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:
- Maps source → target schemas with field-level transformation rules
- Generates an ETL pipeline plan with staging, transform, and load phases
- Creates validation queries (row counts, checksum, referential integrity)
- Builds a rollback plan with point-of-no-return criteria
- 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:
- Executive summary (what, why, when, risk level)
- Schema mapping tables
- ETL pipeline specification
- Validation test suite
- Cutover runbook with rollback
- 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
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1---2name: data-migration-planner3description: Plan, execute, and validate data migrations between systems. Covers schema mapping, ETL pipeline design, rollback strategies, and post-migration validation.4---5
6# Data Migration Planner
7
8Plan, execute, and validate data migrations between systems. Covers schema mapping, ETL pipeline design, rollback strategies, and post-migration validation.
9
10## What It Does
11
12Given source and target system details, this skill:
131. Maps source → target schemas with field-level transformation rules
142. Generates an ETL pipeline plan with staging, transform, and load phases
153. Creates validation queries (row counts, checksum, referential integrity)
164. Builds a rollback plan with point-of-no-return criteria
175. Produces a migration runbook with go/no-go gates
18
19## Usage
20
21Tell your agent:
22- "Plan a migration from Salesforce to HubSpot CRM"
23- "Create a data migration runbook for moving from MySQL to PostgreSQL"
24- "Map our legacy ERP data to the new system schema"
25
26## Migration Framework
27
28### Phase 1: Discovery
29- Inventory all source tables/objects and record counts
30- Document data types, constraints, and relationships
31- Identify data quality issues (nulls, duplicates, orphans)
32- Map business rules that affect data interpretation
33
34### Phase 2: Schema Mapping
35For each source entity, document:
36| Source Field | Type | Target Field | Type | Transform | Notes |
37|---|---|---|---|---|---|
38| (field) | (type) | (field) | (type) | (rule) | (edge cases) |
39
40### Phase 3: ETL Pipeline
41```
42Extract → Stage (raw) → Clean → Transform → Validate → Load → Verify
43```
44- **Extract**: Full vs incremental, API vs direct DB, rate limits
45- **Stage**: Raw landing zone, no transforms, audit trail
46- **Clean**: Dedup, null handling, encoding fixes
47- **Transform**: Type conversions, lookups, calculated fields
48- **Validate**: Pre-load checks (counts, checksums, business rules)
49- **Load**: Batch size, parallelism, error handling
50- **Verify**: Post-load reconciliation
51
52### Phase 4: Validation
53- Row count match (source vs target, per table)
54- Checksum validation on key columns
55- Referential integrity checks
56- Business rule validation (e.g., all active accounts migrated)
57- User acceptance sampling (random 5% manual review)
58
59### Phase 5: Cutover
60- Go/no-go criteria checklist
61- Point-of-no-return definition
62- Rollback procedure and time estimate
63- Communication plan (users, stakeholders)
64- Parallel run period (if applicable)
65
66## Risk Factors
67- **Data volume**: >10M rows = batch strategy required
68- **Downtime window**: Zero-downtime needs CDC/dual-write
69- **Data quality**: Garbage in = garbage out. Clean BEFORE migrating
70- **Dependencies**: Other systems reading from source during migration
71- **Compliance**: GDPR/HIPAA data handling during transit
72
73## Output Format
74Deliver a migration runbook as structured markdown with:
751. Executive summary (what, why, when, risk level)
762. Schema mapping tables
773. ETL pipeline specification
784. Validation test suite
795. Cutover runbook with rollback
806. Timeline with milestones
81
82## Cost Estimation
83Typical migration costs by complexity:
84- Simple (1-5 tables, <1M rows): $5K-$15K or 1-2 weeks internal
85- Medium (10-50 tables, 1-10M rows): $25K-$75K or 1-2 months
86- Complex (50+ tables, 10M+ rows, multiple systems): $100K-$500K or 3-6 months
87
88---
89
90**Built by [AfrexAI](https://afrexai-cto.github.io/context-packs/)** — AI Context Packs for business automation.
91
92Calculate your AI automation ROI: [Revenue Calculator](https://afrexai-cto.github.io/ai-revenue-calculator/)