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Duodata
Engineering

How the meaning your business owns reaches your platform.

Two things have to happen for AI to answer with your definitions: the meaning has to be deployed into Snowflake or Databricks, and the data layers behind it have to be built and checked. This page shows how both work — in terms a business owner can follow.

Offer 01 · Native semantic deployment

Your definitions, deployed where your AI reads them.

The Native Apps turn the definitions your business owns into the semantic objects Snowflake and Databricks AI actually read — and keep them in step as things change, with review before anything ships.

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

Databricks AppSnowflake Native AppOpen formats

When a source column changes

A rename in a source system surfaces the one affected mapping for review. The definition your business agreed does not change — only the connection is updated, explicitly.

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 fact to physical column
LogicalPhysical
gross_revenuegross_revenue
discountsdiscounts
creditscredits

Business definition unchanged: net revenue = gross revenue − discounts − credits.

3 · Totals

Q2 net revenue
$9.3m
Q3 net revenue
$9.8m

Mapping current

Databricks metric view · revenue_semantics.sqlsql
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.

Offer 02 · Delivered engineering workflow

The data layers behind the answer, built with your team.

Agreed definitions still need data shaped to match them. Our engineers work alongside yours to build the transformations, facts and dimensions behind each measure — in your repositories, to your standards — and prove the output matches the source.

  1. 01

    With your engineers

    Delivered alongside your team and their AI coding and deployment tools.

  2. 02

    Your Git, your standards

    Code lands in your repositories, reviewed under your conventions.

  3. 03

    Your platform runtime

    Runs in Databricks or Snowflake through your deployment workflow; existing ingestion stays as it is.

  4. 04

    Facts and dimensions

    Silver-to-gold transformations shaped by business definitions.

  5. 05

    Maintained formulas

    Definition and schema changes handled as reviewed updates.

  6. 06

    Reconciliation

    Source → silver → gold → semantic checks on each change.

See the gold-layer workflow
Free pilot

Start a free pilot

Pick one workflow: a semantic model to deploy, or the facts and dimensions behind a business question. We agree the scope of a free pilot together before we start.