Put business intent to work in your data platform.
Deploy agreed business definitions as native semantics in Snowflake and Databricks — and build the layers behind them with a delivered engineering workflow.
From business meaning to platform execution
Defined by the business
Net revenue
= gross revenue − discounts − credits
- Owner
- Finance
- Used for
- Quarterly reporting
Model, mappings and native output
Your business meaning, ready for AI
One agreed definition with an owner, a time grain and the words people use to ask for it.
Choose your platform
Your platform executes the calculation. Pick one; you don't need both.
Answer in AI/BI Genie
Net revenue grew 5.4% from Q2 to Q3.
Definition used: net revenue = gross revenue − discounts − credits (owner: Finance), served through Unity Catalog metric views.
Fictional example
An assistant will find a revenue column. Whether it is the right one is your problem.
gross_revenue exists in three tables with three meanings. Without an agreed definition mapped to a column, the assistant picks one — fluently.
Question
“How much did revenue grow?”
AI answer without the business definition
Revenue grew 10%.
Gross revenue: $10.0m → $11.0m. The gross-revenue answer is 4.6 percentage points above the growth Finance reports.
Gross revenue, Q2 vs Q3 2026
Growth: 10.0%
| Component | Q2 | Q3 |
|---|---|---|
| Gross revenue | $10.0m | $11.0m |
| Discounts | $0.5m | $0.8m |
| Credits | $0.2m | $0.4m |
| Net revenue | $9.3m | $9.8m |
Fictional example in USD. Not customer data.
Your people know what the numbers mean.
Finance knows what counts as net revenue. Operations knows which shipments count as flown. Duodata captures that meaning in plain language, with an owner. Your existing reports and models are the starting point.
How business ownership worksYour platform runs it natively.
Engineers map that meaning to real tables and deploy it as Unity Catalog metric views or Snowflake semantic views — in your account, your Git and your release process.
See the engineering viewRename a source column. Watch the mapping catch it.
An observed schema, a business mapping and the native output — kept distinct, the way the real workflow keeps them.
Mapping and native output match the observed schema.
1 · Observed schema
demo.gold.revenue_by_quarter
Unity Catalog catalog demo, schema gold, table revenue_by_quarter
- credits DECIMAL(18,2)
- discounts DECIMAL(18,2)
- gross_revenue DECIMAL(18,2)
- quarter DATE
2 · Mapping
| Logical | Physical |
|---|---|
| gross_revenue | gross_revenue |
| discounts | discounts |
| credits | credits |
Business definition unchanged: net revenue = gross revenue − discounts − credits.
3 · Totals
- Q2 net revenue
- $9.3m
- Q3 net revenue
- $9.8m
Mapping current
CREATE OR REPLACE VIEW demo.gold.revenue_semantics
WITH METRICS
LANGUAGE YAML
AS $$
version: "1.1"
comment: Fictional quarterly revenue example.
source: demo.gold.revenue_by_quarter
fields:
- name: quarter
expr: source.quarter
measures:
- name: net_revenue
expr: SUM(source.gross_revenue) - SUM(source.discounts) - SUM(source.credits)
$$;Fictional example. Nothing here runs against a warehouse.
Start where your role starts.
Executives
See the decision a number supports and who owns its meaning.
Open the executives viewBusiness analysts
Capture definitions, ownership and the semantic layer your teams rely on.
Open the business analysts viewBI and data analysts
Follow mappings, tools, lineage and validation behind each measure.
Open the bi and data analysts viewData engineers
Inspect schemas, mappings and native platform output.
Open the data engineers view
Built for Snowflake and Databricks.
Duodata puts business meaning to work in Databricks and Snowflake — where your data and AI assistants already run.
Native Apps and a delivered engineering workflow.
Databricks App
Deploy and maintain Unity Catalog metric views used by AI/BI Genie.
Explore Databricks AppSnowflake Native App
Deploy and maintain Snowflake semantic views used by Cortex Analyst.
Explore Snowflake Native AppDelivered data workflows
Transformations, facts, dimensions and reconciliation, built with your engineers.
Explore Delivered data workflowsWorked examples
- 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
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

