Dagster Data Orchestration

Author Dagster assets, jobs, sensors, and schedules. Generates asset-based DAGs, partitioned backfills, dbt+Dagster integrations, IO managers for warehouses, and observability with declarative scheduling.

Dagster treats your data pipeline as a graph of assets, not tasks — making lineage and backfills first-class. This skill writes software-defined assets, sets up partitioned backfills, integrates dbt and Airbyte, configures IO managers for warehouses, and adds sensors for event-driven runs.

When to use

Use when Airflow's task-centric model is getting in the way of asset lineage, when you need partition-aware backfills, or when you want dbt and Python transformations orchestrated as one DAG.

Examples

Asset-based DAG with dbt

One graph from raw ingestion through dbt models

Build a Dagster pipeline that ingests CSVs from S3 as assets, materializes them in Snowflake, then triggers downstream dbt models in the same graph

Partitioned backfill

Reprocess only the affected partitions

Define a daily-partitioned Dagster asset and write the backfill plan for the last 30 days after a schema fix