularly. To make important decisions this raw data is very essential. However, there are a few major challenges in the process. It is very difficult to analyze such a huge amount of data (Petabyte) using a traditional database like MySQL, Oracle, SQL Server, etc. In order to get any tangible insight from this data, you would need to move data to Data Warehouse like Google BigQuery. This post provides a step-by-step walkthrough on how to migrate data from Amazon Aurora to the BigQuery Data warehouse using 2 steps. Read along and decide which method suits you the best!Performing ETL from Amazon Aurora to BigQuery
Method 1: Using Custom Code to Move Data from Aurora to BigQuery
This method consists of a 5-step process to move data from Amazon Aurora to BigQuery through custom ETL Scripts. There are various advantages of using this method but a few limitations as well.
Method 2: Using LIKE.TG Data to Move Data from Aurora to BigQuery
LIKE.TG Data can load your data fromAurora to BigQueryin minutes without writing a single line of code and forfree. Data loading can be configured on a visual, point, and click interface. Since LIKE.TG is fully managed, you would not have to invest any additional time and resource in maintaining and monitoring the data. LIKE.TG promises 100% data consistency and accuracy.
Sign up here for a 14-day Free Trial!
Methods to Connect Aurora to BigQuery
Here are the methods you can use to connect Aurora to BigQuery in a seamless fashion:
Method 1: Using Custom Code to Move Data from Aurora to BigQuery
Method 2: Using LIKE.TG Data to Move Data from Aurora to BigQuery
In this post, we will cover the second method (Custom Code) in detail. Towards the end of the post, you can also find a quick comparison of both data replication methods so that you can evaluate your requirements and choose wisely.
Method 1: Using Custom Code to Move Data from Aurora to BigQuery
This method requires you to manually set up the data transfer process from Aurora to BigQuery. The steps involved in migrating data from Aurora DB to BigQuery are as follows:
Step 1: Getting Data out of Amazon Aurora
Step 2: Preparing Amazon Aurora Data
Step 3: Upload Data to Google Cloud Storage
Step 4: Upload to BigQuery from GCS
Step 5: Update the Target Table in BigQuery
Step 1: Getting Data out of Amazon Aurora
By writing SQL queries we can export data from Aurora. TheSELECT queries enable us to pull the data we want. You can specify filters and order of the data. You can also limit results.
A command-line tool called mysqldump lets you export entire tables and databases in a format you specify (i.e. delimited text, CSV, or SQL queries).
mysql -u user_name -p --database=db_name --host=rds_hostname --port=rdsport --batch -e "select * from table_name" | sed 's/t/","/g;s/^/"/;s/$/"/;s/n//g' > file_name
Step 2: Preparing Amazon Aurora Data
You need to make sure the target BigQuery table is perfectly aligned with the source Aurora table, specifically column sequence and data type of columns.
Step 3: Upload Data to Google Cloud Storage
You can use the bq command-line tool to upload the files to your datasets, adding schema and data type information. In GCP quickstart guide you can find the syntax of bq command line. Iterate through this process as many times as it takes to load all of your tables into BigQuery.
Once the data has been extracted from the Aurora database the next step is to upload it to the GCS There are multiple ways this can be achieved. The various methods are explained below.
(A) Using Gsutil
The gsutil utility will help us upload a local file to GCS(Google Cloud Storage) bucket.
To copy a file to GCS:
gsutil cp local_copy.csv gs://gcs_bucket_name/path/to/folder/
To copy an entire folder to GCS:
gsutil local_dir_name -r dir gs://gcs_bucket_name/path/to/parent_folder/
(B) Using Web console
An alternative means to upload the data from your local machine to GCS is using the web console. To use the web console alternative, follow the steps laid out below:
1. First of all, you need to Login to your GCP account. You ought to have a working Google account to make use of GCP. In the menu option, click on storage and navigate to the browser on the left tab
2. Create a new bucket to upload your data. Make sure the name you choose is globally unique
3. Click on the bucket name that you have created in step 2, this will ask to you browse the file from your local machine
4. Choose the file and click on the upload button. Once you see a progress bar wait for the action to be completed. You can see the file is loaded in the bucket.
