A migration can complete every copy task and still deliver an unusable target. Equal row counts do not detect changed values, duplicate business keys, truncated strings, timezone shifts, missing relationships, incorrect transformations, out-of-order changes, or dashboards that no longer reconcile.
AI can accelerate test design and mismatch triage, but deterministic checks must produce the evidence. The migration team needs a versioned validation contract, equivalent source and target states, defined tolerances, exception ownership, and a cutover authority who can stop the release.
Will the first complete reconciliation happen during the cutover window? Datrick can assess one migration domain, build layered checks, automate exception evidence, and create a repeatable dry-run and cutover workflow.
Define the migration validation contract
| Validation layer | Examples | Failure detected |
|---|---|---|
| Structure | Object inventory, schema, type, length, precision, nullability, default, key, index, partition, permission, and encoding. | Missing or incompatible target design. |
| Completeness | Rows, files, bytes, partitions, key ranges, minimum and maximum dates, source watermark, and CDC position. | Missing, duplicated, stale, or unprocessed scope. |
| Record and value | Primary or business key, row hash, selected columns, nulls, duplicates, truncation, rounding, timezone, and encoding. | Source-to-target mismatch at the usable record level. |
| Business and relational | Referential integrity, statuses, balances, aggregates, period totals, invariants, and transformation rules. | Technically copied data that violates business meaning. |
| Consumer | Critical reports, reconciliations, APIs, extracts, permissions, performance, and downstream freshness. | Correct-looking tables that fail the operating workload. |
| Cutover | Final delta, in-flight transactions, lag, open exceptions, rerun status, rollback trigger, and owner sign-off. | Target state that is not ready to become authoritative. |
Migration platforms provide useful primitives. AWS Database Migration Service can compare corresponding source and target rows, report missing or different records, run validation-only tasks, and resynchronize supported discrepancies. Azure Data Factory copy activity can validate file size and checksum for supported file copies and compare tabular row counts. These controls should feed a broader reconciliation plan rather than replace business-level validation.
Compare equivalent states while data is moving
Validation is meaningless if the source and target represent different moments. Record the full-load snapshot, transaction-log or CDC position, target apply position, latency, in-flight transactions, and validation watermark. For high-change tables, revalidate changed keys and run the final bounded delta during cutover.
Do not query production without a load budget. Row-by-row validation can consume source, target, and network capacity. Use risk-based coverage, partitioning, group-level checks, checksums, samples, and targeted record comparison. Increase depth for financial, identity, entitlement, and other high-consequence domains.
Build a repeatable reconciliation workflow
| Component | Responsibility | Control |
|---|---|---|
| Mapping registry | Stores source, target, keys, transformations, exclusions, owners, criticality, and migration version. | Reviewed change history and no undocumented rule. |
| State coordinator | Captures snapshot, watermark, CDC position, lag, interval, and equivalent comparison boundary. | Deterministic state selection and explicit in-flight data. |
| Validation runner | Executes structural, count, checksum, record, aggregate, relational, business, and consumer checks. | Read budget, bounded partitions, versioned SQL, and immutable raw results. |
| AI exception analyst | Groups mismatches, links transformations and changes, proposes likely class, and drafts investigation tasks. | Source-grounded output, redaction, confidence, and no pass-or-fail authority. |
| Exception workflow | Assigns owner, severity, tolerance, remediation, rerun, waiver, and deadline. | Named approver, expiry, evidence, and no silent exclusion. |
| Cutover gate | Combines required checks, open exceptions, CDC lag, report results, rollback readiness, and sign-offs. | Deterministic gate, separation of duties, and stop authority. |
| Evidence register | Preserves run, query, counts, samples, mismatches, actions, reruns, approval, and final decision. | Access control, retention, auditability, and reproducibility. |
Use AI to investigate exceptions, not waive them
Mismatch volume can be large and repetitive. AI can cluster failures by table, field, transformation, source pattern, migration version, and likely cause. It can explain that many row differences share a timezone conversion or truncation rule and prepare the exact keys and source links for review.
