Modern Data Warehouse: Why Should You Migrate?

Data is the lifeblood of informed decision-making, and a modern data warehouse is its beating heart, where insights are born. The emphasis here is not on any data warehouse but a “modern” data warehouse that can keep up with the demands of today’s data challenges.  

The unprecedented rise in data volume and complexity has shown that the traditional infrastructure cannot suffice, which is why data warehouse modernization is an essential requirement.  

So, what do we mean by a modern data warehouse? You might be thinking it’s on the cloud. Yes, it is, but there is much more to it than just being on the cloud. In this blog, we will discuss everything about a modern data warehouse including why you should invest in one and how you can migrate your traditional infrastructure to a modern data warehouse. 

What is a Modern Data Warehouse? 

Simply put, a modern data warehouse is hosted over cloud as compared to traditional data warehouses, which are on-premises. So, what does the cloud have to offer? The most obvious benefit is scalability. The cloud data warehouse is highly scalable, which makes it ideal for processing an insane amount of data and performing extremely complex queries within minutes.  

The interesting part about a cloud data warehouse is the pay-as-you-go model. So, it is not only powerful and capable, but affordable as well. On top of it, many modern data warehouses separate storage from compute resources, so organizations can scale each component independently, which further improves cost efficiency and performance optimization. 

As far as the technical aspects are concerned, modern data warehouses do not follow a rigid schema, which means they can easily handle semi-structured and unstructured data. The best part about a modern data warehouse is that it comes with user-friendly tools and interfaces that enable business analysts and data scientists to explore and analyze data without requiring deep technical expertise. 

Modern Data Warehouse Architecture 

If you talk about traditional data warehouse architecture, they usually have a three-tier architecture, with data base server as the bottom layer, data storage layer as the middle layer and data processing as the final layer. The traditional architecture is hence limited to ETL/ELT and mostly star schema.  

Modern data warehouses, on the other hand, don’t have a particular structure. Each cloud data warehouse platform supports a different architecture. However, to generalize, a modern data warehouse can support:  

Massively Parallel Processing (MPP):  It is a distributed computing architecture designed to process and analyze large volumes of data through parallelism. MPP architecture achieves parallelism by dividing data and tasks into smaller, manageable units that can be processed simultaneously across multiple nodes. Each node is equipped with its own processing power and memory. 

MPP systems often employ load balancing to distribute query workloads evenly across nodes to prevent any single node from becoming a performance bottleneck. 

Due to the scalability, MPP systems can adapt to the evolving data needs of businesses, which is why they are well-suited for dynamic environments where data is constantly growing and evolving. 

Lambda Architecture: The Lambda Architecture aims to provide a robust and fault-tolerant solution for processing both batch and real-time data in a scalable way. The architecture is divided into different layers including:  

1. Batch Layer: This layer is responsible for handling historical or batch data processing. 
2. Speed Layer: The Speed Layer deals with real-time data processing and is responsible for handling data as it arrives. It is designed to process data with very low latency for real-time insights. 
3. Serving Layer: The Serving Layer is where the results from both the Batch and Speed Layers are stored and made available for querying. This layer provides a consolidated view of processed data. 
4. The Presentation Layer represents the top layer of the Lambda Architecture. It is where data is presented to end-users through various applications, including dashboards, reports, and APIs.

Hybrid Architecture:  A hybrid architecture in the context of a modern data warehouse refers to a design that combines both on-premises and cloud-based components to meet an organization’s data processing and analytics needs. The core of the hybrid architecture is a cloud data warehouse, which provides scalability, flexibility, and the ability to efficiently process and store large volumes of data. However, organizations maintain some of their data sources and systems on their own physical infrastructure. These on-premises data sources can include legacy databases, proprietary systems, and sensitive data that needs to be kept in-house for compliance or security reasons. 

Traditional Vs Modern Data Warehouse 

When it comes to the differences between a traditional and a modern data warehouse, it is not just about being on cloud or on-premises. Here are some more differences between the two:  

1. Data Processing Paradigm 

Traditional data warehouses are typically batch-oriented. They process and load data in scheduled, periodic batches, so data is not available for analysis until after it has been processed and loaded, leading to a latency in data availability. Modern data warehouses, on the other hand, emphasize real-time or near-real-time data processing. They enable continuous data streaming and support both batch and stream processing, which provide faster access to up-to-the-minute insights. 

2. Scalability 

Scaling traditional data warehouses can be a complex and expensive process because they often require significant upfront investments in hardware and infrastructure. Cloud data warehouses are are highly scalable and allow you to easily scale up or down based on their needs, which is particularly valuable for handling variable workloads and accommodating growing data volumes. 

3. Data Integration 

Data integration in traditional data warehouses can be complex and time-consuming, as it involves extensive ETL (Extract, Transform, Load) processes to prepare data for analysis. Data from different sources often needs to be transformed to fit into a common schema. 

Modern data warehouses often provide built-in data integration and transformation capabilities, which simplifies the process of ingesting, cleansing, and harmonizing data from various sources. 

4.  Data Storage and Cost: 

Traditional data warehouses rely on specialized, expensive hardware and on-premises infrastructure. Organizations are responsible for managing and maintaining the hardware, which results in high operational costs. 

Modern ata warehouses operate on a pay-as-you-go model, where you only pay for the resources you consume, resulting in cost savings and more predictable expenses. 

5.  Schema Flexibility  

One of the most important differences between the two is that traditional data warehouses often use fixed and rigid schemas. Any changes in the data structure or schema require significant effort and can disrupt ongoing operations. In contrast, modern data warehouses offer more schema flexibility, and can support semi-structured and unstructured data. 