Step 4: Upload to BigQuery from GCS
You can upload data to BigQuery from GCS using two methods: (A) Using console UI (B) Using the command line
(A) Uploading the data using the web console UI:
1. Go to the BigQuery from the menu option
2. On UI click on create a dataset, provide dataset name and location
3. Then click on the name of created dataset name. Click on create table option and provide the dataset name, table name, project name, table type.
(B) Using data using the command line
To open the command-line tool, on the GCS home page click on the cloud shell icon shown below:
The Syntax of the bq command line to load the file in the BigQuery table:
bq --location=[LOCATION] load --source_format=[FORMAT] [DATASET].[TABLE]
[PATH_TO_SOURCE] [SCHEMA]
[LOCATION] is an optional parameter that represents Location name like “us-east”
[FORMAT] to load CSV file set it to CSV
[DATASET] dataset name.
[TABLE] table name to load the data.
[PATH_TO_SOURCE] path to source file present on the GCS bucket.
[SCHEMA] Specify the schema
Note: Autodetect flag recognizes the table schema
You can specify your schema using bq command line:
bq --location=US load --source_format=CSV your_dataset.your_table gs://your_bucket/your_data.csv ./your_schema.json
Your target table schema can also be autodetected:
bq --location=US load --autodetect --source_format=CSV your_dataset.your_table gs://mybucket/data.cs
BigQuery command line interface offers us to 3 options to write to an existing table.
Overwrite the tablebq --location = US load --autodetect --replace --source_file_format = CSV your_target_dataset_name.your_target_table_name gs://source_bucket_name/path/to/file/source_file_name.csv
Append data to the table bq --location = US load --autodetect --noreplace --source_file_format = CSV your_target_dataset_name.your_table_table_name gs://source_bucket_name/path/to/file/source_file_name.csv ./schema_file.json
Adding new fields in the target table bq --location = US load --noreplace --schema_update_option = ALLOW_FIELD_ADDITION --source_file_format = CSV your_target_dataset.your_target_table gs://bucket_name/source_data.csv ./target_schema.json
Step 5: Update the Target Table in BigQuery
The data that was matched in the above-mentioned steps have not done complete data updates on the target table. The data is stored in an intermediate data table, this is because GCS is a staging area for BigQuery upload. Hence, the data is stored in an intermediate table before been uploaded to BigQuery
There two ways of updating the final table as explained below:
Update the rows in the final table, Then insert new rows from the intermediate tableUPDATE target_table t SET t.value = s.value FROM intermediate_table s WHERE t.id = s.id; INSERT target_table (id, value) SELECT id, value FROM intermediate_table WHERE NOT id IN (SELECT id FROM target_table);
Delete all the rows from the final table which are in the intermediate table, Then insert all the rows newly loaded in the intermediate table. Here the intermediate table will be in truncate and load mode DELETE FROM final_table f WHERE f.id IN (SELECT id from intermediate_table); INSERT data_setname.target_table(id, value) SELECT id, value FROM data_set_name.intermediate_table;
That’s it! Your Amazon Aurora to Google BigQuery data transfer process is complete.
Limitations of using Custom Code to Move Data from Aurora to BigQuery
The manual approach will allow you to move your data from Amazon Aurora to BigQuery successfully, however it suffers from the following limitations:
Writing custom code would benefit only if you are looking for one-time data migration from Amazon Aurora to BigQuery.
When you have a use case where data needs to be migrated on an ongoing basis or in real-time, you would have to move it in an incremental manner. The above custom code ETL would fail here. You would need to write additional lines of code to achieve this real-time data migration.
There are chances that the custom code breaks if the source schema gets changed.
If in future you identify the data transformations needs to be applied on data, you would need extra time and resources.
Since you have developed this custom code to migrate data you have to maintain the standard of the code to achieve the business goals.
In the custom code approach, You have to focus on both business and technical details.
ETL code is fragile with a high susceptibility to break the entire process that may cause inaccurate and delay in data availability in BigQuery.
Method 2: Using LIKE.TG Data to Move Data from Aurora to BigQuery
Using a fully managed, easy-to-use Data Pipeline platform likeLIKE.TG , you can load your data from Aurora to BigQuery in a matter of minutes. LIKE.TG is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.
