Transform raw data into human-usable metrics.
How you transform data has a huge impact on the happiness and productivity of your team.
Have you ever attempted to debug a long stored procedure that someone else wrote?
With modular data modeling, anyone who knows SQL can make sense of your team’s work, and build on it when the time comes.
dbt is a framework for writing high-leverage SQL transformation code.
Define transformations in your native SQL syntax.
Layer your models with the ref() function, and dbt handles the rest.
Express your data warehouse design in terms of sources, staging models and marts.
Write SQL that writes itself, to avoid repeating frequently-used statements (ex: dbt_utils).
dbt makes easy things easy, and hard things possible.
Set your materialization logic inline with your SQL transformations.
dbt supports materializing models as tables, incremental tables, views, or a custom materialization of your design.
Long-running SQL transformations can eat up development time and drive up your cloud data warehouse bill.
dbt's support for incremental transformations allows you to only run models on newly-arrived data.
Mutable source tables are updated over time: an order is fulfilled, or a customer cancels their subscription.
dbt's snapshots allow you to snap source tables at a point in time.
Whether your team prefers your data tall or short, narrow or wide, your data transformation tool should support your efforts.