Addressing Key Challenges in Centralizing Data Warehousing Solutions

This article is part of a series coauthored by Prince Kher and Amit Kharche dedicated to exploring and addressing the challenges faced in the data warehousing domain. By delving into these issues, we aim to offer solutions that organizations can utilize to improve their design decisions, data infrastructure, and tooling.

In order to build centralized data architecture, organizations are increasingly focused on developing centralized data warehouses to extract valuable insights and support informed decision-making. Despite these efforts, many initiatives fail to meet their goals due to issues with scalability, architectural inefficiencies, and the need to adapt to evolving business requirements.

In this article, we want to address the scalability concerns associated with traditional data warehouses and discuss strategies to mitigate these challenges.

Problem: The scalability bottleneck in traditional data Warehouses built on RDBMS technology

As organizations embark on their journey to build robust data warehouses, they encounter significant challenges in managing scalability and efficiency. Solutions that perform well initially often fail to scale as data volume increases. Traditional data warehouse architectures, which rely on tightly coupled compute and storage, struggle to keep pace with growing data volumes and can become more and more expensive for incremental data growth.

In such implementation, you can observe the data ingestion, data consumption capability is delivered via the same storage and compute node. Traditional architectures replying on RDBMS technology often struggle to balance these two critical processes, leading to inefficiencies and performance bottlenecks. To overcome these limitations, organizations are increasingly exploring decoupled architectures that separate data ingestion and consumption, separate compute and storage, allowing for more scalable, cost-effective, and flexible data warehousing solutions.

This article examines different approaches to address scalability concerns.

Solution 1: Separation of Read and Write Loads within Same Database

One approach to address scalability is separating read and write operations within the same compute and storage infrastructure. This can involve using read replicas or distinct layers for transformation and querying.

Pros:

Concurrent Processes: Operations like reporting and transactional updates can occur simultaneously without contention.

Improved Performance: Read-heavy workloads, such as analytical dashboards, remain unaffected by ongoing write operations.

Cons:

Cost: Additional replicas increase compute resource requirements and storage overhead.

Preparation Time: Deploying and maintaining replicas or layered infrastructure demands dedicated engineering efforts. 

Latency: Depending on data volume, there can be significant latency in refreshing read replicas.

Implementation example: Read replicas can be created in Postgres, Oracle and MySQL Server etc.

Solution 2: Separation of Read and Write, Data Replica Created Outside of Database

This solution is a special case of implementation mentioned in the solution 1, but read replicas are created outside of database e.g. read replicas are created in parquet files on storage solution such as ADLS, Blob etc. The (heavy) analytical queries run on top of Parquet files in Spark cluster or ETL tools like Azure data factory or any other scripting environment.

Implementation example: Mirroring of Postgres Flexible instance into Parquet files, or manually creating Parquet files on storage solution like ADLS and Azure Blob store.

Pros:

Cheaper Data storage: Data replicas stored on ADLS or Blob storage are cheaper compared to data storage solution within database.

Data Compression Possibility: Data compression of Parquet format can offer benefits of reduced network IO, data storage cost.

Cons:

Preparation Time: Deploying and maintaining replicas or layered infrastructure demands dedicated engineering efforts. 

Latency: Depending on data volume, there can be significant latency in refreshing read replicas.

 Solution 3: Decoupling Compute and Storage into Separate Modules

Decoupling compute from storage involves storing data in scalable, compressed formats such as Delta Lake or Apache Iceberg on platforms like Azure Data Lake Storage (ADLS), Amazon S3, or Google Cloud Storage (GCS). And Compute-intensive tasks like ELT/ETL are handled externally using tools like DBT, Azure Data Factory, or AWS Glue.

This separation allows flexibility and cost efficiency to scale storage and compute capability independently.  

Implementation example: Microsoft Fabric, Google Big query, Databricks and Snowflake

Pros:

Elastic Resource Allocation: Heavy ETL loads can be handled externally with compute resources allocated on-demand.

Cost Efficiency: Compressed storage formats reduce data footprint, while elastic compute minimizes idle costs.

Concurrent Operations: File-based systems like Delta Lake allow simultaneous read and write access, enhancing performance.

Cons:

Operational Complexity: Managing orchestration and data movement between storage and compute layers requires specialized tools and expertise.

Migration Costs: Transitioning from traditional architectures to decoupled systems involves initial setup investment.

Comparison of Approaches

Key Takeaway: In today’s rapidly evolving digital landscape, organizations must future-proof their data architectures to stay competitive. The first step in this journey is a thorough assessment of the current infrastructure to identify scalability bottlenecks. Addressing these challenges can be effectively achieved by decoupling storage and compute into separate infrastructure modules. This strategic separation allows each component to scale independently based on specific requirements, ensuring a more flexible and efficient system. By adopting this approach, organizations can enhance their data management capabilities and drive analytical excellence.

Conclusion: Several products, such as Microsoft Fabric, Databricks, Snowflake, and Google BigQuery, exemplify this approach. These solutions enable organizations to build modern data architectures that effectively separate storage and compute, addressing scalability and performance issues.

Stay tuned for our follow-up articles, where we will delve deeper into how these solutions solve the problem of storage and compute in building modern data architectures.

Note: This article is not endorsement of any particular solution.

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