Get Started with LIKE.TG for free
This can be achieved in a code-free, point-and-click visual interface. Here are simple steps to replicate Amazon Aurora to BigQuery using LIKE.TG :
Step 1: Connect to your Aurora DB by providing the proper credentials.
Step 2: Select one of the following the replication mode:
Full dump (load all tables)
Load data from Custom SQL Query
Fetch data using BinLog
Step 3: CompleteAurora to BigQueryMigration by providing information about your Google BigQuery destination such as the authorized Email Address, Project ID, etc.
About Amazon Aurora
Amazon Aurora is a popular relational database developed by Amazon. It is one of the most widely used Databases for low latency data storage and data processing. This Database operates on Cloud technology and is easily compatible with MySQL and PostgreSQL. This way it provides performance and accessibility similar to traditional databases at a relatively low price. Moreover, it is simple to use and it has Amazon security and reliability features.
Amazon Aurora is a MySQL-compatible relational database used by businesses. Aurora offers better performance and cost-effective price than traditional MySQL. It is primarily used for a transactional or operational database. It is specifically not recommended for analytics.
About Google BigQuery
BigQuery is a Google-managed cloud-based data warehouse service. This is intended to store, process and analyze large volume (Petabytes) of data to make data analysis more accurate. BigQuery is known to give quick results with very minimal cost and great performance. Since infrastructure is managed by Google, you as a developer, data analyst or data scientist can focus on uncovering meaningful insights using native SQL.
Conclusion
This blog talks about the two methods you can implement to move data from Aurora to BigQuery in a seamless fashion.
Visit our Website to Explore LIKE.TG
With LIKE.TG , you can achieve simple and efficient Data Replication from Aurora to BigQuery. LIKE.TG can help you move data from not just Aurora DB but 100s of additional data sources.
Sign Up for a 14-Day Free Trial with LIKE.TG and experience a seamless, hassle-free data loading experience from Aurora DB to Google BigQuery. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.
Share your understanding of the Amazon Aurora BigQuery Integration in the comments below!
Zendesk to Redshift: 2 Easy Steps to Move Data
Getting data from Zendesk to Redshift is the right step towards centralizing your organization’s customer interactions and tickets. Analyzing this information can help you gain a deeper understanding of the overall health of your Customer Support, Agent Performance, Customer Satisfaction, and more. Eventually, you would be able to unlock deep insights that grow your business. What is Zendesk?
Zendesk is a Cloud-based all-in-one Customer Support Platform widely used by a broad spectrum of enterprises, from large corporations to small startups. Using any data — from anywhere — Zendesk presents businesses with a comprehensive view of the consumer. Hence, its products are built to include and innovate depending on user input collected through beta and Early Access Programs (EAPs).
Companies that have outgrown their current CRM or are investigating other systems, currently utilize Zendesk’s Support Platform, or deal with a high volume of incoming customer inquiries can benefit from Zendesk. The Zendesk Support Platform helps companies thrive in self-service and proactive engagement by delivering consistent support. Organizations can manage all of their one-on-one customer interactions using Zendesk’s one Customer Support Platform.
Zendesk CRM Software allows you to deliver personalized support where consumers expect it, expand your customer experience process, and optimize your operations. Businesses can find a range of Zendesk products with solutions catered to their needs. Out of its suite of CRM products, Zendesk Sunshine is a contemporary CRM Platform built on top of Amazon Web Services (AWS).
Zendesk CRM Software Products are simple and easy to use, thereby allowing business teams to focus on making the most of their time and energy by selling and answering customer questions. This helps in the expansion of businesses without disrupting software services.
For more information on Zendesk Solution, do visit Zendesk’s informative blog here.
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What is Amazon Redshift?
Amazon Redshift is a petabyte-scale, fully managed data warehouse service that stores data in the form of clusters that you can access with ease. It supports a multi-layered architecture that provides robust integration support for various business intelligence tools and a fast query processing functionality. Apart from business intelligence tools, you can also connect Amazon Redshift to SQL-based clients. It further allows users and applications to access the nodes independently.
Being a fully-managed warehouse, all administrative tasks associated with Amazon Redshift, such as creating backups, security, etc. are taken care of by Amazon.
