dagster-aws
Package for AWS-specific Dagster framework solid and resource components.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (supports up to 3.14); AWS credentials must be configured in your environment for boto3 to authenticate.
- 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 · maintenance · safety
Apache-2.0 (permissive) — Apache 2.0 permissive license allows commercial use, modification, and redistribution with minimal restrictions, making it suitable for both open-source and proprietary projects.
last release 2026-08-07 (7 days) · last repo commit 2026-08-13 · 15,996 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 5,213,474 downloads/mo, #2,138 on PyPI
Alternatives
Verify before relying
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- 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
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 on it.
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
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.
Requires Python 3.10 or later (supports up to 3.14); AWS credentials must be configured in your environment for boto3 to authenticate.
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():
passVerify 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
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <3.15,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesboto3dagsterpackagingrequests |
| Maintenance | Actively maintained 7 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 5,213,474 / month, #2,138 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: dagster_aws-0.29.17-py3-none-any.whl
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See also dagster · dagster-azure · dagster-duckdb · dagster-docker · dagster-gcp · dagster-pipes · dagster-databricks · dagster-airbyte · dagster-rest-resources · dagster-ssh