dbt (data build tool)

What is dbt?

dbt is a tool for transforming data inside a warehouse. Raw tables go in, clean and modeled tables come out, and the logic in between is written as SQL select statements that dbt turns into the tables and views your analysts and dashboards rely on. It handles the T in ELT, the transform step, after data has already been loaded.

What sets it apart is that it brings software engineering discipline to that work. Transformations live in version control. Models can reference each other, and dbt figures out the order to run them. You write tests that assert things like "this column is never null" or "this id is unique," and documentation gets generated from the code itself. A finance team whose revenue numbers come from a tested, version-controlled dbt model, rather than a spreadsheet someone edits by hand, is the whole point. dbt does not store or move data; it orchestrates transformations on top of warehouses like Snowflake, BigQuery, or Redshift.

The result is a transformation layer you can review, test, and trust, instead of a tangle of one-off SQL scripts nobody remembers writing.

dbt at Dallonses

When we build a client's data warehouse, dbt is usually how we keep the transformation layer sane. We model the data in versioned SQL, add tests so bad data fails loudly instead of silently poisoning a dashboard, and document the models so the next person understands them without a meeting. Clean transformations are the unglamorous reason the numbers downstream can be trusted.

This work connects our data lakes and data warehouses with data analytics and visualization, since the tables dbt produces are exactly what the dashboards read from. We set up the pipeline so it runs on a schedule, alerts when a test breaks, and stays readable as the model grows. Global brands bring us reporting that has to be right, and dbt is a big part of how we keep it that way.

Tired of dashboards built on SQL nobody trusts? Let's rebuild the layer underneath.

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