For further information on Amazon Redshift, you can check our other post here.
For most recent updates on Amazon.com, Inc, visit the Amazon Statistics and Facts page
Methods to Move Data from Zendesk to Redshift
There are two popular methods to perform Zendesk to Redshift data replication.
Method 1: Copying your Data from Zendesk to Redshift Using Custom Scripts
You would have to spend engineering resources to write custom scripts to pull the data using Zendesk API, move data to S3, and then to Redshift destination tables. To achieve data consistency and ensure no discrepancies arise, you will have to constantly monitor and invest in maintaining the infrastructure.
Method 2: Moving your Data from Zendesk to Redshift Using LIKE.TG
LIKE.TG is an easy-to-use Data Integration Platform that can move your data from Zendesk (Data Source Available for Free in LIKE.TG ) to Redshift in minutes. You can achieve this on a visual interface without writing a single line of code. Since LIKE.TG is fully managed, you would not have to worry about any monitoring and maintenance activities. This will ensure that you stop worrying about data and start focussing on insights.
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Methods to Move Data from Zendesk to Redshift
Method 1: Copying your Data from Zendesk to Redshift Using Custom ScriptsMethod 2: Moving your Data from Zendesk to Redshift Using LIKE.TG
Let us, deep-dive, into both these methods.
Method 1: Copying your Data from Zendesk to Redshift Using Custom Scripts
Here is a glimpse of the broad steps involved in this:
Write scripts for some or all of Zendesk’s APIs to extract data. If you are looking to get updated data on a periodic basis, make sure the script can fetch incremental data. For this, you might have to set up cron jobsCreate tables and columns in Redshift and map Zendesk’s JSON files to this schema. While doing this, you would have to take care of the data type compatibility between Zendesk data and Redshift. Redshift has a much larger list of datatypes than JSON, so you need to make sure you map each JSON data type into one supported by RedshiftRedshift is not designed for line-by-line updates or SQL “upsert” operations. It is recommended to use an intermediary such as AWS S3. If you choose to use S3, you will need to Create a bucket for your dataWrite an HTTP PUT for your AWS REST API using Curl or PostmanOnce the bucket is in place, you can then send your data to S3Then you can use a COPY command to get your data from S3 into Redshift In addition to this, you need to make sure that there is proper monitoring to detect any change in the Zendesk Schema. You would need to modify and update the script if there is any change in the incoming data structure
Method 2: Moving your Data from Zendesk to Redshift Using LIKE.TG
LIKE.TG is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.
Using the LIKE.TG Data Integration Platform, you can seamlessly replicate data from Zendesk to Redshift with 2 simple steps.
Step 1: Configure the data source using Zendesk API token, Pipeline Name, Email, and Sub Domain.
Step 2: Configure the Redshift warehouse where you want to move your Zendesk data by giving the Database Port, Database User, Database Password, Database Name, Database Schema, Database Cluster Identifier, and Destination Name.
LIKE.TG does all the heavy-weightlifting and will ensure your data is moved reliably to Redshift in real-time.
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Advantages of Using LIKE.TG
The LIKE.TG Data Integration platform lets you move data from Zendesk (Data Source Available for Free in LIKE.TG ) to Redshift. Here are some other advantages:
No Data Loss – LIKE.TG ’s fault-tolerant architecture ensures that data is reliably moved from Freshdesk to Redshift without data loss.100’s of Out of the Box Integrations – In addition to Freshdesk, LIKE.TG can bring data from 100+ Data Sources (Including 30+ Free Data Sources)into Redshift in just a few clicks. This will ensure that you always have a reliable partner to cater to your growing data needs.Minimal Setup – Since LIKE.TG is fully managed, setting up the platform would need minimal effort and bandwidth from your end.Automatic schema detection and mapping – LIKE.TG automatically scans the schema of incoming Freshdesk data. If any changes are detected, it handles this seamlessly by incorporating this change on Redshift.Exceptional Support – LIKE.TG provides 24×7 support to ensure that you always have Technical support for LIKE.TG is provided on a 24/7 basis over both Email and Slack.
Challenges While Transferring Data from Zendesk to Redshift Using Custom Code
Before you write thousands of lines of code to copy your data, you need to familiarize yourself with the downside of this approach.
