Dagster is an open-source data orchestrator designed for building, running, and monitoring data pipelines. It helps manage complex data workflows by ensuring reliability, observability, and modularity. Unlike traditional workflow schedulers, Dagster treats data pipelines as software assets, emphasizing testing, type safety, and version control.
Key Features:
• Declarative Pipeline Definition: Uses Python to define workflows as directed acyclic graphs (DAGs) of computations.
• Modularity and Reusability: Allows breaking down pipelines into reusable components.
• Observability & Monitoring: Provides built-in logging, metrics, and dashboards to track job execution.
• Integration Support: Works with tools like dbt, Apache Spark, Snowflake, and cloud storage.
• Local & Cloud Execution: Can run jobs locally or in cloud environments like AWS, GCP, or Kubernetes.
Dagster is a good choice for organizations looking to improve data pipeline reliability while maintaining flexibility in development and deployment. Would you like insights on how it compares to other orchestrators like Apache Airflow or Prefect?