Dagster is an open source data orchestration platform that models pipelines as software-defined assets, giving data teams visibility into what data exists, where it comes from, and whether it is fresh, without the operational complexity of older workflow tools.
The Problem
Data teams running pipelines with Apache Airflow or home-built schedulers often have poor visibility into the state of their data assets. A failed run might mean a dashboard is showing stale numbers, but tracking which downstream assets are affected requires manual investigation. As data pipelines grow, the relationship between tasks and the data they produce becomes opaque. Commercial orchestrators like Fivetran and dbt Cloud solve narrow slices of this problem at per-seat or per-row pricing.
How Dagster Solves It
Dagster's asset-centric model inverts the traditional task graph: instead of defining "run this script," you define the data asset you want to produce and the code that produces it. Dagster tracks which assets are materialized, when they were last updated, and what depends on them. A built-in data catalog surfaces this metadata in a web UI. Apache 2.0 licensed; deploy locally, on Kubernetes, or on Dagster Cloud.
Key Features
- Software-defined assets: model data products (tables, ML models, reports) as first-class objects rather than tasks
- Built-in data catalog: track materialization status, metadata, and lineage for all assets in the web UI
- Flexible scheduling: time-based schedules, sensor-triggered runs, and manual backfills
- Resource system: inject database connections, API clients, and config as reusable, testable resources
- Partitioned assets: run pipelines over date ranges or custom partition keys with incremental processing
- Apache 2.0 licensed; open source with Dagster Cloud as a managed deployment option
Who It's For
Dagster is best for data engineers and ML platform teams at companies with complex data pipelines who need visibility into asset lineage and freshness, and for organizations moving from Airflow who want a more developer-friendly, testable orchestration model.
Compared to Apache Airflow
Unlike Airflow, Dagster centers on data assets rather than task execution, giving teams a built-in catalog and lineage view without additional tooling. Airflow has a larger operator ecosystem and longer community history; Dagster provides better type safety, testability, and asset-level observability out of the box.
