Database Migration Projects | Zero-Downtime Guide

Database migrations are high-risk projects requiring careful coordination. GitScrum tracks migration phases, checkpoints, rollback plans, and cross-team communication.

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Database migrations are high-risk projects requiring careful coordination between teams and meticulous tracking of dependencies. GitScrum's milestone tracking, checklist features, and cross-team visibility help organizations plan migrations systematically, track progress against critical checkpoints, and maintain clear communication throughout the process.

Migration Project Phases

Phase	Activities	Duration
Assessment	Schema analysis, size estimation, risk identification	1-2 weeks
Planning	Migration strategy, testing plan, rollback procedures	1-2 weeks
Development	Scripts, tools, application updates	2-4 weeks
Testing	Data validation, performance testing, DR testing	2-4 weeks
Migration	Execute migration, validation, cutover	1-3 days
Post-Migration	Monitoring, cleanup, documentation	1 week

Migration Project Structure

DATABASE MIGRATION EPIC STRUCTURE

Epic: Migrate from MySQL to PostgreSQL
├── Phase 1: Assessment
│   ├── Schema inventory and analysis
│   ├── Data volume assessment
│   ├── Application dependency mapping
│   ├── Risk identification
│   └── Stakeholder communication plan
│
├── Phase 2: Planning
│   ├── Choose migration strategy
│   ├── Design schema mapping
│   ├── Create test data sets
│   ├── Define validation criteria
│   └── Create rollback procedures
│
├── Phase 3: Development
│   ├── Schema migration scripts
│   ├── Data transformation scripts
│   ├── Application dual-write support
│   ├── Feature flags for database
│   └── Monitoring and alerting
│
├── Phase 4: Testing
│   ├── Migrate staging environment
│   ├── Data validation tests
│   ├── Performance benchmarking
│   ├── Application integration tests
│   └── Disaster recovery test
│
├── Phase 5: Migration Execution
│   ├── Final production backup
│   ├── Execute migration
│   ├── Data validation
│   ├── Application cutover
│   └── Performance verification
│
└── Phase 6: Post-Migration
    ├── Extended monitoring
    ├── Old database decommission
    ├── Documentation update
    └── Lessons learned

Migration Strategy Options

STRATEGY SELECTION

1. BIG BANG MIGRATION
┌─────────────────────────────────────────────────┐
│  Schedule downtime                              │
│  Migrate all data at once                       │
│  Cut over application                           │
│                                                 │
│  Pros: Simpler, single point of change          │
│  Cons: Downtime required, high risk             │
│  Best for: Small datasets, acceptable downtime  │
└─────────────────────────────────────────────────┘

2. DUAL-WRITE MIGRATION
┌─────────────────────────────────────────────────┐
│  Write to both databases simultaneously         │
│  Migrate historical data in background          │
│  Gradually shift reads to new database          │
│  Disable writes to old database                 │
│                                                 │
│  Pros: Zero downtime, gradual rollout           │
│  Cons: Complex implementation, consistency      │
│  Best for: High-availability requirements       │
└─────────────────────────────────────────────────┘

3. FEATURE FLAG MIGRATION
┌─────────────────────────────────────────────────┐
│  New features use new database                  │
│  Old features continue on old database          │
│  Gradually migrate features                     │
│  Decommission old database when empty           │
│                                                 │
│  Pros: Low risk, parallel operation             │
│  Cons: Longer timeline, complexity              │
│  Best for: Large migrations, risk-averse        │
└─────────────────────────────────────────────────┘

Validation Checklist

DATA VALIDATION CHECKLIST

PRE-MIGRATION:
□ Row counts match between source and target
□ Schema mapping verified
□ All data types correctly converted
□ Foreign key relationships preserved
□ Indexes created and optimized

POST-MIGRATION:
□ Sample record comparison (1% or 1000 records)
□ Aggregate validation (sums, counts)
□ Edge cases verified (nulls, special chars)
□ Date/time conversions correct
□ Application queries return expected results

PERFORMANCE:
□ Query performance meets baseline
□ Write performance acceptable
□ Index usage verified
□ Connection pooling configured
□ No unexpected locks or blocking

APPLICATION:
□ All CRUD operations working
□ Transactions behaving correctly
□ Error handling for new error types
□ Logging shows correct database calls
□ Monitoring shows healthy metrics

Risk Mitigation

MIGRATION RISK MATRIX

Risk                        Mitigation
─────────────────────────────────────────────────
Data loss                   Backups before each step
                           Validation at each stage
                           Dry run on staging

Performance regression      Benchmark before/after
                           Index optimization
                           Query analysis

Application failures        Dual-write testing
                           Feature flags
                           Rollback procedures

Extended downtime          Time-box migrations
                           Practice runs
                           Rollback triggers

Missed data               Full schema inventory
                          Dependency tracking
                          Validation scripts

Best Practices

Practice migration multiple times on staging

Validate data at every step

Have clear rollback triggers and procedures

Communicate broadly about timeline and risks

Monitor aggressively during and after migration

Keep old database available for 1-2 weeks

Document decisions and deviations

Run post-mortem even if successful

Anti-Patterns

✗ Big bang migration without practice
✗ No rollback plan
✗ Skipping validation steps
✗ Single person owns entire migration
✗ Migrating during peak traffic
✗ Decommissioning old DB immediately