More often than not, you will need to monitor the Zendesk APIs for changes, check your data tables to make sure all columns are being updated correctly. Additionally, you have to come up with a data validation system to ensure all your data is being transferred accurately.
In an ideal world, all of this is perfectly doable. However, in today’s agile work environment, it usually means expensive engineering resources are scrambling just to stay on top of all the possible things that can go wrong.
Think about the following:
How will you know if an API has been changed by Zendesk?How will you find out when the Redshift is not available for writing?Do you have the resources to rewrite or update the code periodically?How quickly can you update the schema in Redshift in response to a request for more data?
On the other hand, a ready-to-use platform like LIKE.TG rids you of all these complexities. This will not only provide you with analysis-ready data but will also empower you to focus on uncovering meaningful insights instead of wrangling with Zendesk data.
Conclusion
The flexibility you get from building your own custom solution to move data from Zendesk to Redshift comes with a high and ongoing cost in terms of engineering resources.
In this article, you learned about Zendesk to Redshift Data Migration methods. You also learned about the Zendesk Software and Amazon Redshift Data warehouse. However, integrating and analyzing your data from a diverse set of data sources can be challenging and this is where LIKE.TG Data comes into the picture.
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LIKE.TG is a No-code Data Pipeline and has awesome 100+ pre-built integrations that you can choose from. LIKE.TG can help you integrate your data from numerous sources such as Zendesk (Data Source Available for Free in LIKE.TG ) and load it into a destination to analyze real-time data with a BI tool and create your Dashboards. It will make your life easier and make data migration hassle-free. It is user-friendly, reliable, and secure.
Want to take LIKE.TG for a spin? Sign Up for a 14-day free trial and experience the feature-rich LIKE.TG suite first hand. You can also have a look at the unbeatablepricingthat will help you choose the right plan for your business needs.
Share your experience of learning about Zendesk to Reshift Data Migration. Let us know in the comments below!
Snowflake Data Warehouse 101: A Comprehensive Guide
Snowflake Data Warehouse delivers essential infrastructure for handling a Data Lake, and Data Warehouse needs. It can store semi-structured and structured data in one place due to its multi-clusters architecture that allows users to independently query data using SQL. Moreover, Snowflake as a Data Lake offers a flexible Query Engine that allows users to seamlessly integrate with other Data Lakes such as Amazon S3, Azure Storage, and Google Cloud Storage and perform all queries from the Snowflake Query Engine.This article will give you a comprehensive guide to Snowflake Data Warehouse. You will get to know about the architecture and performance of Snowflake Data Warehouse. You will also explore the Features, Pricing, Advantages, Limitations, and many more in further sections. Let’s get started.
What is Snowflake Data Warehouse?
Snowflake Data Warehouse is a fully managed, cloud data warehouse available to customers in the form of Software-as-a-Service (SaaS) or Database-as-a-Service (DaaS). The phrase ‘fully managed’ means users shouldn’t be concerned about any of the back-end work like server installation, maintenance, etc. A Snowflake Data Warehouse instance can easily be deployed on any of the three major cloud providers –
Amazon Web Services (AWS)
Google Cloud Storage (GCS)
Microsoft Azure
The customer can select which cloud provider they want for their Snowflake instance. This comes in handy for firms working with multiple cloud providers. Snowflake querying follows the standard ANSI SQL protocol and it supports fully structured as well as semi-structured data like JSON, Parquet, XML, etc.
To know more about Snowflake Data Warehouse, visit this link.
Architecture of Snowflake Data Warehouse
Here’s a diagram depicting the fundamental Snowflake architecture –
At the storage level, there exists cloud storage that includes both shared-disk (for storing persistent data) as well as shared-nothing (for massively parallel processing or MPP of queries with portions of data stored locally) entities. Ingested cloud data is optimized before storing in a columnar format. The data ingestion, compression, and storage are fully managed by Snowflake; as a matter of fact, this stored data is not directly accessible to users and can only be accessed via SQL queries.
