Dagster
Asset-centric orchestration for data and ML pipelines
Dagster is profiled here as a Pipeline Orchestration tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
Dagster is an open-source orchestrator created in 2018 by GraphQL co-creator Nick Schrock and developed by Dagster Labs. It models pipelines as the data assets they produce, so lineage, freshness, and quality checks attach directly to the tables and models a team actually cares about. Dagster+ adds a managed control plane with branch deployments and cost insights. The asset catalog doubles as documentation, giving analysts a searchable view of every table, its owner, and its freshness status.
Key Capabilities:
Software-defined assets with built-in lineage and cataloging
Declarative automation through schedules, sensors, and freshness policies
Asset checks for in-pipeline data quality validation
First-class dbt integration mapping models to assets
Branch deployments for testing pipeline changes in isolation
Apache 2.0 license with the managed Dagster+ platform
Alternative tools
- Flyte
Kubernetes-native orchestration for data and ML workflows
- Kestra
Declarative, event-driven orchestration defined in YAML
- Temporal
Durable execution for long-running, reliable workflows
- Prefect
Python-native orchestration for data and ML workflows
- Apache Airflow
The most widely deployed open-source workflow orchestrator