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Duodata
Engineering

Two ways we work with your engineering team.

Native semantic deployment for Snowflake and Databricks, and a delivered workflow for the transformations, facts and dimensions behind it.

Offer 01 · Native semantic deployment

Deploy and maintain semantics in Snowflake and Databricks.

Bring business meaning, physical schemas and open models into one engineering workflow. Deploy native semantic views in Snowflake and metric views in Databricks.

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.

AI answer

Net revenue grew 5.4% from Q2 to Q3.

Definition used: net revenue = gross revenue − discounts − credits (owner: Finance).

Fictional example

Databricks AppSnowflake Native AppOpen formats

When a physical column changes

A rename surfaces the affected mapping for review. You update the mapping explicitly; the business definition stays the same.

Mapping and native output match the observed schema.

1 · Observed schema

demo.gold.revenue_by_quarter

Unity Catalog catalog demo, schema gold, view 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

Build and maintain the layers behind your AI.

Use business definitions, source mappings and engineering standards to build transformations, facts and dimensions, then validate the results from source to output.

  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
Scoped proof of concept

Bring the modeling or transformation workflow you want to simplify.

Pick one workflow: a semantic model to deploy, or the facts and dimensions behind a business question. We scope a paid proof of concept with your engineers.

  • 01One business question
  • 02One owner of the definition
  • 03The data it depends on
  • 04The result you expect
  • Bring your use case