name: data-move description: Deep data migration workflow—scope, mapping, validation, batching and ordering, dual-write and cutover, rollback, and reconciliation. Use when moving tenants, bulk backfills, or changing stores without losing trust in data correctness.

Data Move

Data migration fails in silent corruption, ordering bugs, and unclear cutover. Treat it as ETL with production risk: explicit mapping, checkpoints, and reconciliation against sources of truth.

When to Offer This Workflow

Trigger conditions:

• Moving data between databases, regions, or tenants
• Large backfills after schema changes
• Zero or minimal downtime requirements

Initial offer:

Use seven stages: (1) scope & invariants, (2) source/target mapping, (3) batching & idempotency, (4) validation rules, (5) execution strategy (big bang vs phased), (6) cutover & rollback, (7) reconciliation & sign-off). Confirm volume, downtime budget, and compliance (PII, audit).

Stage 1: Scope & Invariants

Goal: Define what moves, what must never diverge, and ordering dependencies (foreign keys, references).

Questions

1. Cutover moment: read-only window vs dual-write?
2. Immutable identifiers: preserve primary keys or remap with mapping tables?
3. Deletes: soft-delete vs hard-delete semantics in target

Exit condition: Written invariants (e.g., “every migrated row has legacy_id for traceability”).

Stage 2: Source/Target Mapping

Goal: Field-level mapping document; transforms (timezone, encoding, rounding); defaults for nulls.

Practices

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• Surrogate keys generated deterministically or via mapping table
• Document one-way vs bi-directional sync if any

Stage 3: Batching & Idempotency

Goal: Jobs restartable; same input yields same output (idempotent writes or upsert keys).

Practices

• Checkpoint by primary key or updated_at watermark
• Throttle to protect source and target DB

Stage 4: Validation Rules

Goal: Row counts, checksums, sample joins, business invariants (sums, balances).

Practices

• Shadow compare: run parallel queries on old vs new for critical aggregates

Exit condition: Validation checklist signed before cutover.

Stage 5: Execution Strategy

Goal: Phased by tenant/region vs single window—risk vs complexity trade-off.

Patterns

• Dual-write then backfill then flip reads
• Blue/green tables with rename swap

Stage 6: Cutover & Rollback

Goal: Runbook: who flips DNS/config, order of steps, rollback triggers (error rate, failed checks).

Practices

• Feature flags for read path to new store
• Keep rollback script tested in staging

Stage 7: Reconciliation & Sign-off

Goal: Post-cutover 24–72h monitoring; reconciliation job scheduled; support playbook for edge cases.

Final Review Checklist

• [ ] Invariants and mapping documented
• [ ] Idempotent batches with checkpoints
• [ ] Validation and shadow checks passed
• [ ] Cutover/rollback runbook tested
• [ ] Reconciliation after go-live

Tips for Effective Guidance

• Never assume “batch job finished” = correct—prove with checks.
• Clock skew and timezone bugs are classic—call them out in transforms.
• Pair with db-migrate for schema timing vs data movement.

Handling Deviations

• Small one-off SQL: still document mapping and run counts before/after.