Dagster is an orchestration platform for the development, production, and observation of data assets.
Dagster addresses the challenge of building reliable data pipelines by treating data assets as first-class objects rather than focusing solely on tasks or jobs. The platform enables developers to define, test, and monitor data workflows through a Python-native approach. Assets are connected through dependencies, allowing Dagster to understand the full lineage of data and automatically orchestrate their computation. The system provides observability into asset health, data quality, and execution history, making it possible to understand what data exists, how it was produced, and whether it meets expectations.
Teams building data platforms, analytics infrastructure, or machine learning pipelines should consider Dagster when they need visibility into data lineage and asset dependencies. The tool suits projects where understanding data provenance matters as much as job execution, and where teams want to catch data quality issues early in development rather than in production. It works well for organizations moving beyond simple task scheduling toward asset-oriented data engineering practices.
The project maintains active development with regular updates to core functionality and integrations. The codebase shows consistent refinement of the asset-centric model and expansion of observability features. Development includes ongoing work on the execution engine and improvements to how the system tracks and reports on data asset state. The project demonstrates sustained investment in documentation and examples that help teams adopt the asset-first approach.
