Cloud Migration Strategies for Data Platforms
A pragmatic approach to migrating on-premises data workloads to Azure—lessons from the trenches.
Migrating data platforms to the cloud is rarely a lift-and-shift. After supporting multiple migrations across insurance and manufacturing, I've learned that the right strategy depends on your constraints—and that incremental wins beat big-bang approaches.
Migration Approaches
1. Rehost (Lift and Shift)
Move workloads as-is with minimal changes. Good for quick wins when you need to decommission data centers fast. Use Azure Data Factory or Azure Migrate for data movement.
2. Refactor
Optimize for cloud-native services. Replace SSIS with ADF, SQL Server with Azure SQL or Synapse. Better long-term, but requires more planning and testing.
3. Rebuild
Design new architectures (e.g., lakehouse) and migrate data incrementally. Best when legacy systems are hindering growth. Highest effort, highest payoff.
Key Success Factors
Start with the Data
Map data flows, dependencies, and ownership before moving anything. Data lineage tools and discovery workshops reveal hidden couplings.
Prioritize by Value and Risk
Migrate high-value, low-risk workloads first. Use dual-running or parallel validation to ensure correctness before cutting over.
Conclusion
There's no single right path. Choose an approach that fits your timeline, budget, and talent. Document decisions, measure progress, and iterate. Cloud migration is a journey, not a project.
