Notebooks

Notebooks give you JupyterLab for analysis in Python, with BigQuery ready to use. Each project has its own private notebook environment.

Using notebooks

Editors and admins press Start on the Notebooks page; JupyterLab opens inside the platform (the first start takes a moment). Your notebooks are files in the notebooks/ folder of your project’s repository, so you can commit them from Build like any other code.

In every notebook, these are already set up:

• bq: a BigQuery client for the project’s warehouse,
• DATASET: the dataset your dbt models write to,
• the %%bigquery magic, to run SQL in a cell and get a table back,
• pandas, NumPy, SciPy, scikit-learn, matplotlib, seaborn and Plotly.

df = bq.query(f"select * from `{DATASET}.my_model` limit 100").to_dataframe()
df.head()

No key file is needed; the environment is already authorised.

Limits and safety

• Each BigQuery query is capped at 100 GB billed by default. You can pass your own job configuration for a different cap in a single query.
• A notebook environment is private to its project. It can’t reach other projects’ data or the platform itself, and its internet access is limited to public HTTPS sites.
• It stops automatically after 60 minutes without activity, to save resources. Start it again when you need it. Files in notebooks/ are kept.

For viewers

Viewers can’t run code. Instead they can open the finished, saved results of notebooks read-only (the output you last saved), at a comfortable reading width.