Dagster
Dagster is a system for building modern data applications.
Elegant programming model: Dagster provides a set of abstractions for building self-describing, testable, and reliable data applications. It embraces the principles of functional data programming; gradual, optional typing; and testability as a first-class value.
Flexible & incremental: Dagster integrates with your existing tools and systems, and can invoke any computation–whether it be Spark, Python, a Jupyter notebook, or SQL. It is also designed to work with your existing systems like Kubernetes.
Beautiful tools: Dagster's development environment, dagit, is designed to facilitate local development for data engineers, machine learning engineers, and data scientists. It also can be run as a production service, to support operating, debugging, and maintaining large-scale production data pipelines.
Getting Started
Installation
pip install dagster dagit
This installs two modules:
- Dagster: the core programming model and abstraction stack; stateless, single-node, single-process and multi-process execution engines; and a CLI tool for driving those engines.
- Dagit: the UI for developing and operating Dagster pipelines, including a DAG browser, a type-aware config editor, and a live execution interface.
Hello dagster 👋
hello_dagster.py
from dagster import execute_pipeline, pipeline, solid
@solid
def get_name(_):
return 'dagster'
@solid
def hello(context, name: str):
context.log.info('Hello, {name}!'.format(name=name))
@pipeline
def hello_pipeline():
hello(get_name())
Save the code above in a file named hello_dagster.py
. You can execute the pipeline using any one
of the following methods:
(1) Dagster Python API
if __name__ == "__main__":
execute_pipeline(hello_pipeline) # Hello, dagster!
(2) Dagster CLI
$ dagster pipeline execute -f hello_dagster.py
(3) Dagit web UI
$ dagit -f hello_dagster.py
Learn
Next, jump right into our tutorial, or read our complete documentation. If you're actively using Dagster or have questions on getting started, we'd love to hear from you:
Contributing
For details on contributing or running the project for development, check out our contributing
guide.
Integrations
Dagster works with the tools and systems that you're already using with your data, including:
Integration | Dagster Library | |
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Apache Airflow | dagster-airflow Allows Dagster pipelines to be scheduled and executed, either containerized or uncontainerized, as Apache Airflow DAGs. |
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Apache Spark | dagster-spark · dagster-pyspark
Libraries for interacting with Apache Spark and PySpark. |
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Dask | dagster-dask
Provides a Dagster integration with Dask / Dask.Distributed. |
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Datadog | dagster-datadog
Provides a Dagster resource for publishing metrics to Datadog. |
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Jupyter / Papermill | dagstermill Built on the papermill library, dagstermill is meant for integrating productionized Jupyter notebooks into dagster pipelines. |
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PagerDuty | dagster-pagerduty
A library for creating PagerDuty alerts from Dagster workflows. |
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Snowflake | dagster-snowflake
A library for interacting with the Snowflake Data Warehouse. |
Cloud Providers | ||
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AWS | dagster-aws
A library for interacting with Amazon Web Services. Provides integrations with Cloudwatch, S3, EMR, and Redshift. |
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Azure | dagster-azure
A library for interacting with Microsoft Azure. |
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GCP | dagster-gcp
A library for interacting with Google Cloud Platform. Provides integrations with GCS, BigQuery, and Cloud Dataproc. |
This list is growing as we are actively building more integrations, and we welcome contributions!