This section explains how to accomplish common tasks in Dagster and showcases Dagster's experimental features.
| Name | Description | |
|---|---|---|
| Upgrading to Software-Defined Assets | This guide describes how to enrich what you've built in Dagster with software-defined assets. | |
| Versioning and Memoization | This guide describes how to use Dagster's versioning and memoization features. Experimental | |
| Software-Defined Assets with Pandas and PySpark | This guide offers a fast introduction to software-defined assets, with Pandas and PySpark. | |
| Run Attribution | This guide describes how to perform Run Attribution by using a Custom Run Coordinator Experimental | |
| Migrating to Graphs, Jobs, and Ops | This guide describes how to migrate to the Graph, Job, and Op APIs from the legacy Dagster APIs (Solids and Pipelines). | |
| Re-execution | This guide describes how to re-execute a job within Dagit and using Dagster's APIs. | |
| Fully-Featured Example Project | This guide describes the Hacker News example project, which takes advantage of many of Dagster's features | |
| Validating Data with Dagster Type Factories | This guide illustrates the use of a Dagster Type factory to validate Pandas dataframes using the third-party library Pandera. |