Key Features
- Connects arbitrary sources and destinations without bespoke connectors
- AI agents infer field mappings and transformations
- Validates row counts and integrity on both shores
- Incremental and resumable migrations for large datasets
- Dry-run previews before anything is written
Built For
Engineers and operators moving off legacy systems, consolidating databases, or onboarding customer data
Vision
Every migration is the same problem wearing a different costume: read from here, reshape, write to there, prove nothing was lost. Dataferry treats that as a solved category. Describe the two shores and let AI agents map the fields, transform the shapes, and verify the crossing — so a migration is a task you finish, not a quarter you survive.
The Problem
Data migration is where projects quietly stall. Schemas never line up, edge cases hide in old rows, and the ETL scripts written to bridge two systems are thrown away the moment the move is done. Teams rebuild the same disposable plumbing for every migration, and the riskiest part — confirming the data arrived intact — is usually the most manual.
Key Differentiators
- Agent-mapped, not hand-mapped: field mappings and transforms are inferred and reviewed, not typed out by hand.
- Verification is first-class: integrity checks run on both sides, so "did everything make it" has an answer, not a hope.
- Resumable at scale: large crossings checkpoint and resume instead of restarting from zero.
- Preview before commit: dry-run the entire migration and inspect the result before a single row is written.