data-migration-planner

# Data Migration Planner Plan, execute, and validate data migrations between systems. Covers schema mapping, ETL pipeline design, rollback strategies, and post-migration validation. ## What It Does Given source and target system details, this skill: 1. Maps source → target schemas with field-level transformation rules 2. Generates an ETL pipeline plan with staging, transform, and load phases 3. Creates validation queries (row counts, checksum, referential integrity) 4. Builds a rollback plan with point-of-no-return criteria 5. Produces a migration runbook with go/no-go gates ## Usage Tell your agent: - "Plan a migration from Salesforce to HubSpot CRM" - "Create a data migration runbook for moving from MySQL to PostgreSQL" - "Map our legacy ERP data to the new system schema" ## Migration Framework ### Phase 1: Discovery - Inventory all source tables/objects and record counts - Document data types, constraints, and relationships - Identify data quality issues (nulls, duplicates, orphans) - Map business rules that affect data interpretation ### Phase 2: Schema Mapping For each source entity, document: | Source Field | Type | Target Field | Type | Transform | Notes | |---|---|---|---|---|---| | (field) | (type) | (field) | (type) | (rule) | (edge cases) | ### Phase 3: ETL Pipeline ``` Extract → Stage (raw) → Clean → Transform → Validate → Load → Verify ``` - **Extract**: Full vs incremental, API vs direct DB, rate limits - **Stage**: Raw landing zone, no transforms, audit trail - **Clean**: Dedup, null handling, encoding fixes - **Transform**: Type conversions, lookups, calculated fields - **Validate**: Pre-load checks (counts, checksums, business rules) - **Load**: Batch size, parallelism, error handling - **Verify**: Post-load reconciliation ### Phase 4: Validation - Row count match (source vs target, per table) - Checksum validation on key columns - Referential integrity checks - Business rule validation (e.g., all active accounts migrated) - User acceptance sampling (random 5% manual review) ### Phase 5: Cutover - Go/no-go criteria checklist - Point-of-no-return definition - Rollback procedure and time estimate - Communication plan (users, stakeholders) - Parallel run period (if applicable) ## Risk Factors - **Data volume**: >10M rows = batch strategy required - **Downtime window**: Zero-downtime needs CDC/dual-write - **Data quality**: Garbage in = garbage out. Clean BEFORE migrating - **Dependencies**: Other systems reading from source during migration - **Compliance**: GDPR/HIPAA data handling during transit ## Output Format Deliver a migration runbook as structured markdown with: 1. Executive summary (what, why, when, risk level) 2. Schema mapping tables 3. ETL pipeline specification 4. Validation test suite 5. Cutover runbook with rollback 6. Timeline with milestones ## Cost Estimation Typical migration costs by complexity: - Simple (1-5 tables, <1M rows): $5K-$15K or 1-2 weeks internal - Medium (10-50 tables, 1-10M rows): $25K-$75K or 1-2 months - Complex (50+ tables, 10M+ rows, multiple systems): $100K-$500K or 3-6 months --- **Built by [AfrexAI](https://afrexai-cto.github.io/context-packs/)** — AI Context Packs for business automation. Calculate your AI automation ROI: [Revenue Calculator](https://afrexai-cto.github.io/ai-revenue-calculator/)