dagster-aws

What it is and what it does

Dagster-aws is an integration layer that extends Dagster's data orchestration framework with AWS-native components. It allows you to build data pipelines that directly interact with AWS services, using Dagster's declarative asset model to define, schedule, and monitor data workflows that depend on or produce AWS resources.

The package sits between Dagster (the core orchestration engine) and boto3 (the AWS SDK), providing pre-built resource definitions and I/O managers that handle AWS authentication, credential management, and service interactions. You define your data assets as Python functions decorated with Dagster's @asset decorator, and dagster-aws handles the plumbing to connect them to S3 buckets, compute resources, or other AWS services. It inherits Dagster's testing and observability capabilities, so you can unit-test your AWS workflows locally and monitor them in production.

Use it for:

• Build ETL pipelines that read from S3, transform data, and write results back to S3 with Dagster orchestration
• Schedule and monitor machine learning model training jobs on AWS compute resources as part of a larger data asset graph
• Define data quality checks and lineage tracking for datasets stored in AWS, visible in Dagster's web UI
• Deploy multi-stage data workflows across development, staging, and production AWS environments using Dagster's environment configuration

Worth the install?

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

Dagster-aws provides AWS-specific integrations for Dagster, enabling data pipeline orchestration to work with AWS services like S3, EC2, and other AWS resources.

Yes, if you are already using Dagster and need to orchestrate workflows involving AWS services. The package is actively maintained, has no known vulnerabilities, and installs with low friction. It is a natural extension of Dagster rather than a standalone tool, so it is only worth installing if you have decided to use Dagster as your orchestration platform.

Install

dagster-aws on PyPI

pip install dagster-aws

uv add dagster-aws

poetry add dagster-aws

Installing dagster-aws

Before you install

Low friction installation with a pure-Python wheel. The package is actively maintained with a release 7 days ago and depends on well-established libraries (boto3, dagster, packaging, requests), all of which are widely used and stable.

License in practice

Apache 2.0 permissive license allows commercial use, modification, and redistribution with minimal restrictions, making it suitable for both open-source and proprietary projects.

Quickstart

pip install dagster-aws

import dagster_aws
from dagster import asset, define_asset_job

# Use AWS resources in your Dagster assets
@asset
def my_s3_asset():
    pass

Requires Python 3.10 or later (supports up to 3.14); AWS credentials must be configured in your environment for boto3 to authenticate.

Verify before relying

• Which specific AWS services are supported (S3, EC2, RDS, etc.) and their scope of integration
• Whether dagster-aws provides resource definitions, I/O managers, or both
• Performance characteristics and any limits on concurrent AWS operations

Package facts

Evidence: dagster_aws-0.29.17-py3-none-any.whl

Tags

More Distributed Computing packages