Draft:Dagster

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Dagster is an open-source data orchestration platform designed for the development and management of data pipelines.[1] It was created by Nick Schrock, who formerly worked at Facebook and helped develop the GraphQL query language,[2] and was made available as an open-source project in 2019.[3] In contrast to traditional task-based workflow design, Dagster uses an asset-first design that focuses on data assets such as tables or machine learning models rather than individual workflow steps.[2][1] This design captures data lineage and enables workflow observability, with the aim of improving pipeline testability and lifecycle management.[1]

Nick Schrock founded Elementl in 2018 to build Dagster after observing limitations in existing pipeline scheduling systems such as Apache Airflow.[2][3] The public Dagster project debuted in mid-2019 as a pre-release Python library for developing data applications, including ETL and machine learning pipelines.[4] Elementl also launched Dagit, a browser-based graphical interface for creating and visualizing pipelines.[5]

The Dagster 0.x series continued until the platform reached a stable 1.0 release in August 2022.[6] This release introduced Dagster Cloud, a managed cloud environment providing hosted deployments and enterprise features such as branch deployments, single sign-on (SSO), and role-based access control.[7][8] In late 2022, Pete Hunt was appointed chief executive officer, while Schrock became chief technology officer.[6] In 2023, Elementl was renamed Dagster Labs.[9]

Dagster Labs raised a $33 million Series B investment in 2023, led by Georgian, bringing total funding to approximately $49 million.[2] Media coverage during this period reported on Dagster’s adoption by data teams at multiple companies.[2][3]

• Dagster defines pipelines in terms of software-defined assets rather than tasks. Assets represent data objects produced by computations, and Dagster tracks dependencies and lineage automatically.[2][10]
• Dagster supports Python type hints and explicit input and output definitions for assets, which can help identify errors during development.[3][10] Pipeline components can be tested locally using unit tests and mocks and integrated into CI/CD workflows.[3][10]
• Dagster includes a developer interface called Dagit for visualizing pipelines, viewing asset graphs, executing runs, inspecting logs, and debugging failures.[5][10]
• Dagster provides integrations with tools commonly used in data engineering, including dbt, Apache Spark, pandas, SQL data warehouses, and Apache Airflow via adapters.[3] Workflows can be deployed locally or on Docker, Kubernetes, or cloud-managed environments.[3]
• Dagster supports time-based schedules and event-driven sensors for triggering pipeline execution. It also provides features for data partitioning and backfills, allowing partial reprocessing of data.[5]
• Dagster captures metadata and lineage for each asset materialized during pipeline execution.[5] The Dagit UI displays error traces and execution timing information.[5]

Dagster has been adopted for production use by a range of organizations. Reported users include DoorDash, Flexport, Aritzia, Mapbox, and VMware.[3][2] In 2022, Dagster’s maintainers reported growth in the number of active projects and community contributions.[7][2]

Dagster Labs has also released tooling to support migration from Apache Airflow, including the ability to run Airflow DAGs within Dagster.[2] Case studies have cited improved development workflows after migration, particularly due to support for local testing and richer metadata.[2][10]

Dagster is developed as an open-source project licensed under the Apache License, Version 2.0, with source code hosted on GitHub.[1] The primary contributors are engineers at Dagster Labs, alongside community contributors.[7] Project governance follows a typical open-source model, with Dagster Labs guiding development and reviewing contributions.[2][8] As of 2025, the project has continued to grow in contributor participation and community-developed integrations.[8]

Dagster Labs also offers a commercial managed service, Dagster Cloud, which provides hosted deployments and additional operational features. The open-source version remains available for self-hosted use, while Dagster Cloud includes features such as SSO, role-based access control, and managed infrastructure.[8][7][2]

Dagster is frequently cited as part of a newer generation of data orchestration tools addressing limitations of earlier platforms such as Apache Airflow.[3][10] Commentary has highlighted its asset-centric approach, developer tooling, and observability features.

In comparative discussions, Dagster is often mentioned alongside Apache Airflow and Prefect as a leading open-source workflow orchestrator.[3][10] Media coverage, including articles published in 2023, has profiled Dagster’s growth and adoption in production environments.[2][3]

• Apache Airflow – Open-source workflow scheduler often used for ETL jobs
• Apache Oozie – Early Hadoop-based workflow scheduler for big data pipelines