Data migration is the act of moving data from one place to another, for example from an old CRM to a new one, from a server to the cloud, or from a spreadsheet into a proper database. It sounds simple, but the data almost never fits the new system in exactly the same shape, so part of the work is reshaping it along the way.
Picture moving house. You don’t just throw everything in a van. You sort it, throw out what is broken, label the boxes, and make sure each item ends up in the right room. A data migration is the same: records get cleaned, mapped to the new structure, and checked before they are unpacked. Much of that mapping work overlaps with a schema migration, and the moving itself often runs through a data pipeline.
The least glamorous step quietly decides the outcome: field mapping. The old “client_name” might split into first and last name in the new system, a single address field might become five, and a status code of 3 might need translating to “active”. Get this mapping wrong and the data lands looking complete while meaning the wrong thing. This is also why data cleaning usually happens before the move, not after.
The riskiest moment is the cutover. Skip the checks and you can silently lose records or duplicate them. That is why careful data validation runs before, during and after the move, comparing counts and key fields on both sides.
At TopDevs we treat every migration as a one-way door, so we migrate into a copy first, prove the numbers match, and only switch a client over once nothing is left behind.
