Dagster
The asset-oriented data orchestrator for declarative, testable and observable pipelines.
Dagster is an open-source orchestrator that thinks in assets rather than tasks: every table, file or model is declared in Python as a software-defined asset, and the dependency graph, lineage and freshness follow from it. Partitions and backfills replay a date range, asset checks validate quality on every materialization, and dbt, Spark, Snowflake or Airbyte integrations plug into the same graph. It's our choice when a data team wants to test pipelines locally and know, asset by asset, what is up to date.
What Dagster brings to your project.
Typical use cases: ELT and dbt orchestration, ML feature pipelines, internal data platforms, migration from Airflow.
- 01
Software-defined assets: lineage, dependencies and freshness derived from code.
- 02
Partitions and backfills: replay a date range in one click.
- 03
Asset checks and declarative automation: rule-driven quality and refreshes.
- 04
Testable local development, native dbt integration, managed Dagster+ with branch deployments.
Entrust your project
to our experts.
Entrust your project to our
experts.
Entrust your project
to our experts.
Our experts build your project, delivering superior technical and functional quality within shorter timeframes.
They trust us.
They trust
us.
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A project with Dagster?
Describe your project. Our team replies within 24 hours with free technical scoping, along with a clear estimate of costs and timelines. No commitment.
- Reply within 24 hours from a project manager
or engineer.Reply within 24 hours from a project manager or engineer. - Technical scoping and quote, with no fees.
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confidential.No commitment, your data stays confidential.
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Paris · Lyon · Marseille · Nice · Geneva
Your company may qualify for a tax credit equal to 20% of the costs linked to developing its solution.
Free scoping & estimate in less than 24h
Describe your project and we'll get back to you with a costed estimate and a roadmap.