Introduction to Databricks notebooks

Notebooks are a common tool in data science and machine learning for developing code and presenting results. In Databricks, notebooks are the primary tool for creating data science and machine learning workflows and collaborating with colleagues. Databricks notebooks provide real-time coauthoring in multiple languages, automatic versioning, and built-in data visualizations.

With Databricks notebooks, you can:

Notebooks are also useful for exploratory data analysis (EDA).

How to import and run example notebooks

The Databricks documentation includes many example notebooks that are intended to illustrate how to use Databricks capabilities. To import one of these notebooks into a Databricks workspace:

  1. Click Copy link for import at the upper right of the notebook preview that appears on the page.

    MLflow autologging quickstart Python notebook

    Open notebook in new tab

  2. In the workspace browser, navigate to the location where you want to import the notebook.

  3. Right-click the folder and select Import from the menu.

  4. Click the URL radio button and paste the link you just copied in the field.

    Import notebook from URL

  5. Click Import. The notebook is imported and opens automatically in the workspace. Changes you make to the notebook are saved automatically. For information about editing notebooks in the workspace, see Develop code in Databricks notebooks.

  6. To run the notebook, click Run all button at the top of the notebook. For more information about running notebooks and individual notebook cells, see Run Databricks notebooks.

To create a new, blank notebook in your workspace, see Create a notebook.

Start using Databricks notebooks