dbt-athena-community
The athena adapter plugin for dbt (data build tool)
Decision gist · record as of 2026-08-14
Yes. The adapter is production-stable, actively maintained, and has no known vulnerabilities. It's the standard way to run dbt against Athena and supports modern Python versions. Install it if you're building dbt workflows on AWS Athena; the low dependency footprint and permissive license make adoption straightforward. Requires AWS credentials and an existing Athena database setup.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires AWS credentials configured via aws cli/boto3 conventions or explicit aws_access_key_id and aws_secret_access_key.
- Needs an existing S3 bucket and Athena database.
- Low friction installation as a pure Python wheel.
License · maintenance · safety
permissive license (permissive) — Licensed under Apache Software License (permissive), allowing broad use and modification without significant restrictions on commercial or private projects.
last release 2026-07-16 (29 days) · last repo commit 2026-08-14 · 231 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 827,278 downloads/mo, #4,958 on PyPI
Alternatives
Verify before relying
pip install dbt-athena-community
Then configure profiles.yml:
athena:
target: dev
outputs:
dev:
type: athena
s3_staging_dir: s3://bucket/dbt/
region_name: eu-west-1
schema: dbt
database: awsdatacatalog
aws_profile_name: my-profile- Whether dbt-athena dependency is automatically installed or must be managed separately.
- Specific performance characteristics or query timeout defaults when working with large datasets.
- Compatibility with dbt Cloud beyond dbt Core mentioned in keywords.
What it is and what it does
dbt-athena-community is a dbt adapter that connects dbt workflows to AWS Athena, allowing you to build and manage data transformation pipelines using SQL and Python directly against Athena-managed data. It acts as the bridge between dbt's transformation framework and Athena's query engine, handling the translation of dbt models into Athena queries and managing the resulting tables and metadata.
The adapter supports the full range of dbt model types—tables, views, incremental models, snapshots, and Python models—with specific optimizations for Athena's architecture. It works with both Hive and Iceberg table formats (Iceberg requires Athena Engine v3), integrates with AWS Glue for metadata management, and supports Lake Formation for access control. Configuration is managed through dbt profiles, with options for S3 staging directories, workgroups, retry policies, and seed data handling.
Use it for
- Build incremental fact tables on Athena using insert_overwrite or append strategies, with merge support on Iceberg tables.
- Create and maintain slowly-changing dimension snapshots with timestamp or check strategies for historical data tracking.
- Run Python-based transformations alongside SQL models using Athena Spark workgroups for complex analytics.
- Integrate dbt workflows with AWS Lake Formation to enforce fine-grained access control on transformed tables.
- Manage seed data uploads to S3 with custom boto3 arguments for ACL and permission control.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The adapter is production-stable, actively maintained, and has no known vulnerabilities. It's the standard way to run dbt against Athena and supports modern Python versions. Install it if you're building dbt workflows on AWS Athena; the low dependency footprint and permissive license make adoption straightforward. Requires AWS credentials and an existing Athena database setup.
Install
dbt-athena-community on PyPI
Before you install
Low friction installation as a pure Python wheel. Actively maintained with a release within the last month. Requires Python 3.10 or later and depends only on dbt-athena, making setup straightforward.
Requires AWS credentials configured via aws cli/boto3 conventions or explicit aws_access_key_id and aws_secret_access_key. Needs an existing S3 bucket and Athena database.
License in practice
Licensed under Apache Software License (permissive), allowing broad use and modification without significant restrictions on commercial or private projects.
Quickstart
pip install dbt-athena-community
Then configure profiles.yml:
athena:
target: dev
outputs:
dev:
type: athena
s3_staging_dir: s3://bucket/dbt/
region_name: eu-west-1
schema: dbt
database: awsdatacatalog
aws_profile_name: my-profileVerify before relying
- Whether dbt-athena dependency is automatically installed or must be managed separately.
- Specific performance characteristics or query timeout defaults when working with large datasets.
- Compatibility with dbt Cloud beyond dbt Core mentioned in keywords.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagedbt-athena |
| Maintenance | Actively maintained 29 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 827,278 / month, #4,958 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 :: 3.12 |
Evidence: dbt_athena_community-1.11.0-py3-none-any.whl
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See also dbt-athena · pythena · dbt-spark · dbt-clickhouse · dbt-databricks · dbt-vertica · PyAthena · dbt-duckdb · dbt-sqlserver · dbt-glue