Data Migration
Leave legacy tooling behind without the risk. Our experts migrate your existing ETL jobs, spreadsheets, and scripts into DataFuseAI — preserving your business logic and keeping data flowing throughout the transition.
Challenges We Solve
Years of business logic locked inside legacy ETL tools, spreadsheets, or scripts.
Fear that migrating will break reports or lose institutional knowledge.
No clear inventory of what each existing job actually does.
Keeping data flowing while the migration is in progress.
What's Included
Migration Assessment
We inventory your existing jobs and map what each one does before moving anything.
Logic Preservation
Rebuild your transformations in DataFuseAI so business logic carries over intact.
Phased Cutover
Migrate in stages and run in parallel so nothing breaks during the transition.
Validation & Sign-Off
Compare outputs against the old system so you can switch over with confidence.
Key Benefits
FAQ
Frequently Asked Questions — Data Migration
Moving data between systems you already run — database to database, on-premise to cloud, or from a legacy store into a modern warehouse — by reading the source, reshaping it where the target requires a different structure, and writing it with an explicit table strategy.
Compare the two sides rather than trusting the run. Profiling both source and target gives row counts, distinct and null counts, and per-column sums that can be checked against each other — a sum you can reconcile is stronger evidence than a job that reported success.
Yes. Because the migration is a pipeline, it can be scheduled and re-run, and the table strategy controls whether each run overwrites, appends, or recreates the target. That is what makes a phased cutover possible instead of a single irreversible event.
It is read, not altered. A migration pipeline pulls from the source and writes to the target, so the decision to decommission the source stays yours and can happen after you are satisfied with what landed.
Type and definition mismatches rather than volume. A numeric field stored as text, a date in an unexpected format, or a column whose meaning differs subtly between the two systems produces a target that looks correct and is not. Profiling the output is how those surface before the old system is switched off.
