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Dagster

by Dagster Labs · Platform · also known as Dagster+

Asset-oriented Python orchestrator that models pipelines as software-defined data assets with lineage, checks and a hosted Dagster+ option

Apache-2.0 licenseOpen sourceFreemiumUsage-basedEnterpriseLocalSelf-hostedCloudSelf-hostable
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Link verified Nov 2, 2026

Overview

Dagster is an open-source orchestrator from Dagster Labs, released under Apache 2.0, that organizes pipelines around software-defined assets: Python functions declare the tables, files or models they produce and the upstream assets they depend on, and Dagster derives the execution graph, lineage, freshness policies and partitioning from those declarations. Asset checks attach data-quality tests, sensors and schedules trigger materializations, and the web UI shows the asset catalog alongside run history. Integrations include dbt (each dbt model becomes an asset), Spark, DuckDB, Snowflake, BigQuery, pandas and Polars I/O managers, and Kubernetes or Celery execution. A local development mode runs the full UI from one command, which makes it approachable for individuals.

Dagster+ is the managed control plane with serverless or hybrid agents, plus a free tier and usage-based paid plans. The asset model is a different mental model from Airflow's tasks and pays off most when pipelines produce persistent datasets; purely procedural jobs can feel forced into it, and the framework's rapid API evolution has required code updates between versions.

Capabilities

  • Software-defined assets with declarative dependencies and partitions
  • Asset checks for data quality
  • Schedules, sensors and freshness policies
  • dbt integration mapping models to assets
  • I/O managers for Parquet, DuckDB, warehouses and DataFrames
  • Local UI and hosted Dagster+ control plane

Typical workflows

  1. Asset graph combining Python ingestion, dbt models and ML training
  2. Partitioned backfills of daily datasets
  3. Local development of a research data pipeline with lineage

Strengths

  • Lineage and data-quality checks are built into the model
  • Simple local setup
  • Strong dbt integration

Limitations

  • Asset-centric model suits persistent datasets more than ad-hoc jobs
  • API has changed between releases
  • Hybrid/serverless features are tied to Dagster+

Prerequisites

  • Python environment; optional Dagster+ account

Who it is for

  • Data engineers and analytics engineers
  • Research teams maintaining reproducible pipelines

Alternatives & comparison

  • Apache Airflow

    Python workflow orchestrator for scheduling and monitoring DAGs of tasks, with hundreds of provider packages and managed offerings from several clouds

    Open sourceOpen source
  • dbt

    SQL-first transformation framework that compiles templated models, tests and documentation into warehouse tables; open-source Core plus a commercial platform

    MixedOpen source
  • Great Expectations (GX Core)

    Python data-quality framework that declares Expectations about datasets, validates them against pandas, Spark or SQL sources and produces Data Docs

    Open sourceOpen source
  • Prefect

    Python workflow orchestration library and server where decorated functions become observable, retryable flows, with Prefect Cloud as hosted option

    Open sourceOpen source
Compare Dagster with its alternatives →

Sources & verification

Primary sources consulted for this record. Claims are tied to the fields each source supports; the automated link checker re-verifies every URL on a schedule.

  • License
    Dagster LICENSE (Apache 2.0)

    https://github.com/dagster-io/dagster/blob/master/LICENSE

    Supports: license_spdx, source_model

    checked Nov 1, 2026
  • Official documentation
    Dagster documentation

    https://docs.dagster.io/

    Supports: capabilities, integrations

    checked Nov 1, 2026
  • Official site
    Dagster

    https://dagster.io/

    Supports: name, summary

    checked Nov 1, 2026
  • Pricing page
    Dagster+ pricing

    https://dagster.io/pricing

    Supports: pricing_model

    checked Nov 1, 2026
  • Repository
    dagster-io/dagster

    https://github.com/dagster-io/dagster

    Supports: repo_url

    checked Nov 1, 2026
Confidence
High
Reviewed
Nov 1, 2026
Links
Redirected · Nov 2, 2026

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