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


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

Dagster with dbtOrchestrate dbt from Dagster.
Dagster with Great ExpectationsRun data quality tests using Great Expectations in a Dagster pipeline.
Dagster with SparkDefine and execute Spark jobs in Dagster.
Dagster with PandasHow Dagster works with Pandas.
Dagster with Jupyter/PapermillHow to orchestrate Jupyter notebooks from Dagster.
Dagster with AirflowUse Dagster in an Airflow cluster, or transform Airflow DAGs into Dagster pipelines.


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

Airflow dagster-airflow
AWS dagster-aws
Azure dagster-azure
Celery dagster-celery
Celery + Docker dagster-celery-docker
Dask dagster-dask
Databricks dagster-databricks
Datadog dagster-datadog
Docker dagster-docker
dbt dagster-dbt
Fivetran dagster-dbt
GCP dagster-gcp
Great Expectations dagster-ge
Github dagster-github
Kubernetes dagster-k8s
Microsoft Teams dagster-msteams
MLflow dagster-mlflow
MySQL dagster-mysql
PagerDuty dagster-pagerduty
Pandas dagster-pandas
Papermill dagstermill
Papertrail dagster-papertrail
PostgreSQL dagster-postgres
Prometheus dagster-prometheus
Pyspark dagster-pyspark
Shell dagster-shell
Slack dagster-slack
Snowflake dagster-snowflake
Spark dagster-spark
SSH / SFTP dagster-ssh
Twilio dagster-twilio