# Data Migration

> Data Migration

- Skill: `comeonoliver/data-migration` (Agent Skill)
- Install (CLI): `npx skillmds@latest add comeonoliver/data-migration`
- Raw SKILL.md: https://api.skillmd.com/api/skills/comeonoliver/data-migration/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: ComeOnOliver (https://skillmd.com/u/comeonoliver)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/comeonoliver/data-migration

---

# Data Migration

## Overview

Builds automated data migration pipelines between databases. Handles schema analysis and mapping, type conversions, data transformations, dependency-ordered table loading, batch processing for large datasets, checkpoint/resume for reliability, post-migration validation, and cutover planning. Produces repeatable scripts that can be dry-run against staging before production.

## Instructions

### 1. Schema Analysis

Start every migration by analyzing source and target:

```
For each table in source:
  - Column names, types, nullability, defaults
  - Primary keys and auto-increment sequences
  - Foreign key relationships (build dependency graph)
  - Indexes and unique constraints
  - Row count estimate (for batch sizing)
  - Encoding/collation (especially for MySQL → PostgreSQL)
```

Generate a schema map document listing every column with its source type, target type, and any transformation needed.

### 2. Type Mapping

Common cross-database type conversions:

| MySQL | PostgreSQL | Notes |
|-------|-----------|-------|
| TINYINT(1) | BOOLEAN | Map 0/1 to false/true |
| ENUM('a','b') | VARCHAR + CHECK | Or create custom TYPE |
| DATETIME | TIMESTAMPTZ | Add timezone info |
| INT AUTO_INCREMENT | SERIAL | Reset sequence after migration |
| DOUBLE | DOUBLE PRECISION | Direct mapping |
| BLOB | BYTEA | Binary data |
| TEXT (latin1) | TEXT (UTF-8) | Re-encode characters |
| JSON | JSONB | Use binary JSON in PG |

### 3. Dependency Resolution

Build a directed acyclic graph from foreign keys:

```
1. Parse all FK constraints → build adjacency list
2. Topological sort → migration order
3. Circular dependencies: temporarily drop FK, migrate, re-add FK
4. Self-referencing tables: migrate in two passes (data, then self-FK updates)
```

### 4. Batch Processing

For tables with more than 10,000 rows:

```
function migrateLargeTable(table, batchSize = 5000):
  lastId = loadCheckpoint(table) or 0
  while true:
    rows = SELECT * FROM source.table WHERE id > lastId ORDER BY id LIMIT batchSize
    if rows.empty: break
    transformed = rows.map(row => transform(row, table.mapping))
    INSERT INTO target.table VALUES transformed
    lastId = rows.last.id
    saveCheckpoint(table, lastId, totalMigrated)
```

Performance targets:
- 5,000 rows/batch for most tables
- 1,000 rows/batch for tables with BLOB/TEXT columns
- Disable target indexes during bulk load, rebuild after

### 5. Validation

Post-migration validation checklist:

```
1. Row counts: source vs target for every table
2. Random sampling: 100 random rows per table, field-by-field comparison
3. Aggregate checks: SUM, COUNT, MIN, MAX on numeric columns
4. Referential integrity: all FKs resolve (no orphans)
5. Encoding: sample text fields for valid UTF-8
6. Sequences: verify auto-increment/serial values set above max ID
7. Nullability: no unexpected NULLs in NOT NULL target columns
```

### 6. Cutover Planning

Three strategies by downtime tolerance:

**Full downtime** (simplest): Stop app → migrate → validate → start app. For small datasets (< 1M rows, < 1 hour).

**Minimal downtime** (recommended): Pre-migrate bulk data → set up change capture → maintenance mode → apply delta → switch → validate. Downtime: 2-10 minutes.

**Zero downtime** (complex): Dual-write to both databases → background migration → gradual read traffic shift → drop old writes. Requires application changes.

## Examples

### Example 1: MySQL to PostgreSQL

**Prompt**: "Migrate our MySQL 5.7 database to PostgreSQL 16. 30 tables, biggest is 5M rows."

**Output**: Schema mapping JSON, type conversion DDL, migration script with dependency ordering, batch processing for large tables, checkpoint file, validation suite, and cutover runbook.

### Example 2: Database Consolidation

**Prompt**: "Merge two SQLite databases into one PostgreSQL. Some tables overlap with different schemas."

**Output**: Schema diff report, merge strategy document (which columns win conflicts), deduplication logic using configurable match keys, migration script, and conflict resolution log.

## Guidelines

- **Always dry-run on staging first** — never run migration directly against production
- **Keep source untouched** — migration should be read-only on source until cutover
- **Checkpoint everything** — large migrations will fail; resumability is required
- **Validate before cutover** — automated validation catches what manual spot-checks miss
- **Plan rollback** — if target validation fails, have a documented path back to source
- **Log extensively** — rows processed, rows skipped, transformation errors, timing
- **Reset sequences** — after migration, set serial/auto-increment above max migrated ID
- **Test with production volume** — a script that works on 1000 rows may OOM on 5M