Migrating databases is one of those projects that looks simple on paper and reveals all its complexity only when you're in the middle of it. Here's a guide based on common patterns and pitfalls.
When to Migrate
Not every legacy database needs migration. Signs you should consider moving:
Vendor is sunsetting your version
Licensing costs are unsustainable
You're hitting scaling limits that can't be solved vertically
Your team can't hire for the specific technology anymore
Migration Strategies
- Lift and Shift Move the database to managed infrastructure with minimal changes. Example: self-hosted PostgreSQL 11 → RDS PostgreSQL 15.
Pros: Fast, low risk
Cons: You're still on the same old architecture
- Re-platform Make moderate changes to take advantage of cloud features. Example: self-hosted MySQL → Amazon Aurora MySQL.
Pros: Better performance without full rewrite
Cons: Some code changes required
- Refactor / Re-architect Full rewrite. Move from legacy relational to modern distributed database. Example: Oracle → MongoDB + microservices
Pros: Modern architecture, long-term maintainability
Cons: Expensive, risky, time-consuming
Step-by-Step Process
Phase 1: Assessment
Audit current data volume, transaction rates, dependencies
Identify hardcoded queries and vendor-specific features in use
Map all applications that connect to the database
Document SLAs you need to maintain
Phase 2: Choose Target
PostgreSQL for general purpose, strong consistency
MongoDB for document-heavy workloads, flexible schemas
DynamoDB for serverless, predictable scaling
ClickHouse or Snowflake for analytics-heavy workloads
Phase 3: Schema Migration
Generate DDL scripts from source
Test on small dataset first
Handle data type conversions (DATE vs DATETIME vs TIMESTAMP is a common trap)
Index strategy: recreate existing indexes, add new ones based on query patterns
Phase 4: Data Migration
Full dump/restore for databases under 100GB
For larger: use CDC (Change Data Capture) tools like Debezium
Always have rollback plan
Migrate in off-peak hours
Phase 5: Application Changes
Update connection strings
Test connection pooling
Run parallel reads/writes in shadow mode if possible
Enable query logging to catch issues early
Common Pitfalls
Character encoding mismatches — UTF-8 vs Latin1 causes data loss
Timezone handling — Always store UTC, convert at application layer
Index differences — What worked on MySQL may not work the same on PostgreSQL
Query plan differences — Same query can have dramatically different execution plans
Transaction isolation levels — Different defaults across databases
Testing Checklist
Data integrity: row count matches, no truncation
Character data: special characters, emojis render correctly
Numeric precision: no rounding or truncation in decimals
Date/time: timezone handling correct
Indexes: recreated and used by query planner
Stored procedures/functions: ported and tested
Performance: query times acceptable on new platform
Backup/restore: tested on fresh instance
Post-Migration Monitoring
Monitor for 2-4 weeks:
Query performance degradation
Connection pool exhaustion
Replication lag (if applicable)
Application error rates
User-reported data issues
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