dbt-athena
The athena adapter plugin for dbt (data build tool)
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires AWS credentials (via aws cli, boto3 conventions, or explicit keys) and an existing S3 bucket and Athena database.
- Low install friction; pure Python wheel with 9 runtime dependencies.
- Actively maintained with a release 29 days ago and recent commits.
License · maintenance · safety
permissive license (permissive) — Permissive license treatment allows use in commercial and proprietary projects without restriction.
last release 2026-07-16 (29 days) · last repo commit 2026-08-14 · 231 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,159,960 downloads/mo, #4,285 on PyPI
Alternatives
Verify before relying
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/- 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.
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 on it.
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
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.
Requires AWS credentials (via aws cli, boto3 conventions, or explicit keys) and an existing S3 bucket and Athena database.
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/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
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 9 packagesboto3-stubsboto3dbt-adaptersdbt-commondbt-coremmh3pyathenapydantictenacity |
| Maintenance | Actively maintained 29 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,159,960 / month, #4,285 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableLicense :: OSI Approved :: Apache Software LicenseOperating System :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13 |
Evidence: dbt_athena-1.11.0-py3-none-any.whl
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See also dbt-athena-community · PyAthena · dbt-redshift · dbt-spark · dbt-bigquery · dbt-duckdb · pythena · dbt-postgres · dbt-snowflake · awswrangler