Migrating to NoSQL
Transitioning from a relational database to NoSQL requires careful planning and a shift in thinking about data modeling.
Phase 1: Assessment
Analyze Your Data
Understand your current data model, relationships, and access patterns
Identify Pain Points
What problems are you trying to solve? Scale? Flexibility? Performance?
Choose the Right NoSQL Type
Document? Key-Value? Graph? Column-family? Match your use case
Phase 2: Data Modeling
Key Difference: Model for Queries, Not Normalization
In NoSQL, design your data model based on how you'll query it, not on eliminating redundancy.
Common Patterns:
- Embedding related data (denormalization)
- Using references for large or frequently updated data
- Duplicating data to optimize read performance
- Creating aggregated views for common queries
Phase 3: Migration Strategy
1. Strangler Pattern (Recommended)
Gradually replace parts of your system, allowing both databases to coexist.
2. Parallel Run
Write to both databases simultaneously, compare results, then switch reads.
3. Big Bang (Risky)
Complete migration at once. Only for small datasets or non-critical systems.
Phase 4: Testing
- Load testing with production-like data volumes
- Query performance benchmarking
- Data integrity validation
- Failover and recovery testing
- Application integration testing
Common Pitfalls to Avoid
❌ Trying to replicate relational patterns
Don't force JOINs and normalization into NoSQL
❌ Not considering consistency requirements
Understand eventual consistency trade-offs
❌ Migrating everything at once
Start with non-critical components
Pro Tip
Consider a polyglot persistence approach - use SQL for transactional data and NoSQL for specific use cases where it excels.