Now that you know the different architectures, let’s look at some of the specific modern cloud data warehouses in the market and their architectures.

Read More: Data warehouse Cost Estimation

New Data Warehouses Architecture 

Amazon Redshift 

Source 

Amazon Redshift harnesses the power of a massively parallel processing (MPP) architecture, where data is stored in slices using a columnar format. In this architecture, each node is equipped with its dedicated storage, RAM, and computational resources. Redshift primarily operates through two types of nodes: leader nodes and compute nodes. The leader node takes charge of accepting queries and then delegates their execution to the compute nodes, which carry out the data processing tasks. 

One of the key advantages of this MPP setup is its ability to allow compute nodes to concurrently process data across various slices, resulting in robust query performance. These compute nodes subsequently return the query results to the leader nodes, which consolidate and prepare the data for client-side applications to utilize. 

What’s especially convenient is that you can seamlessly connect with Redshift using a variety of business intelligence (BI) or analytics tools.  

Read More: 3 Ways to Transfer Data from Amazon S3 to Redshift

Google Big Query 

Google BigQuery operates on a serverless architecture that lets Google dynamically manage the allocation of machine resources and keep resource management decisions hidden from you. One of the standout features of BigQuery is its versatility in data handling. You can load data from various sources, including Google Cloud Storage and other readable data repositories. Alternatively, you can opt for real-time data streaming, which enables the incremental addition of data row-by-row as it becomes available. 

At the heart of BigQuery’s architecture is Dremel, a high-performance, massively parallel query engine Dremel employs a columnar data structure, akin to Redshift, and leverages a tree architecture to efficiently dispatch queries among thousands of machines, resulting in rapid response times.

Read More: BigQuery vs. Redshift: Which One Should You Choose?

Snowflake

Source 

Snowflake’s architecture also follows a cluster and node-based approach. However, the major difference in the architecture is that it that separates compute capabilities from storage. So, when data is not actively in use, Snowflake effectively relocates it to a more cost-efficient storage area. This approach optimizes resource utilization as it avoids the unnecessary use of storage areas during computation. Moreover, the decoupling of storage from compute offers the ability to efficiently scale resources up or down as needed, surpassing the capacities of traditional on-premises data warehouses. 

Benefits of Modern Data Warehousing 

Up till now, we have established the fact that modern data warehouses are on the cloud and they are way better and capable that traditional data warehouses. But is modern data warehousing even worth it? Should you even consider disrupting your entire architecture to migrate to a modern one?  

Here are some benefits that you should consider if you are stuck with the dilemma:  

1. Cost Efficiency: While migrating to a new architecture would be a time consuming and cost intensive initially, but you need to think long term. Cloud-based modern data warehouses often operate on a pay-as-you-go model, so there is no need for upfront investments in hardware and infrastructure. This model reduces total cost of ownership and predictable expenses. 
2. Global Reach: Traditional data warehouses cannot beat this benefit at any cost. Cloud providers have data centers in various regions, so you can store and process data closer to their target audience, which Improves user experience and ultimately leads to potential revenue gains 
3. High Performance: Many cloud data warehouses are designed for high-speed querying and analytics, offering impressive performance for complex analytical workloads. Faster analytics mean quicker insights and quicker decision making.  
4. Agility: Modern data warehouses offer rapid provisioning, so you can set up and start using the environment quickly. The agility is especially important for organizations that need to respond to changing data and analytics demands promptly. 
5. Security: Now this is a debatable benefit. Often the argument is that on-premises architecture is safer for sensitive data. However, cloud providers invest heavily in security measures, including data encryption, access controls, and compliance certifications. Your data is stored in highly secure data centers, which reduces the risk of data breaches.  

How to Migrate?

Migrating to a modern data warehouse is a strategic process that requires careful planning and execution. Here’s a more detailed step-by-step guide for a successful migration: 

1. Assessment and Planning: Conduct a comprehensive assessment of your current data infrastructure, including databases, data sources, and processing systems. Identify pain points, scalability limitations, and areas that need improvement. Define clear objectives for the migration. Determine how the modern data warehouse aligns with your organization’s business goals. 
2. Select the Right Modern Data Warehouse: Research and select a modern data warehouse solution that best suits your organization’s needs. Consider factors such as scalability, integration capabilities, pricing, and data processing features. 
3. Data Profiling and Cleansing: Before migration, perform data profiling and cleansing to ensure data quality. Address issues such as duplicates, inconsistencies, and inaccuracies in your data. 
4. Data Mapping and Transformation: Map your existing data structures to the schema of the modern data warehouse. You may need to transform data to match the new schema requirements. 
5. Data Migration: Execute the data migration plan by extracting data from current systems, transforming it as needed, and loading it into the modern data warehouse. Use ETL (Extract, Transform, Load) tools where necessary. 
6. Integration with Data Sources: Configure data integration pipelines to establish smooth data flow from diverse sources into the modern data warehouse. Implement connectors and pipelines for automated data ingestion. 
7. Testing and Validation: Conduct rigorous testing to ensure the accuracy and completeness of the migrated data. Verify that the data warehouse processes queries and analytics correctly and maintains data integrity. 
8. Performance Optimization: Optimize the performance of the modern data warehouse by adjusting configurations, indexing, and query optimization. Ensure efficient query execution. 
9. Security and Access Control: Implement robust security measures, including role-based access controls, encryption, and compliance with data privacy regulations. Safeguard data in transit and at rest 

Build Your Data Warehouse with LIKE.TG Data Warehouse Builder 

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