Astronomer
Managed Apache Airflow service for orchestrating and monitoring data pipelines in the cloud
Dagster
Cloud-native orchestrator for developing and maintaining data assets
Dagster is a declarative data pipeline orchestrator with integrated lineage, observability, and testability. Build and maintain tables, datasets, ML models, and reports using Python functions.
Overview
Modern Data Orchestration for the Entire Lifecycle
Dagster is a cloud-native orchestration platform designed for developing, testing, and maintaining data assets—tables, datasets, machine learning models, and reports. Unlike traditional workflow engines, Dagster uses a declarative programming model where you define assets as Python functions, and the platform handles scheduling, dependencies, and execution.
Built for Data Teams at Every Stage
From local development and unit testing to staging and production, Dagster provides integrated lineage tracking, observability, and diagnostics in a unified control plane. The platform scales both technically and organizationally with multi-tenant, multi-tool orchestration capabilities. Data practitioners can embrace CI/CD best practices, build reusable components, catch data quality issues early, and maintain visibility as complexity grows.
Extensible and Integration-Ready
Dagster integrates with the modern data stack through a growing library of connectors for popular tools. Deploy to your own infrastructure while centralizing metadata, cataloging, and performance monitoring. The platform supports Python 3.9 through 3.13 and includes a web UI for development and operations.
Highlights
Declarative asset definitions using Python functions with automatic dependency resolution
Integrated lineage tracking, observability, and cataloging in a unified control plane
Built-in testability supporting local development through production deployment
Extensive integrations with modern data stack tools and flexible infrastructure deployment
Pros
- Declarative programming model simplifies asset dependency management
- Comprehensive observability and lineage tracking built into the platform
- Strong testing capabilities across the entire development lifecycle
- Active community and extensive integration library for popular data tools
Considerations
- Python-centric approach may require learning curve for non-Python teams
- Declarative asset model represents a paradigm shift from traditional DAG orchestrators
- Multi-tenant production deployments require infrastructure planning and configuration
- Feature richness can introduce complexity for simple pipeline use cases
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Fit guide
Great for
- Data engineering teams building and maintaining complex data asset dependencies
- Organizations requiring integrated lineage and observability across data pipelines
- Teams practicing CI/CD and test-driven development for data workflows
- Multi-tool environments needing centralized orchestration and metadata management
Not ideal when
- Teams requiring orchestration in languages other than Python
- Simple scheduled tasks that don't benefit from asset-based modeling
- Organizations seeking fully managed, no-code orchestration solutions
- Projects with minimal dependency complexity or observability requirements
How teams use it
Machine Learning Pipeline Orchestration
Define feature tables, training datasets, and models as assets with automatic dependency tracking and lineage from raw data to deployed models
Data Warehouse Table Management
Declaratively manage table dependencies and transformations with built-in data quality checks and observability across staging and production environments
Analytics Report Generation
Orchestrate report creation with upstream data dependencies, ensuring reports update automatically when source data changes while maintaining full lineage
Multi-Team Data Platform
Centralize orchestration across teams using different tools with unified metadata, cataloging, and performance monitoring in a single control plane
Tech snapshot
Python79%
TypeScript19%
LookML1%
Jupyter Notebook1%
CSS1%
JavaScript1%
Frequently asked questions
What makes Dagster different from traditional workflow orchestrators?
Dagster uses a declarative asset-based model where you define what data assets to build rather than just task sequences. It includes integrated lineage, observability, and testability as core features rather than add-ons.
What Python versions does Dagster support?
Dagster officially supports Python 3.9 through Python 3.13 and is available via PyPI for easy installation.
Can Dagster integrate with my existing data tools?
Yes, Dagster provides a growing library of integrations for popular data stack tools and allows deployment to your own infrastructure while maintaining centralized orchestration.
Is Dagster suitable for both development and production?
Yes, Dagster is designed for the entire development lifecycle—from local development and unit tests through integration testing, staging, and production deployment with multi-tenant capabilities.
How does Dagster handle data lineage and observability?
Dagster provides built-in lineage tracking that automatically maps dependencies between assets, along with integrated observability, diagnostics, and cataloging in a unified control plane.
Project at a glance
Active- Stars
- 14,781
- Watchers
- 14,781
- Forks
- 1,942
LicenseApache-2.0
Repo age7 years old
Last commit12 hours ago
Primary languagePython
Last synced 3 hours ago