dbt-athena

What it is and what it does

dbt-athena is a plugin that connects dbt to Amazon Athena, allowing data engineers to write SQL and Python transformations that execute in Athena. It handles the translation of dbt's model definitions, incremental logic, and snapshots into Athena-compatible DDL and DML, managing query execution, result polling, and metadata updates through boto3 and pyathena.

The adapter supports both Hive and Iceberg table formats, with Iceberg requiring Athena Engine v3 and a unique table location. It integrates with AWS Glue for metadata management, supports incremental models with different merge strategies depending on table type, and can run Python models through Athena Spark workgroups. Configuration is handled through dbt profiles, with options for S3 staging directories, retry policies, workgroup selection, and AWS Lake Formation tagging.

Use it for:

• Build incremental fact tables in Athena using dbt's merge or append strategies, with automatic query retry and state management.
• Create snapshots of slowly-changing dimensions in Athena using timestamp or check strategies for historical tracking.
• Run Python-based data transformations in Athena via Spark workgroups, integrated into a dbt DAG.
• Manage Iceberg table format in Athena with dbt's table materialization and incremental model support.
• Organize raw S3 data into Athena databases with dbt seeds and models, leveraging Glue catalog integration.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

dbt-athena is a dbt adapter that enables data transformation workflows to run against Amazon Athena, supporting table and incremental models, snapshots, and Python models on Hive and Iceberg table formats.

Yes. dbt-athena is actively maintained, has low install friction, supports current Python versions (3.10–3.13), carries no known vulnerabilities, and uses a permissive license. It is the standard way to integrate dbt workflows with Athena. Install it if you use dbt and need to transform data in Athena.

Install

dbt-athena on PyPI

pip install dbt-athena

uv add dbt-athena

poetry add dbt-athena

Installing dbt-athena

Before you install

Low install friction; pure Python wheel with 9 runtime dependencies. Actively maintained with a release 29 days ago and recent commits. Supports Python 3.10 through 3.13.

License in practice

Permissive license treatment allows use in commercial and proprietary projects without restriction.

Quickstart

pip install dbt-athena

In profiles.yml:
athena:
  target: dev
  outputs:
    dev:
      type: athena
      schema: my_schema
      database: awsdatacatalog
      region_name: us-west-2
      s3_staging_dir: s3://my-bucket/dbt/

Requires AWS credentials (via aws cli, boto3 conventions, or explicit keys) and an existing S3 bucket and Athena database.

Verify before relying

• Whether dbt version 1.7.* constraint applies to all use cases or only specific features.
• Performance characteristics for large-scale incremental models on Hive vs. Iceberg tables.
• Compatibility with dbt Cloud beyond dbt Core.

Package facts

Evidence: dbt_athena-1.11.0-py3-none-any.whl

Keywords: adapter, adapters, athena, database, dbt, dbt Cloud, dbt Core, dbt Labs, dbt-core, elt

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