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A Practical Guide to Database Migration: From Legacy Systems to Modern Infrastructure

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

  1. 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

  1. 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

  1. 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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