Who is Dagster?
Dagster is a data orchestration platform (data orchestration) for building, scheduling, and monitoring data pipelines and assets across modern analytics, Machine Learning (ML), and Extract, Transform, Load (ETL) workflows.
Show more
- Open-source data orchestration framework for defining, running, and observing data pipelines and assets
- Software-defined assets and jobs for analytics, ML, and ETL workloads (data engineering)
- Deployment across local, hybrid, and cloud environments with container and Kubernetes support (cloud DevOps)
- Observability features for run logging, lineage, visibility into data dependencies, and failure handling (data observability)
- Commercial offering with management, collaboration, and governance capabilities for teams (SaaS data orchestration)
More About Dagster
Dagster is a data orchestration platform (data orchestration) designed for organizations that need to coordinate data pipelines and data assets across analytics, ML, and business intelligence environments. It provides a framework for defining data workflows as code, enabling engineers and data teams to manage dependencies, schedule executions, and monitor runs in a repeatable and testable way. The platform is used in contexts where data reliability, observability, and deployment flexibility are required across development, staging, and production environments.
Dagster centers on software-defined assets and jobs, expressed in Python, that represent tables, files, ML models, or other data products in an organization’s ecosystem (data engineering). These constructs expose metadata about inputs, outputs, and dependencies, which Dagster’s orchestration engine uses to determine what to run and in what order. This approach allows enterprises to build asset-centric workflows, align pipeline execution with data lineage, and integrate with existing data platforms, storage systems, and processing engines.
The platform supports deployment patterns that span local development, containerized workloads, and Kubernetes-based clusters (cloud DevOps). Organizations can run Dagster in their own infrastructure or use managed options that provide hosted control planes and operational tooling (SaaS data orchestration). The system exposes APIs and configuration mechanisms that integrate with Continuous Integration and Continuous Deployment (CI/CD) pipelines, Infrastructure-as-Code (IaC) practices, and common cloud providers, aligning with enterprise requirements for repeatable deployments, security controls, and environment isolation.
Dagster includes observability features for data workflows, including run logs, event streams, and visualizations of job and asset dependencies (data observability). This enables teams to track which assets were materialized, when they RAN, and which upstream changes triggered executions. Failure handling, retries, and alerting can be encoded in configuration and code, giving operators and data engineers a structured way to manage operational states and incident response within data platforms.
From a marketplace taxonomy perspective, Dagster aligns with categories such as data orchestration, data engineering, and data observability. It is typically evaluated alongside other workflow orchestrators and modern data stack components, with its focus on asset-based modeling, Python-defined pipelines, and hybrid deployment options. Enterprises use Dagster to coordinate batch and event-driven workloads, manage dependencies across warehouses, lakes, and ML systems, and centralize the control plane for data production processes in analytics and Artificial Intelligence (AI) initiatives.
Our description of Dagster. Updated January 2026.
