Dagster

Dagster is a unified control plane for building, scaling, and observing AI and data pipelines. It provides teams with an asset-based orchestration framework that supports ETL/ELT pipelines, data transformations, and AI/ML workflows. Trusted by companies like Kraft Heinz, Vanta, Bayer, and AMD, Dagster enables data teams to deliver insights faster while maintaining reliability and governance across their data platforms.

• Asset-Based Orchestration enables teams to define data assets declaratively, making pipelines easier to understand, test, and maintain with clear dependencies and lineage tracking.

• Data Catalog & Lineage empowers teams to discover and understand datasets with clear ownership, column-level lineage, and auto-generated documentation that stays current.

• Monitoring & Alerting keeps teams ahead of data incidents with intelligent alerts in Slack, streamlined resolution workflows, and AI-powered debugging with impact analysis.

• Real-time Health Metrics track freshness, performance, costs, and reliability to keep pipelines healthy and stakeholders confident in their data.

• Branch Deployments support local development workflows and CI/CD integration, allowing teams to test changes in isolated environments before production.

• Event-Based Automations trigger pipelines based on data events, schedules, or external signals for responsive data processing.

• Partitions & Backfills handle time-series and categorical data efficiently with built-in support for incremental processing and historical data reprocessing.

• Data Quality Checks validate asset freshness and quality with native checks and dbt test integration to catch issues before they impact downstream consumers.

• Compass Feature turns warehouse data into instant, trustworthy answers for stakeholders inside the tools they already use, governed by the data team through GitOps.

To get started, sign up for Dagster+ with a 30-day free trial or install the open-source version locally. Define your data assets using Python, configure integrations with your existing tools like dbt, Snowflake, or BigQuery, and deploy to production with serverless or hybrid deployment options.