LIKE.TG vs DMS AWS – 7 Comprehensive Parameters
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Migrating data from different sources into Data Warehouses can be hard. Hours of engineering time need to be spent in hand-coding complex scripts to bring data into the Data Warehouse. Moreover, Data Streaming often fails due to unforeseen errors for eg. the destination is down or an error in a piece of code. With the increase in such overheads, opting for a Data Migration product becomes impertinent for smooth Data Migration.
LIKE.TG Data and DMS AWS are two very effective ETL tools available in the market and users are often confused while deciding one of them. The LIKE.TG vs DMS AWS is a constant dilemma amongst the users who are looking for a hassle-free way to automate their ETL process.
This post on LIKE.TG vs DMS AWS has attempted to highlight the differences between LIKE.TG and AWS Database Migration Service on a few critical parameters to help you make the right choice. Read along with the comparisons of LIKE.TG VS DMS AWS and decide which one suits you the best.
LIKE.TG _Data">Introduction to LIKE.TG Data
LIKE.TG is a Unified Data Integration platform that lets you bring data into your Data Warehouse in real-time. With a beautiful interface and flawless user experience, any user can transform, enrich and clean the data and build data pipelines in minutes. Additionally, LIKE.TG also enables users to build joins and aggregates to create materialized views on the data warehouse for faster query computations.
LIKE.TG also helps you to start moving data from 100+ sources to your data warehouse in real-time with no code for the price of $249/month!
To learn more about LIKE.TG Data, visit here.
Introduction to AWS DMS
AWS DMS is a fully managed Database Migration service provided by Amazon. Users can connect various JDBC-based data sources and move the data from within the AWS console.
AWS Database Migration Service allows you to migrate data from various Databases to AWS quickly and securely. The original Database remains fully functional during the migration, thereby minimizing downtime for applications that depend on the Database.
To learn more about DMS AWS, visit here.
LIKE.TG _Data">LIKE.TG -web-icon LIKE.TG -bookmark note__header__icon">Simplify your ETL Process with LIKE.TG Data
LIKE.TG Data is a simple to use Data Pipeline Platform that helps you load data from 100+ sources to any destination like Databases, Data Warehouses, BI Tools, or any other destination of your choice in real-time without having to write a single line of code. LIKE.TG provides you a hassle-free data transfer experience. Here are some more reasons why LIKE.TG is the right choice for you:
- Minimal Setup Time: LIKE.TG has a point-and-click visual interface that lets you connect your data source and destination in a jiffy. No ETL scripts, cron jobs, or technical knowledge is needed to get started. Your data will be moved to the destination in minutes, in real-time.
- Automatic Schema Mapping: Once you have connected your data source, LIKE.TG automatically detects the schema of the incoming data and maps it to the destination tables. With its AI-powered algorithm, it automatically takes care of data type mapping and adjustments – even when the schema changes at a later point.
- Mature Data Transformation Capability: LIKE.TG allows you to enrich, transform and clean the data on the fly using an easy Python interface. What’s more – LIKE.TG also comes with an environment where you can test the transformation on a sample data set before loading to the destination.
- Secure and Reliable Data Integration: LIKE.TG has a fault-tolerant architecture that ensures that the data is moved from the data source to destination in a secure, consistent and dependable manner with zero data loss.
- Unlimited Integrations: LIKE.TG has a large integration list for Databases, Data Warehouses, SDKs & Streaming, Cloud Storage, Cloud Applications, Analytics, Marketing, and BI tools. This, in turn, makes LIKE.TG the right partner for the ETL needs of your growing organization.
Try out LIKE.TG by signing up for a 14-day free trial here.
LIKE.TG _vs_DMS_AWS">Comparing LIKE.TG vs DMS AWS
LIKE.TG _vs_DMS_AWS">1) Variety of Data Source Connectors: LIKE.TG vs DMS AWS
The starting point of the LIKE.TG vs DMS AWS discussion is the number of data sources these two can connect. With LIKE.TG you can migrate data from not only JDBC sources, but also from various cloud storage (Google Drive, Box, S3) SaaS (Salesforce, Zendesk, Freshdesk, Asana, etc.), Marketing systems (Google Analytics, Clevertap, Hubspot, Mixpanel, etc.) and SDKs (iOS, Android, Rest, etc.). LIKE.TG supports the migration of both structured and unstructured data. A complete list of sources supported by LIKE.TG can be found here.
LIKE.TG supports all the sources supported by DMS and more.
DMS, on the other hand, provides support to only JDBC databases like MySQL, PostgreSQL, MariaDB, Oracle, etc. A complete list of sources supported by DMS can be found here.
However, if you need to move data from other sources like Google Analytics, Salesforce, Webhooks, etc. you would have to build and maintain complex scripts for migration to bring it into S3. From S3, DMS can be used to migrate the data to the destination DB. This would make migration a tedious two-step process.
DMS does not provide support to move unstructured NoSQL data.
