Data Migration Workflow: Schema Mapping, Validation and Cutover Without the Spreadsheet
Migrations fail on mapping and validation, not on moving bytes. The workflow profiles the source, proposes the field mapping with an AI agent that reads both schemas and sample values, lets a person confirm it, applies transformation rules, validates on samples with a reconciliation report, dry-runs, and performs the cutover in batches with a rollback path. The spreadsheet of field mappings becomes a versioned artefact and the migration becomes repeatable.
Written by Max Zeshut
Founder at Agentmelt · Last updated Sep 11, 2026
The problem
Mapping 300 fields between two systems is done in a spreadsheet by one person, transformations are ad-hoc scripts, validation is eyeballing, and cutover is a weekend with no way back.
What changes when it runs
A confirmed mapping with every field's transformation and validation rule, a dry run that reconciles record counts and sums, a batch cutover with progress and error reports, and a rollback plan that was tested. The migration runs on schedule; the team spends its time on the exceptions the validation found.
Trigger, then 8 steps
Trigger
Manual trigger per phase
Each phase — profile, map, validate, dry-run, cutover — starts on an explicit click; no phase runs unattended.
Profile the source
PostgresTables, fields, types, null rates, distinct values and sample rows from the source system (database, CRM export, API).
Propose the mapping
AI AgentReads source and target schemas with sample values and proposes field-by-field mappings with transformation notes (date formats, picklist translation, splits/merges) and a confidence per field.
Confirm the mapping
Google SheetsThe mapping sheet is reviewed and edited by the migration owner; it becomes the versioned source of truth for every later step.
Apply transformations
CodeTransformations generated from the mapping sheet — type conversions, lookups, deduplication rules — applied to a sample batch.
Validate
Compare DatasetsRecord counts, sums of numeric fields, referential integrity and picklist coverage compared between source and transformed target sample; failures listed per rule.
Dry run
HTTP RequestFull transformed dataset loaded into a target sandbox; reconciliation report produced; errors per batch.
Cut over in batches
Loop Over ItemsProduction load in batches with progress, per-record error capture and retry; the source stays read-only during cutover.
Reconcile and hand over
Google DocsFinal reconciliation report, error log, the mapping sheet and the rollback procedure archived; the workflow can re-run deltas for late data.
Data it touches
- Source system (database, CRM/helpdesk export or API)
- Target system API or import
- Mapping sheet (owner-confirmed)
- Validation rules
Guardrails
- No phase runs without an explicit trigger; cutover requires a dry run that passed.
- The mapping is confirmed by a person; the agent proposes with confidence, it does not decide.
- The source is read-only during cutover and a tested rollback exists before the first production batch.
- Every transformed record carries its source id for traceability.
The mapping sheet is the deliverable
A migration's risk is concentrated in the mapping: which field goes where, how it is transformed, what happens to values that do not fit. The agent's proposal — reading both schemas and real sample values — gets a 300-field mapping to a reviewable draft in minutes, with confidence per field so the owner knows where to look. Once confirmed, everything else (transformations, validation, load) is generated from the sheet, which is why the migration becomes repeatable and the sheet is the artefact that matters.
Validate on samples, then reconcile the whole
Sample validation catches mapping errors cheaply; the dry-run reconciliation — counts, sums, integrity — catches what the sample missed. Cutover only starts when the dry run passes. This ordering is standard in data engineering and rare in business-system migrations, which is why those go wrong.
Tools in the stack
| Tool | Role in this workflow |
|---|---|
| n8n | Profiling, transformation, validation, batch loading |
| Claude | Mapping proposals from schemas and samples |
| Postgres / BigQuery | Staging and reconciliation |
| Target APIs (HubSpot, Salesforce, Zendesk…) | Loading |
Want this running without building it?
Automation workflow
$297/month
We set up, host and maintain this workflow on n8n and connect it to your tools. Setup included, cancel monthly, you keep the JSON.
Custom build
$5,000–12,000 one-time
Your systems, your rules, your edge cases. A one-off build on Claude and n8n, delivered with documentation and a walkthrough.
Migrations are project work; the subscription price applies to teams running repeated migrations (agencies, onboarding teams). One-off migrations are scoped by object count and complexity.
Frequently asked questions
Which systems can it migrate between?
Anything with an export and an import or API: CRMs (Salesforce, HubSpot, Pipedrive), helpdesks (Zendesk, Intercom, Freshdesk), databases, and spreadsheets. Legacy systems without exports need a custom extraction step.
Does it migrate attachments and history?
Yes — activities, notes, tickets and attachments are handled as related objects with their own mapping and integrity checks, which is where most migration effort goes.
Can it run a delta after go-live?
Yes — the same mapping applied to records changed since a timestamp, for a parallel-run period or late data.
Case study
AI Coding Agent for Enterprise Migration: 60% Faster Legacy Java to Kotlin Conversion
How a fintech company used an AI coding agent to migrate 180K lines of Java to Kotlin, completing in 8 weeks instead of the estimated 20 weeks with manual conversion.
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