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dbt-polars

dbt-polars integrates the flexibility and performance of Polars with the data governance and lineage capabilities of dbt.

Key features of dbt-polars:

  • Run anywhere: Polars runs on any compute platform, so you’re not locked into an expensive data warehouse.
  • Flexible: Available catalogs let you connect to local storage, Iceberg catalogs, Databricks, and more.
  • Extensible: Create a custom catalog to fit your unique storage and warehouse needs.
  • First-class Python models: Run Python models with the same performance and capabilities as SQL models.
  • Mix Python and SQL: Choose the language that best fits the transformation.
  • dbt-native: Bring lineage, documentation, and data testing into your workflow.
  • Ingestion: Build efficient, incremental API ingestion with Python models and keep the entire pipeline visible in the lineage graph.

Installation

pip install dbt-polars

Extra dependencies are required for cloud and Iceberg backends:

pip install 'dbt-polars[azure]'       # Azure Blob Storage
pip install 'dbt-polars[databricks]'  # Azure Blob Storage
pip install 'dbt-polars[iceberg]'     # Apache Iceberg (SQLite, REST, Databricks, …)

Catalogs

dbt-polars uses catalogs to define where data is stored. Each catalog maps to a storage backend. You can configure multiple catalogs in a single profile and reference them from your models.

Catalog Backend Status
local Local filesystem Usable
iceberg with SQLite Iceberg + SQLite metastore Usable
iceberg with REST + Databricks Iceberg REST API (Unity Catalog) Experimental
iceberg with other backends Any pyiceberg-supported backend Experimental
azure Azure Blob Storage / ADLS Gen2 Experimental
databricks Unity Catalog REST API + credential vending Experimental

Quick start

See the Getting started guide.

A minimal profiles.yml for local development is:

my_project:
  target: dev
  outputs:
    dev:
      type: polars
      schema: dev

      catalog_type: local
      root: ./data

Documentation

Metadata

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