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Integrations#

Dagster is flexible and allows incremental adoption. It provides add-on libraries to integrate with your existing tools and infrastructure.

Guides#

This section includes guides on how to use Dagster with other tools.

NameDescription
Dagster with dbtThis guide shows how to orchestrate dbt from Dagster.
Dagster with AirflowThis guide shows how to compile an Airflow DAG into a Dagster pipeline.
Dagster with Great ExpectationsThis guide shows how to run data quality tests using Great Expectations in a Dagster pipeline.
Dagster with PySparkThis guide shows how to define and execute spark jobs in Dagster.
Dagster with PandasThis guide shows how Dagster works with Pandas.
Dagster with Jupyter/PapermillThis guide shows how to orchestrate Jupyter notebooks from Dagster.

Libraries#

Here is a complete list of Dagster's integration libraries. See full documentation in API Reference.

IntegrationLibrary
Airflowdagster-airflow
AWSdagster-aws
Azuredagster-azure
Celerydagster-celery
Celery + Dockerdagster-celery-docker
Crondagster-cron
Daskdagster-dask
Databricksdagster-databricks
Datadogdagster-datadog
Dockerdagster-docker
dbtdagster-dbt
GCPdagster-gcp
Great Expectationsdagster-ge
Githubdagster-github
Kubernetesdagster-k8s
MySQLdagster-mysql
PagerDutydagster-pagerduty
Pandasdagster-pandas
Papermilldagstermill
Papertraildagster-papertrail
PostgreSQLdagster-postgres
Prometheusdagster-prometheus
Pysparkdagster-pyspark
Shelldagster-shell
Slackdagster-slack
Snowflakedagster-snowflake
Sparkdagster-spark
SSH / SFTPdagster-ssh
Twiliodagster-twilio