For data engineers
Start with business intent you can implement.
Definitions arrive with relationships and source mappings attached. You keep your Git, your standards and your platform runtime — Duodata fits the workflow, not the other way round.
What you get
The engineering picture
Mappings, not archaeology
Business terms arrive bound to candidate source columns, with observed schemas to confirm against.
Native deployment
Output is a Unity Catalog metric view or Snowflake semantic view in your account, reviewed in your Git flow.
Schema drift handled
Renames and type changes surface as impacted mappings to review, not broken dashboards to debug.
Industry
See it worked through.
- Food production & quality“Which inspection results actually count as passed?”The pass-rate definition, its mapping to the inspection tables and the native semantic asset it deploys as on your platform.For quality and operations teams · Quality · Operations · FinanceSee the Food production & quality story
- Air cargo“How much of what we booked actually flew?”The booked-to-flown cohort definition, its mapping to booking and shipment sources and the native semantic asset it deploys as on your platform.For cargo operations and commercial teams · Cargo operations · Commercial planning · FinanceSee the Air cargo story
Free pilot
Start a free pilot
We work through one business question with you: agree its meaning, map it to the relevant data and review how it works on Snowflake or Databricks. We agree the scope together before we start.
- 01One business question
- 02One owner of the definition
- 03The data it depends on
- 04A review on your platform
- Start a free pilotWatch demo