Next up is the query processing level, this is where the SQL queries are executed. All the SQL queries are part of a particular cluster that consists of several compute nodes (this is customizable) and are executed in a dedicated, MPP environment. These dedicated MPPs are also known as virtual data warehouses. It is not uncommon for a firm to have separate virtual data warehouses for individual business units like sales, marketing, finance, etc. This setup is more costly but it ensures data integrity and maximum performance.
Finally, we have cloud services. As mentioned in the boxes, these are a bunch of services that help tie together the different units of Snowflake ranging from access control/data security to infrastructure and storage management. Know more about Snowflake Data Warehouse architecture here.
Performance of Snowflake Data Warehouse
The Snowflake Features has been designed for simplicity and maximum efficiency via parallel workload execution through the MPP architecture. The idea of increasing query performance is switched from the traditional manual performance tuning options like indexing, sorting, etc. to following certain generally applicable best practices. These include the following –
Workload Separation
Persisted or Cached Results
1. Workload Separation
Because it is super easy to spin up multiple virtual data warehouses with the desired number of compute nodes, it is a common practice to divide the workloads into separate clusters based on either Business Units (sales, marketing, etc.) or type of operation (data analytics, ETL/BI loads, etc.) It is also interesting to note that virtual data warehouses can be set to auto-suspend (default is 10 minutes) when they go inactive or in other words, no queries are being executed. This feature ensures that customers don’t accrue a lot of costs while having many virtual data warehouses operate in parallel.
2. Persisted or Cached Results
Query results are stored or cached for a certain timeframe (default is 24 hours). This is utilized when a query is essentially re-run to fetch the same result. Caching is done at two levels – local cache and result cache. Local cache provides the stored results for users within the same virtual data warehouse whereas result cache holds results that could be retrieved by users regardless of the virtual data warehouse they belong to.
ETL and Data Transfer in Snowflake Data Warehouse
ETL refers to the process of extracting data from a certain source, transform the source data to a certain format (typically the format that matches up to the target table) and load this data into the desired target table. The source and target are often two different entities or database systems. Some examples include a flat-file load into an Oracle table, a CRM data export into an Amazon Redshift table, data migration from a Postgres database onto a Snowflake Data Warehouse, etc.
Snowflake has been designed to connect to a multitude of data integrators using either a JDBC or an ODBC connection.
In terms of loading data, Snowflake offers two methods –
Bulk Loading This is basically batch loading of data files using the COPY command. COPY command lets users copy data files from the cloud storage into Snowflake tables. This step involves writing code that typically gets scripted to run at scheduled intervals.
Continuous LoadingIn this case, smaller amounts of data are extracted from the staging environment (as soon as they are available) and loaded in quick increments into a target Snowflake table. The feature named Snowpipe makes this possible.
Snowflake offers a bunch of transformation options for the incoming data before the load. This is achieved through the COPY command. Some of these include –
Reordering of columns
Column omissions
Casting columns in the select statement
When it comes to dealing with these intricacies of ETL, it is best to implement a fully managed Data Integration Software solution like LIKE.TG .
Scaling on Snowflake Data Warehouse
Previously, the article briefly touched on virtual data warehouses, clusters, nodes, etc. Now, let’s dive deeper into these areas to better understand how can one tweak these to enable scaling most efficiently.
Snowflake provides for two kinds of scaling –
Scaling up
Scaling out
1. Scaling up
Scaling up means resizing a virtual data warehouse in terms of its nodes. A Snowflake Data Warehouse user can easily modify the number of nodes assigned to a virtual data warehouse. This can be done even while the data warehouse is in operation, although, only the queries that are newly submitted or the ones already queued will be affected by the changes. Apart from the ‘auto suspend’ feature described before, there is a provision to set the minimum and a maximum number of nodes per warehouse.
After setting the maximum and the minimum number of nodes, let Snowflake decide when to scale up or down the number of nodes based on the warehouse activity. This is an efficient way to set up your cluster. Scaling is particularly suitable in the following cases –
To improve query performance in case of larger and more complex queries.
When the queries are submitted using the same local cache.
The option to scale out is not there.
Scaling out is generally preferred, especially with the more recent addition and availability of multi-cluster warehouses, which will be discussed next.
