Step 1-2: Analyze Source
1. Generate Documentation
Run full documentation, EDA (quality baseline), and Business Insights (criticality analysis).
2. Analyze State
Assess technical metrics (rows, types, indexes), data quality (completeness, duplicates), and business structure.
Step 3: Identify Challenges
Data Quality Issues
Issue: orders.customer_id has 234 orphaned records Impact: Need customer data cleanup before migration Action: Resolve orphaned records or delete
Schema Differences
Challenge: Source uses VARCHAR(MAX), target requires fixed lengths Impact: Need to determine appropriate VARCHAR sizes Action: Analyze max lengths from EDA
Step 4-5: Mapping & Planning
Migration Sequence
- Reference Data: Products, Categories, Static lookups
- Master Data: Customers, Suppliers, Employees
- Transactional Data: Orders, Payments, Shipments
- Historical Data: Logs, Archives, Audit Trails
Mapping Document Example
Source: customers Target: Customer Transform: - customer_id → CustomerId (INT → BIGINT) - email → EmailAddress (VARCHAR → VARCHAR) - phone → PhoneNumber (Standardize format) Dependencies: Must migrate before orders
Step 6: Validation
After migration, generate documentation for the target database and compare:
- Source vs Target Documentation comparison
- Verify all data migrated correctly (Row counts)
- Check relationship integrity
- Run EDA to ensure data quality is maintained
Success Criteria
- Complete source documentation
- Migration challenges identified
- Migration sequence planned
- Mapping document created
- Target validation plan ready