diff --git a/docs/content/integrations/airlift/airflow-dagster-equivalents.mdx b/docs/content/integrations/airlift/airflow-dagster-equivalents.mdx index 4040089266f71..14d8b92e374cd 100644 --- a/docs/content/integrations/airlift/airflow-dagster-equivalents.mdx +++ b/docs/content/integrations/airlift/airflow-dagster-equivalents.mdx @@ -370,3 +370,262 @@ Airflow Pools allow users to limit the number of concurrent tasks that can be ru ### Airflow Task Groups Airflow task groups allow you to organize tasks into hierarchical groups within the Airflow UI for a particular DAG. Dagster has _global_ asset groups which can be applied to any asset. Learn more about [asset groups](/concepts/assets/software-defined-assets#assigning-assets-to-groups). + +## Cheatsheet + +Here's a cheatsheet for Airflow users migrating to Dagster: + +
+ Airflow concept + | ++ Dagster concept + | +Notes | +
---|---|---|
Directed Acyclic Graphs (DAG) | ++ Jobs + | ++ |
Task | ++ Ops + | ++ |
Datasets | ++ Assets + | ++ Dagster assets are more powerful and mature than datasets and include + support for things like{" "} + + partitioning + + . + | +
Connections/Variables | +
+
|
+ + |
DagBags | ++ Code locations + | ++ Multiple isolated code locations with different system and Python + dependencies can exist within the same Dagster instance. + | +
DAG runs | +Job runs | ++ |
+ depends_on_past
+ |
+
+
|
+ + An asset can{" "} + + depend on earlier partitions of itself + + . When this is the case, + backfills + and + Declarative Automation + will only materialize later partitions after earlier partitions have + completed. + | +
Executors | ++ Executors + | ++ |
Hooks | ++ Resources + | ++ Dagster resource contain a superset of + the functionality of hooks and have much stronger composition + guarantees. + | +
Instances | ++ Instances + | ++ |
Operators | +None | ++ Dagster uses normal Python functions instead of framework-specific + operator classes. For off-the-shelf functionality with third-party + tools, Dagster provides{" "} + integration libraries. + | +
Pools | ++ Run coordinators + | ++ |
Plugins/Providers | ++ Integrations + | ++ |
Schedulers | ++ Schedules + | ++ |
Sensors | ++ Sensors + | ++ |
SubDAGs/TaskGroups | +
+
|
+ + Dagster provides rich, searchable{" "} + + metadata and tagging + {" "} + support beyond what’s offered by Airflow. + | +
+ task_concurrency
+ |
+ + + Asset/op-level concurrency limits + + | ++ |
Trigger | ++ Dagster UI Launchpad + | ++ Triggering and configuring ad-hoc runs is easier in Dagster which allows + them to be initiated through the{" "} + Dagster UI, the{" "} + GraphQL API, or the CLI. + | +
XComs | ++ I/O managers + | ++ I/O managers are more powerful than XComs and allow the passing large + datasets between jobs. + | +