Isolate Data Groups: Organize and migrate data in manageable groups to reduce complexity and risks.
Adapt Workloads: Update applications to work with the data within the DWH, incorporating feature flags for easy rollback if needed.
Incremental Migration: Migrate one workload at a time using the “One Change at a Time” (OCAT) principle to simplify troubleshooting.
Shadow Mode Testing: Run parallel workloads philippines rcs data 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.
Optimize Later: Migrate the existing functionality first as is, then optimize incrementally. This ensures stability and simplifies identifying issues or gaps.
Phase Deployment and Optimization with automation playing a key role:
Automate Everything: Use infrastructure as code (IaC) and tested scripts to minimize manual errors.
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.
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.