2. Scaling out
Scaling out before referred to adding more virtual data warehouses. However, with the advent of the recent multi-cluster warehouse feature, the old way has become more or less obsolete. So let’s get into the multi-cluster warehouse set up – as the name suggests, in this type of arrangement, a data warehouse can have multiple clusters each having a different set of nodes. Even though Snowflake provides for a ‘maximized’ option, which is an instruction for the data warehouse to have all of its clusters running regardless, almost always, you would want to set this to the ‘Auto-Scale’ mode. Here’s an example of how Auto scaling looks like –
As can be seen, you can set a bunch of parameters in a way that works best for you.
Features like Auto-scale and Auto-suspend provides flexibility for query execution as well as cost management. Let’s see how that works in the next section.
Pricing of Snowflake Data Warehouse
Snowflake has a fairly simple pricing model – charges apply to storage and compute aka virtual data warehouses. The storage is charged for every Terabyte (TB) of usage while compute is charged at a per second per computing unit (or credit) basis. Before getting into an example, it is worthwhile to note that Snowflake offers two broader pricing models –
On-demand – Pay per your usage of storage and compute
Pre-purchased – A set capacity of storage and compute could be pre-purchased at a discount as opposed to accruing the same usage at a higher cost via on-demand.
Now onto the usage pricing examples, the two popular on-demand pricing models available are as follows –
Snowflake Standard Edition
Snowflake Enterprise Sensitive Data Edition
1. Snowflake Standard Edition
Storage costs you around $23 per TB and computes costs would be approximately 4 cents per minute per credit, billed for a minimum time of one minute.
2. Snowflake Enterprise Sensitive Data Edition
Being a premium version with advanced encryption and security features as well as HIPAA compliance, storage costs roughly the same while compute gets bumped to around 6.6 cents per minute per credit.
The above charges for compute apply only to ‘active’ data warehouses and any inactive session time is ignored for billing purposes. This is why it’s important and profitable to set features like auto-suspend and auto-scale in a way as to minimize the charges accrued for idle warehouse periods.
Data Security Maintenance on Snowflake Data Warehouse
Data security is dealt with very seriously at all levels of the Snowflake ecosystem.
Regardless of the version, all data is encrypted using AES 256, and the higher-end enterprise versions have additional security features like period rekeying, etc.
As Snowflake is deployed on a cloud server like AWS or MS Azure, the staging data files (ready for loading/unloading) in these clouds get the same level of security as the staging files for Amazon Redshift or Azure SQL Data Warehouse. While in transit, the data is heavily protected using industrial-strength secure protocols. Know more about Snowflake Data Warehouse security here.
As for maintenance, Snowflake is a fully managed cloud data warehouse, end users have practically nothing to do to ensure a smooth day-to-day operation of the data warehouse. This helps customers tremendously to focus more on the front-end data operations like data analysis and insights generation, and not so much on the back-end stuff like server performance and maintenance activities.
Key Features of Snowflake Data Warehouse
Ever since the Snowflake Data Warehouse got into the growing cloud Data Warehouse market, it has established itself as a solid choice. That being said, here are some things to consider that might make it particularly suitable for your purposes –
It offers five editions going from ‘standard’ to ‘enterprise’. This is a good thing as customers have options to choose from based on their specific needs.
The ability to separate storage and compute is something to consider and how that relates to the kind of data warehousing operations you’d be looking for.
Snowflake is designed in a way to ensure the least user input and interaction required for any performance or maintenance-related activity. This is not a standard among cloud DWHs. For instance, Redshift needs user-driven data vacuuming.
It has some cool querying features like undrop, fast clone, etc. These might be worth checking out as they may account for a good chunk of your day-to-day data operations.
Pros and Cons of Snowflake Data Warehouse
Here are the advantages and disadvantages of using Snowflake Data Warehouse as your data warehousing solution –
Know more about Snowflake Data Warehouse features here.
Why was the Company Called Snowflake?
One of the reasons why the company called Snowflake is that Snowflake has many edges in multiple directions. So as the Snowflake Data Warehouse offers virtual Data Warehousing allowing users to create and organize Data Warehouses just like dimensions tables surround fact tables. The architecture of Snowflake Data Warehouse resembles the Snowflake. Another reason for its name is that the early investors and founders love the winter season, and the name is given as a tribute to it.
