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Notebooks and data apps

Notebooks and data apps are widely used by data teams to explore datasets, test ideas, and share results with other stakeholders.

  • Notebooks (for example, Jupyter or marimo) are often used for prototyping, exploratory analysis, and quick experiments.
  • Data app frameworks (for example, Streamlit) allow you to turn Python code into interactive applications that you can share with a broader audience.

All of these environments can access Bauplan through the Python SDK. You can call any client method from a notebook cell or an app callback to query tables, run pipelines, create branches, or materialize results.

When using notebooks or apps, it is good practice to work on a data branch rather than directly on main. Branches provide an isolated data sandbox in the lakehouse, so you can run exploration and app queries on production data without affecting the production environment. This lets you:

  • create temporary environments for analysis or prototyping,
  • pin apps or notebooks to a stable snapshot,
  • and merge or discard changes once you finish.