Dagster
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What is Dagster
Dagster is an open-source data orchestration and DataOps platform used to define, schedule, and monitor data pipelines and related compute workloads. It targets data engineers and analytics engineering teams that need repeatable pipeline execution with software-defined assets, dependencies, and environment-aware configuration. Dagster emphasizes developer-centric pipeline authoring in Python, strong typing/metadata, and built-in observability features such as run logs, asset lineage, and backfills. It is commonly deployed with modern data stacks to orchestrate ELT/ETL, ML feature pipelines, and batch workloads across cloud and on-prem environments.
Asset-based orchestration model
Dagster models pipelines as software-defined assets with explicit dependencies, which supports lineage-aware execution and selective recomputation. This approach helps teams manage complex DAGs and incremental rebuilds without treating every workflow as a monolithic job. It also aligns well with analytics engineering patterns where tables, files, and models are first-class outputs.
Developer-friendly Python framework
Dagster provides a Python API for defining ops/assets, resources, IO managers, and configuration, which fits teams standardizing on Python for data work. The framework supports local development and testing patterns (e.g., unit testing of ops/assets) and promotes code review and CI practices. This can reduce reliance on GUI-only pipeline builders and improve version control discipline.
Built-in operational visibility
Dagster includes a web UI (Dagit) for run history, logs, schedules/sensors, asset catalog views, and backfill management. It captures metadata and materialization events that help operators understand what ran, what changed, and why failures occurred. These capabilities provide baseline observability without requiring a separate monitoring product for many teams.
Not a turnkey ETL suite
Dagster orchestrates and manages pipelines but does not include a broad library of managed source-to-destination connectors out of the box. Teams typically pair it with separate extraction/loading tools or custom code for integrations. Organizations expecting an all-in-one, connector-heavy ETL product may need additional components and operational ownership.
Data quality is integration-led
Dagster can run data tests and surface results, but comprehensive data quality management (rule authoring, profiling, anomaly detection, and governance workflows) usually depends on integrating external validation frameworks and storage of results. This can increase implementation effort compared with products that bundle extensive quality tooling. Quality monitoring also depends on how consistently teams emit metadata and checks from pipelines.
Operational complexity at scale
Running Dagster in production requires managing executors, storage for run/asset metadata, secrets, and deployment patterns (often containerized). Scaling schedules, sensors, and high-concurrency workloads can require careful infrastructure design and tuning. Teams without platform engineering support may find hosted options or simpler schedulers easier to operate.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Solo | $10 per month | 7.5k credits/month; 1 User; 1 Code location; 1 Deployment; 30-day free trial; serverless compute billed additionally at $0.005 per compute minute; credits overage charged at $0.03/credit; accepts card payment. |
| Starter | $100 per month | 30k credits/month; Up to 3 Users; 5 Code locations; 1 Deployment; Catalog Search; 30-day free trial; serverless compute billed at $0.005 per compute minute; credits overage $0.03/credit; accepts card payment. |
| Pro / Enterprise | Contact Sales | Custom pricing for large-scale platforms: unlimited code locations & deployments; Cost Tracking & Insights; Personalized onboarding & support; private Slack channel; Uptime SLAs; custom security questionnaires; serverless compute is a flat fee or customized; custom invoicing/ACH available. |
Seller details
Dagster Labs, Inc.
San Francisco, CA, USA
2019
Private
https://dagster.io/
https://x.com/dagster
https://www.linkedin.com/company/dagster-labs/
