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Duodata
Delivered engineering workflow

Build and maintain the layers behind your AI.

Use your business definitions, source mappings and engineering standards to generate warehouse transformations, build facts and dimensions, and validate the results from source to output. Keep the work in your Git repository and execute it through your data platform and deployment workflow.

Replace repetitive modeling and transformation work with a repeatable engineering workflow. Bring the gold or silver workflow you want to simplify.

Gold-layer example

From business definition to reconciled output

Net revenue, end to end: definition, mapping, silver input, the Gold table snapshot, native semantics and the source-to-gold check.

On this selection, demo is a Snowflake database: DEMO.GOLD.REVENUE_BY_QUARTER.

Net revenue = gross revenue − discounts − credits

Owner: Finance. Calendar quarters, USD, non-null decimal amounts.

Fictional example. The website computes these rows locally; it does not run SQL in a warehouse.

Layers

One engineering story within your platform

A coding harness and skills may generate transformation work; your warehouse and deployment tooling execute it. The Native App focuses on semantics, not on running every ingestion or transformation step.

  1. 01

    Bronze

    Source inputs and source totals used for reconciliation.

  2. 02

    Silver

    Existing structures and transformations, including mappings into a data vault where applicable.

  3. 03

    Gold

    Generated and maintained facts, dimensions and information-mart artifacts.

  4. 04

    Semantics

    Native semantic views and metric views exposing business meaning to analytics and AI.

Free pilot

Start a free pilot

Bring the gold or silver workflow you want to simplify. We agree the scope of a free pilot together before we start.