Alternatives for Snowflake Data Warehouse
The shift towards cloud data warehousing solutions picked up real pace in the late 2000s, mostly thanks to Google and Amazon. Since then, so many traditional database vendors like Microsoft, Oracle, etc. as well as newer players like Vertica, Panoply, etc. have entered this space. Having said that, let’s take a look at some of the popular alternatives to Snowflake.
Amazon Redshift vs Snowflake
Google BigQuery vs Snowflake
Azure SQL Data Warehouse vs Snowflake
1. Amazon Redshift vs Snowflake
The cloud data warehousing solution of one of the largest cloud providers (Amazon Web Services or AWS) in this domain that can work with Petabyte scale data. Supports fully structured as well as some semi-structured data like JSON, stored in columnar format. However, compute and storage are not separate like Snowflake. Generally, a costlier alternative to Snowflake but more robust and faster with optimizable tuning techniques like materialized views, sorting/distribution keys, etc.
2. Google BigQuery vs Snowflake
Also, a columnar, structured data warehouse that is part of the Google Cloud Services suite. It has other features comparable to Amazon Redshift like MPP architecture. It can be easily integrated with other data vendors, etc. BigQuery is similar to Snowflake in the sense that storage and compute are treated separately, however, instead of a discounted, pre-purchase pricing model (as in Snowflake), BigQuery services are charged monthly/yearly at a flat rate.
3. Azure SQL Data Warehouse vs Snowflake
Azure is gaining in popularity by the day and is especially known for performing analytics tasks. It is part of the Microsoft suite of products so there is a natural advantage for users and firms dealing with MS products and technologies like SQL Server, SSRS, SSIS, T-SQL, etc. Also, a columnar database, with storage and compute separated. Azure SQL engine is also known for its high level of concurrency.
How to Get Started with Snowflake?
Here are some resources for you to get started with Snowflake.
Snowflake Documentation: This is the official documentation from Snowflake about their services, features, and provides clarity on all aspects of this data warehouse.
Snowflake ecosystem of partner integrations. This takes you to their integration options to third-party partners and technologies having native connectivity to Snowflake. This includes various data integration solutions to BI tools to ML and data science platforms.
Pricing page: You can check out this link to know about their pricing plans which also contains guides and relevant contacts for Snowflake consultants.
Community forums: There are different Community Groups under major topics on Snowflake website. You can check out Snowflake Lab on GitHub or visit StackOverflow or Reddit forums as well.
Snowflake University and Hands-on Lab: This contains many courses for people with varying expertise levels.
YouTube channel: You can check out their YouTube for various videos that include tutorials, customer success videos etc.
Conclusion
As can be gathered from the article so far, the Snowflake Data Warehouse is a secure, scalable, and popular cloud data warehousing solution. It has achieved this status by constantly re-engineering and catering to a wide variety of industrial use cases that helped win over so many clients.You can have a good working knowledge of Snowflake by understandingSnowflake Create Table. You can have a look at 8 Best Data Warehousing Tools.
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Frequently Asked Questions
Why Snowflake is better than SQL?
Snowflake’s approach to data modeling is a schema-less approach. This will help you to efficiently store and query data without a predefined schema. On the other hand, SQL Server has a traditional relational data modeling approach which needs creating schemas before you can store your data.
2. Snowflake warehouse vs. database
Snowflake and a database are different in the sense that Snowflake is built of database architectures and utilizes database tables to store data. It also uses massively parallel processing capability to compute clusters to process queries for the data stored in it. A database is an electronically stored and structured data collection.
3. What is the difference between Snowflake and ETL?
Snowflake is a good SaaS data cloud platform and data warehouse that can store and help you query your data efficiently. ETL (extract, transform and load) is the process of moving data from various data sources to a single destination such as a data warehouse.
Businesses can use automated platforms like LIKE.TG Data to set the integration and handle the ETL process. It helps you directly transfer data from a source of your choice to a Data Warehouse, Business Intelligence tools, or any other desired destination in a fully automated and secure manner without having to write any code and will provide you with a hassle-free experience.
Want to take LIKE.TG for a spin? Sign Up for a 14-day free trial and experience the feature-rich LIKE.TG suite first hand. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.
Share your experience of using Snowflake Data Warehouse