Data modelling

Data modelling with dbt

Datalyze turns the raw tables in your warehouse into clean, documented data models built in dbt. We decide what needs modelling, plan the marts around the questions your team asks, and add a semantic layer so people and AI agents get the same answer.

150+Companies trust Datalyze
4-6 weeksto a modelled warehouse
1semantic layer for your team
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  1. 01Scope

    Decide what data needs to be modelled

    Not every table deserves a model. We go through your sources and the questions each team needs answered, then agree which data gets modelled.

  2. 02Plan

    Plan the marts and check they cover every use case

    Before writing any SQL, we map out every mart and data model, what each one holds, and which questions it serves.

  3. 03dbt

    Build the models in dbt

    We build the models in dbt, from staging to marts, with tests, documentation and version control. The logic lives in one place everyone can read, review and change.

  4. 04Semantic layer

    Create a semantic layer

    On top of the models we add the context a table name can't carry: what each metric means, the table schema, example queries and the rules behind each definition.

You get a warehouse your team can query without asking an engineer, and definitions every tool and agent agrees on.

Final step

Turn raw tables into
answers you can trust

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