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Dagster & Jupyter Notebooks

Dagstermill eliminates the tedious "productionization" of Jupyter notebooks.

Using the Dagstermill library enables you to:

  • View notebooks directly in the Dagster UI without needing to set up a Jupyter kernel
  • Define data dependencies to flow inputs and outputs from assets/ops to notebooks, between notebooks, and from notebooks to other assets/ops
  • Use Dagster resources and the Dagster config system inside notebooks
  • Aggregate notebook logs with logs from other Dagster assets and ops
  • Yield custom materializations and other Dagster events from your notebook code

About Jupyter​

Fast iteration, the literate combination of arbitrary code with markdown blocks, and inline plotting make notebooks an indispensable tool for data science. The Dagstermill package makes it easy to run notebooks using the Dagster tools and to integrate them into data jobs with heterogeneous ops: for instance, Spark jobs, SQL statements run against a data warehouse, or arbitrary Python code.