Use Dagster to orchestrate AI workflows: schedule, test locally, track usage, catch errors early, integrate ML tasks & OpenAI, and keep your data assets healthy & trustworthy.
Product Category
Data and Engineering
Product Subcategory
AI Functions
No native generative AI is advertised
Data aware orchestration that reacts to upstream changes using auto materialization policies
Asset Checks embed data quality tests that gate downstream runs
Orchestrates ML and analytics jobs so models, feature pipelines, and retraining stay reproducible
Product Core Functions
Dagster is a data orchestrator that models pipelines as software defined assets. Instead of wiring task graphs by hand, teams declare assets and their dependencies, and Dagster infers an asset graph that drives execution, lineage, and observability. Policies determine when an asset should be materialized, for example when code or upstream data changes, which avoids unnecessary work and cuts cost.
Schedules, sensors, and partitions handle time based or event driven runs. Asset Checks place data quality tests next to the code that produces the data, so invalid outputs fail early and never propagate. Backfills, retries, and re execution are built in, with run logs and metadata to trace every change.
Dagster+ is the managed service. It provides serverless and hybrid deployments, branch and preview environments for CI, role based access, and a unified UI for the asset catalog, lineage, and run management. Teams connect warehouses, lakes, and tools such as dbt, Spark, and Snowflake through Python resources and integrations.
The end result is a single control plane for analytics and ML pipelines that is versionable, testable, and observable, with a clean developer experience and a production grade control surface for operations.
Key Features
Software defined assets with a dependency aware asset graphÂ
Auto materialize policies that refresh only when neededÂ
Asset Checks for embedded data quality and validationÂ
Schedules, sensors, and partitions for time or event driven runsÂ
Managed Dagster+ with serverless or hybrid deployment, RBAC, CI friendly branch deployments
Rich lineage, run logs, retries, and backfills in a unified UIÂ
Strong dbt integration and warehouse, Python, and Spark resourcesÂ
Ease Of Use
Setup: A data engineer defines a few assets, connects a warehouse or lake, and enables a schedule or sensor. Same day validation of a first pipeline is realistic.
Daily workflow: Engineers ship changes through branches, observe runs, review asset checks, trigger backfills, and monitor fresh data from the catalog.
Team onboarding: Analytics engineers, ML engineers, and SREs pick up role focused views quickly, using examples and templates to standardize patterns.
Tool integration: Connect dbt, Snowflake, BigQuery, Spark, or file systems through Python resources, then validate one scheduled run and one event driven sensor. A couple of hours and you are operational.
Admin demand: Moderate for a platform owner. Maintain deployments, permissions, and secrets, tune policies and partitions, review run health and cost.
Use Cases
SaaS data teams coordinating analytics, metrics, and ML retraining
Companies standardizing dbt transformations with clear lineage and checks
Fintech or marketplaces that need partitioned backfills and reliable SLAs
Data platform groups moving from cron or Airflow to asset based orchestration
Consulting and analytics agencies running many client pipelines