At a certain point, data migration stops being just about moving records from one place to another. On paper, simplicity sounds clean, but once you are dealing with large datasets, it can quickly spin out of control. You begin to struggle with fetching safely, processing reliably, recovering from failure, and resuming without corrupting data. This was the challenge in a wallet log migration I worked on while moving ledger operations into a dedicated core-banking service. The destination system needed those logs represented as transaction records, with the correct account mappings, client associations, balances, timestamps, metadata, and deterministic references. The migration had to be retry-safe, recover cleanly from failure, and remain practical under real operational constraints. The existing implementation handled everything in one continuous flow: fetch cursor-paginated records, map them inline, and write directly into the destination database.…