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Duodata
Duodata Databricks App

Deploy and maintain semantics.

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

The Databricks App produces Unity Catalog metric views used by AI/BI Genie, inside your Databricks account.

Bring your use caseBusiness view of Databricks
Workflow

Observe, connect, refine, deploy, maintain.

  1. 01

    Observe

    Inspect physical tables, columns and relationships in your platform.

  2. 02

    Connect meaning

    Map business definitions to their physical implementation.

  3. 03

    Refine

    Resolve the platform details that make a semantic model usable.

  4. 04

    Deploy

    Produce the native semantic artifact for your selected platform.

  5. 05

    Maintain

    Review changes to definitions, schemas and their mappings together.

Already have Ossie models? Start with the Native App in your platform, then connect Duodata's business definitions when your team needs shared ownership and meaning.

Maintain

A schema change, reviewed

Rename the physical gross column and see the one affected mapping. Only Update mapping changes the native output; the business definition and totals stay 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.

Scoped proof of concept

Bring the model you want to deploy

We start with one business question, the person who owns its meaning, the data behind it and the result you expect. A scoped, paid proof of concept puts that meaning to work in your platform.

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