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The RDBMS Split Process: A Practical Guide to Streamlining the Transition to Data Warehouses

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Read more about author Ohad Sheory.

In the first part of this series, we explored how harmonizing relational database management systems (RDBMS) with data warehouses (DWH) can drive scalability, efficiency, and advanced analytics. We discussed the importance of aligning these systems strategically to balance their unique strengths while avoiding unnecessary complexity.

In this installment, we tackle a challenge many organizations face: fostering the smooth transition from an RDBMS only to a hybrid system that incorporates both RDBMS and DWH. This process is critical for unlocking a data ecosystem’s full potential but comes with its fair share of risks – data quality, correctness, downtime, and synchronization hurdles. This article outlines a roadmap to streamline the migration, minimize disruptions, and establish a scalable foundation for future growth.

Phase 1: Planning the Data Split

A smooth transition begins with thoughtful planning. Deciding where each piece of data belongs is crucial for a seamless split:

  1. Categorize Data: Start by dividing data into categories, such as “customer-oriented” (high-volume) and “non-customer-oriented.” This categorization guides migration priorities.
  2. Minimize Dependencies: Analyze table JOINs to identify and minimize cross-group dependencies, simplifying synchronization between the RDBMS and DWH.
  3. Select Migration Methods:
  • For static and fully updated tables, a “lift-and-shift” approach works best.
  • For dynamic tables, employ change data capture (CDC) to ensure real-time updates.
  • Wherever possible, halt updates to the RDBMS post-sync and update DWH only as the new source of truth for that data silo.

Phase 2: Migration and Verification

Executing the migration requires a careful, step-by-step approach:

  1. Isolate Data Groups: Organize and migrate data in manageable groups to reduce complexity and risks.
  2. Adapt Workloads: Update applications to work with the data within the DWH, incorporating feature flags for easy rollback if needed.
  3. Incremental Migration: Migrate one workload at a time using the “One Change at a Time” (OCAT) principle to simplify troubleshooting.
  4. Shadow Mode Testing: Run parallel workloads on the DWH (test) and RDBMS (prod) to validate correctness, data consistency, functionality, and performance. Address issues such as resource conflicts, and sync delays during this phase. Compare the results of test and prod runs for each workload.
  5. Optimize Later: Migrate the existing functionality first as is, then optimize incrementally. This ensures stability and simplifies identifying issues or gaps.

Phase 3: Deployment and Optimization

Deployment is where preparation meets execution, with automation playing a key role:

  1. Automate Everything: Use infrastructure as code (IaC) and tested scripts to minimize manual errors.
  2. Phased Rollouts: To reduce risks, employ strategies like blue-green or canary deployments. In multi-tenant environments, deploy to low-risk tenants first and gradually enroll the rest.
  3. Leverage DWH Features: After migration, explore advanced DWH capabilities like columnar storage, real-time analytics, native machine learning, dynamic table/continuous queries, scalability options, cost optimization, etc. These features can dramatically improve performance and enable new insights.

Conclusion: A Clear Path Forward

While splitting an RDBMS to migrate to a DWH can seem daunting, a structured approach significantly reduces the risks. With careful planning, step-by-step migration, and strategic deployment, you can establish a scalable data ecosystem that powers innovation and growth. The process doesn’t end with migration; rather, this is the foundation for continuous improvement and optimization.

Stay tuned for the next part of this series, where we’ll focus on optimizing workflows and unlocking the full potential of your DWH.