Dagster

The data orchestration platform.

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Overview

Dagster is a cloud-native orchestrator for the full lifecycle of data assets. It allows you to define your data pipelines as graphs of Python functions, and it provides tools for local development, testing, deployment, and monitoring. Dagster's key innovation is its focus on data assets, which represent the tables, files, and models that your pipelines produce.

✨ Key Features

  • Asset-based orchestration
  • Python-native development
  • Integrated testing and debugging
  • Data catalog and lineage
  • Scalable, cloud-native architecture

🎯 Key Differentiators

  • Asset-centric approach to orchestration
  • Strong focus on developer productivity and testing
  • Integrated data catalog and lineage

Unique Value: Develop, deploy, and observe data pipelines with a focus on the data assets they produce, leading to more reliable and maintainable data platforms.

🎯 Use Cases (4)

ETL/ELT pipelines Machine learning pipelines Data quality monitoring Building and maintaining data platforms

✅ Best For

  • Orchestrating a modern data stack with dbt, Fivetran, and Snowflake
  • Building a self-service data platform for analysts
  • Managing complex ML model training and deployment

💡 Check With Vendor

Verify these considerations match your specific requirements:

  • General-purpose, non-data-related workflow automation
  • No-code users

🏆 Alternatives

Apache Airflow Prefect

Provides a more structured and asset-aware approach compared to the task-oriented models of Airflow and Prefect.

💻 Platforms

Web API

✅ Offline Mode Available

🔌 Integrations

dbt Snowflake Spark Pandas Fivetran Airbyte

🛟 Support Options

  • ✓ Email Support
  • ✓ Live Chat
  • ✓ Dedicated Support (Enterprise tier)

🔒 Compliance & Security

✓ SOC 2 ✓ GDPR ✓ SSO ✓ SOC 2 Type II

💰 Pricing

Contact for pricing
Free Tier Available

Free tier: Open source is free. Cloud has a free tier.

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