Dagster

Dagster is a modern data orchestration platform that focuses on asset-based workflow management and data pipeline development. Unlike traditional workflow orchestrators that primarily manage task execution, Dagster organizes workflows around data assets and their relationships, providing strong typing, data lineage tracking, and comprehensive testing capabilities. It emphasizes software engineering best practices for data pipelines, including version control, testing, and observability, making it particularly popular for teams building reliable, maintainable data infrastructure.

Also known as: Asset-based orchestrator, data asset platform, modern workflow orchestrator

Comparisons

• Dagster vs. Apache Airflow: Airflow focuses on task-based workflows with DAGs, while Dagster centers around data assets and their dependencies, providing stronger typing and testing capabilities.
• Dagster vs. Data Orchestration: Dagster is a specific implementation of data orchestration that emphasizes asset-centric modeling and software engineering practices.
• Dagster vs. ETL Frameworks: Traditional ETL tools focus on data transformation processes, whereas Dagster provides comprehensive pipeline management including asset definition, testing, and lineage tracking.

Pros

• Asset-centric approach: Models data pipelines around assets and their relationships, making it easier to understand data dependencies and track data lineage.
• Strong development experience: Provides comprehensive testing, type checking, and local development capabilities that improve code quality and reduce production issues.
• Built-in observability: Offers detailed monitoring, logging, and asset materialization tracking without requiring external observability tools.
• Flexible execution: Supports multiple execution engines and can integrate with existing infrastructure like Kubernetes, Docker, or cloud services.

Cons

• Newer ecosystem: Has fewer third-party integrations and community resources compared to more established orchestration platforms.
• Learning curve: Asset-based modeling and Dagster's concepts require teams to rethink traditional workflow design approaches.
• Resource requirements: Running Dagster with its web interface and asset materialization can require significant computational resources.

Example

A data team uses Dagster to manage their web scraping and ML pipeline: they define assets for raw data collected via web scraper APIs using residential proxies, cleaned datasets, feature stores, and trained models. Dagster automatically tracks dependencies between these assets, ensures data quality checks pass before downstream processing, and provides comprehensive lineage tracking that shows how changes in scraping configurations impact final model performance.