A model must not decide that discrepancies are acceptable. Tolerance needs a business definition: permitted rounding, excluded history, expected anonymization, late-arriving records, or approved transformation. Every waiver needs scope, evidence, owner, reason, expiry, and downstream impact.
Gate cutover on evidence and risk
- Coverage: objects, rows, partitions, critical fields, transformations, relationships, reports, and downstream consumers tested.
- Correctness: missing source or target, value mismatch, duplicate key, null, truncation, aggregate variance, and business-rule failure.
- Change: full-load state, CDC lag, unapplied changes, reordered events, final delta, and cutover-window reconciliation.
- Operations: validation duration, source and target load, rerun time, exception age, owner response, and repeat mismatch.
- Decision: required checks passed, tolerances met, critical reports reconciled, rollback ready, and accountable approvals recorded.
Report results by criticality and domain, not one global pass percentage. Ten million correct archive rows do not offset one missing active entitlement. A cutover gate should fail closed when required evidence is stale, incomplete, or based on non-equivalent states.
Pilot one migration domain
- Select one domain with clear keys, mappings, owners, critical consumers, and representative transformation complexity.
- Define source and target states, CDC boundary, validation layers, tolerances, load budget, exceptions, and cutover authority.
- Inventory schemas, mappings, transformations, exclusions, data quality rules, reports, consumers, and rollback procedure.
- Build deterministic structural, completeness, record, aggregate, relational, business, and consumer checks.
- Run a dry migration, capture mismatches, and use AI-assisted clustering to identify recurring causes and missing rules.
- Repair mappings or data, rerun affected scope, and prove that exceptions close without hiding new discrepancies.
- Simulate changing source data, CDC catch-up, final delta, cutover gate, and rollback trigger.
- Expand by domain only after validation repeatability, load, exception ownership, and cutover evidence meet acceptance thresholds.
A bounded pilot can often reach repeatable dry-run validation in two to six weeks when mappings, keys, access, representative data, CDC state, reports, and owners are available. Undocumented transformations and weak business keys are readiness findings, not reasons to lower the bar.
Frequently asked questions
What is automated data migration reconciliation?
Automated data migration reconciliation compares source, migration output, and target using structural, technical, record-level, aggregate, business-rule, referential, change-data-capture, and downstream-report checks. It records exceptions, evidence, ownership, remediation, reruns, and cutover acceptance.
Are matching row counts enough to validate a data migration?
No. Equal counts can still hide changed values, missing and duplicated keys, truncation, encoding or timezone errors, invalid transformations, broken relationships, incorrect CDC ordering, and report differences. Counts are one control in a layered reconciliation plan.
How can AI help with migration validation?
AI can map source-to-target rules, propose checks from schemas and requirements, classify mismatches, group recurring exceptions, connect failures to transformations, summarize evidence, and draft remediation tasks. Deterministic queries and checks should decide pass or fail; accountable owners approve exceptions and cutover.
How do you validate a migration while source data is changing?
Establish a consistent snapshot or watermark, track CDC position and latency, separate full-load from incremental validation, account for in-flight transactions, and compare only equivalent source and target states. Revalidate changed keys and run a bounded final reconciliation during the cutover window.
How long does a migration reconciliation pilot take?
A pilot for one migration domain can often reach repeatable dry-run validation in two to six weeks when mappings, keys, schemas, transformation rules, source and target access, representative data, CDC state, critical reports, and owners are available. Weak keys, undocumented transformations, high write rates, and inconsistent source data can extend the schedule.
Official implementation references
- AWS DMS data validation
- AWS DMS data resync
- Azure Data Factory data consistency verification
- Azure Data Factory copy activity monitoring
Start with one migration domain and the business checks that determine whether the target is trustworthy. Datrick can assess mappings, validation depth, CDC state, exception workflow, cutover gate, and ownership before proposing a pilot.