Other noteworthy differences on the source side:
- LIKE.TG promises a secure SSH connection when moving data whereas DMS does not.
- LIKE.TG also allows users to write custom SQL to move partial data or perform table joins and aggregates on the fly while DMS does not.
- With LIKE.TG users can enjoy granular control on Table jobs. LIKE.TG lets you control data migration at table level allowing you to pause the data migration for certain tables in your database at will. DMS does not support such a setup.
- LIKE.TG allows you to move data incrementally through SQL queries and BinLog. With DMS, incremental loading of data is possible only through BinLog.
LIKE.TG _vs_DMS_AWS">2) Data Transformations: LIKE.TG vs DMS AWS
With LIKE.TG , users can Clean, Filter, Transform and Enrich both structured and unstructured data on the fly through a simple Python interface. You can even split an incoming event into multiple arbitrary events making it easy for you to normalize nested NoSQL data. All the standard Python Libraries are made available to ensure users have a hassle-free data transformation experience. The below image shows the data transformation process at LIKE.TG .
DMS allows users to create basic data transformations such as Adding a prefix, Changing letters to uppercase, Skip a column, etc. However, advanced transformations like Mapping IP to location, Skipping rows based on conditions, and many others that can be easily done on LIKE.TG are not supported by DMS.
The above image shows the Data transformation process of DMS AWsS. To be sure that the transformation is error-free, DMS users will have to hand-code sample event pulls and experiment on them or worse, wait for data to reach the destination to check. LIKE.TG lets users test the transformation on a sample data set and preview the result before deployment.
LIKE.TG _vs_DMS_AWS">3) Schema handling: LIKE.TG vs DMS AWS
Schemas are important for the ETL process and therefore can act as a good parameter in the LIKE.TG vs DMS discussion. LIKE.TG allows you to map the source schema to the destination schema on a beautiful visual interface. DMS does not have an interface for schema mapping. The data starts moving as soon as the job is configured. If the mapping is incorrect the task fails and someone from engineering will have to manually fix the errors.
Additionally, LIKE.TG automatically detects the changing schema and notifies the user of the change so that he can take necessary action.
LIKE.TG _vs_DMS_AWS">4) Moving Data into Redshift: LIKE.TG vs DMS AWS
Amazon Redshift is a popular Data Warehouse and can act as a judging parameter in this LIKE.TG vs DMS AWS discussion. Moving Data into Redshift is a cakewalk with LIKE.TG . Users would just need to connect the sources to Redshift, write relevant transformations, and voila, data starts streaming.
Moving data into Redshift through DMS comes with a lot of overheads. Users are expected to manage the S3 bucket (creating directories, managing permissions, etc.) themselves. Moreover, DMS compulsorily requires the user’s Redshift cluster region, the DMS region to be the same. While this is not a major drawback, this becomes a problem when users want to change the region of the Redshift cluster but not for S3.
LIKE.TG _vs_DMS_AWS">5) Notifications: LIKE.TG vs DMS AWS
LIKE.TG notifies all exceptions to users on both Slack and Email. The details of the exceptions are also included in the notification to enable users to take quick action.
DMS notifies all the anomalies over AWS Cloudwatch only. The user will have to configure Cloudwatch to receive notifications on email.
LIKE.TG _vs_DMS_AWS">6) Statistics and Audit log: LIKE.TG vs DMS AWS
LIKE.TG provides a detailed audit log to the user to get visibility into activities that happened in the past at the user level. DMS provides logs at the task level.
LIKE.TG provides a simple dashboard that provides a one-stop view of all the tasks you have created. DMS provides data migration statistics on Cloudwatch.
LIKE.TG _vs_DMS_AWS">7) Data Modelling: LIKE.TG vs DMS AWS
Data Modeling is another essential aspect of this LIKE.TG vs DMS AWS dilemma. LIKE.TG ’s Modelling and Workflows features allow you to join and aggregate the data to store results as materialized views on your destination. With these views, users experience faster query response times making any report pulls possible in a few seconds.
DMS restricts its functions to data migration services only. Data Models on LIKE.TG
Conclusion
The article explained briefly about LIKE.TG Data and DMS AWS. It then provided a detailed discussion on the LIKE.TG vs DMS AWS choice dilemma. The article considered 7 parameters to analyze both of these ETL tools. Moreover, it provided you enough information on each criterion used in the LIKE.TG vs DMS AWS discussion.
LIKE.TG Data, understand the complex processes involved in migrating your data from a source to a destination and LIKE.TG has been built just to simplify this for you. With a superior array of features as opposed to DMS, LIKE.TG ensures a hassle-free data migration experience with zero data loss.
LIKE.TG Data, with its strong integration with 100+ sources & BI tools, allows you to export, load, transform & enrich your data & make it analysis-ready in a jiffy.
Want to take LIKE.TG for a spin. Try LIKE.TG Data’s 14 days free trial and experience the benefits!
Share your views on the LIKE.TG vs DMS discussion in the comments section!
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