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Data migration

Data migration services

Moving data off a legacy system without losing history, breaking reports, or discovering the problems on cutover weekend.

Plan a data migration

What is data migration?

Data migration is moving data from one system to another so the new system holds a complete, correct, and usable version of the old one. Codiot runs migrations off legacy platforms and consolidations after acquisitions, for teams who have learned that the risk is never the copying. It is the fields that do not map, the history nobody agreed to keep, and the reconciliation that only fails at full volume.
The moment you're probably in

You'll recognize one of these.

The data is dirtier than anyone admits

Duplicates, dead records, and fields quietly reused for something else. It surfaces during migration because that is the first time anyone reads all of it at once.

Nothing reconciles at the end

Records arrive but the totals do not match, and with no reconciliation designed in there is no way to prove what went missing or where.

Cutover has no way back

The migration runs once, on a weekend, with no rehearsal and no rollback. Whatever breaks, breaks in production with users waiting.

What we build

Data migration, end to end.

Sketch: four-step flow from audit through mapping and migration to verification
AuditMapMigrateVerify

Source audit and profiling

Reading the actual data rather than the schema: volumes, duplicates, orphans, and the fields that stopped meaning what their name says.

Field mapping and transformation rules

A documented map from every source field to its destination, with the judgement calls and exclusions written down rather than made silently in code.

Repeatable, rehearsed runs

Migration built to run many times against real data, so cutover is the rehearsal you have already done, not the first attempt.

Reconciliation and proof

Row counts, control totals, and spot checks that demonstrate the data arrived intact, in a form you can show an auditor or a board.

History and archive strategy

An explicit decision about what moves, what is archived, and what is retired, made before cutover rather than discovered after it.

Systems we speak

Your stack, not our preferences.

PostgreSQLSQL ServerSnowflakePythondbtAirflowAWSAzure
FAQ

Common questions, answered plainly.

How long does a data migration take?
Weeks for a single well-understood system, months when several sources are consolidated or the source data is poor. The copying is rarely the long part. Profiling the source, agreeing the field mapping, and building reconciliation take the majority of the time, which is why migrations that skip those stages tend to fail at cutover.
What usually goes wrong in a data migration?
Four things, repeatedly: source data is dirtier than expected; fields do not map cleanly and someone makes a silent assumption; nobody defined what history to keep; and there is no reconciliation, so nothing can be proven at the end. All four are avoidable, and all four are cheaper to handle before cutover than after.
Can you migrate without downtime?
Often, with an approach that syncs data continuously and cuts over once the new system is current, but it costs more and adds complexity. Many businesses are better served by a short planned outage that has been rehearsed. We recommend based on what the downtime would actually cost you, not by default.
How do you prove the migration worked?
Reconciliation designed in from the start: row counts by entity, control totals on financial fields, referential integrity checks, and sampled record-level comparison. The output is evidence you can hand to finance, audit, or a regulator, not an assurance that it looked fine.
Related

The rest of the stack.

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