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Introduction to notebooks
This document provides an introduction toColab Enterprise notebooksin BigQuery. You can use notebooks to complete
analysis and machine learning (ML) workflows by using SQL, Python, and other
common packages and APIs. Notebooks offer improved collaboration and management
with the following options:
Share notebooks with specific users and groups by using
Identity and Access Management (IAM).
Review the notebook version history.
Revert to or branch from previous versions of the notebook.
Notebooks areBigQuery Studiocode assets powered byDataform.Saved queriesare also code assets.
All code assets are stored in a defaultregion. Updating the default region changes
the region for all code assets created after that point.
Notebook capabilities are available only in the Google Cloud console.
Benefits
Notebooks in BigQuery offer the following benefits:
BigQuery DataFramesis
integrated into notebooks, no setup required. BigQuery DataFrames is
a Python API that you can use to analyze BigQuery data at
scale by using thepandas DataFrameandscikit-learnAPIs.
A notebook runtime is a Compute Engine virtual machine allocated to a
particular user to enable code execution in a notebook. Multiple notebooks can
share the same runtime. However, each runtime belongs to only one user and can't
be used by others. Notebook runtimes are created based on template, which are
typically defined by users with administrative privileges. You can change to a
runtime that uses a different template type at any time.
Notebook security
You control access to notebooks by using Identity and Access Management (IAM) roles. For
more information, seeGrant access to notebooks.
You can monitor your BigQuery Studio notebook slot usage by viewing yourCloud Billing reportin the Google Cloud console. In the Cloud Billing report, apply a filter with the labelgoog-bq-feature-typewith the valueBQ_STUDIO_NOTEBOOKto view slot usage and costs from BigQuery Studio notebook.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-09-04 UTC."],[[["\u003cp\u003eBigQuery notebooks facilitate analysis and machine learning workflows through SQL, Python, and other tools, offering enhanced collaboration features like sharing, version history, and branching.\u003c/p\u003e\n"],["\u003cp\u003eNotebooks are code assets within BigQuery Studio, powered by Dataform, and are integrated with BigQuery DataFrames for scalable data analysis using pandas and scikit-learn.\u003c/p\u003e\n"],["\u003cp\u003eNotebooks provide assistive code development through Gemini AI, auto-completion of SQL statements, and data visualization via matplotlib and seaborn libraries.\u003c/p\u003e\n"],["\u003cp\u003eNotebooks use Colab Enterprise runtimes, which are user-specific Compute Engine virtual machines that can be shared by multiple notebooks but not by multiple users.\u003c/p\u003e\n"],["\u003cp\u003eAccess to notebooks is controlled via Identity and Access Management (IAM), and pricing information for notebook runtimes and slot usage can be monitored via Cloud Billing reports.\u003c/p\u003e\n"]]],[],null,["# Introduction to notebooks\n=========================\n\nThis document provides an introduction to\n[Colab Enterprise notebooks](/colab/docs/introduction)\nin BigQuery. You can use notebooks to complete\nanalysis and machine learning (ML) workflows by using SQL, Python, and other\ncommon packages and APIs. Notebooks offer improved collaboration and management\nwith the following options:\n\n- Share notebooks with specific users and groups by using Identity and Access Management (IAM).\n- Review the notebook version history.\n- Revert to or branch from previous versions of the notebook.\n\nNotebooks are [BigQuery Studio](/bigquery/docs/query-overview#bigquery-studio)\ncode assets powered by [Dataform](/dataform/docs/overview).\n[Saved queries](/bigquery/docs/saved-queries-introduction) are also code assets.\nAll code assets are stored in a default\n[region](#supported_regions). Updating the default region changes\nthe region for all code assets created after that point.\n\nNotebook capabilities are available only in the Google Cloud console.\n\nBenefits\n--------\n\nNotebooks in BigQuery offer the following benefits:\n\n- [BigQuery DataFrames](/python/docs/reference/bigframes/latest) is integrated into notebooks, no setup required. BigQuery DataFrames is a Python API that you can use to analyze BigQuery data at scale by using the [pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) and [scikit-learn](https://scikit-learn.org/stable/modules/classes.html) APIs.\n- Assistive code development powered by [Gemini generative AI](/bigquery/docs/write-sql-gemini).\n- Auto-completion of SQL statements, the same as in the BigQuery editor.\n- The ability to save, share, and manage versions of notebooks.\n- The ability to use [matplotlib](https://matplotlib.org/), [seaborn](https://seaborn.pydata.org/), and other popular libraries to visualize data at any point in your workflow.\n\nRuntime management\n------------------\n\nBigQuery uses\n[Colab Enterprise runtimes](/colab/docs/create-runtime) to run\nnotebooks.\n\nA notebook runtime is a Compute Engine virtual machine allocated to a\nparticular user to enable code execution in a notebook. Multiple notebooks can\nshare the same runtime. However, each runtime belongs to only one user and can't\nbe used by others. Notebook runtimes are created based on template, which are\ntypically defined by users with administrative privileges. You can change to a\nruntime that uses a different template type at any time.\n\nNotebook security\n-----------------\n\nYou control access to notebooks by using Identity and Access Management (IAM) roles. For\nmore information, see\n[Grant access to notebooks](/bigquery/docs/create-notebooks#grant_access_to_notebooks).\n\nTo detect vulnerabilities in Python packages that you use in your notebooks,\ninstall and use\n[Notebook Security Scanner](/security-command-center/docs/enable-notebook-security-scanner)\n([Preview](/products#product-launch-stages)).\n\nSupported regions\n-----------------\n\nBigQuery Studio lets you save, share, and manage versions of\nnotebooks. The following table lists the regions where BigQuery Studio is\navailable:\n\nPricing\n-------\n\nFor pricing information about BigQuery Studio notebooks, see [Notebook runtime pricing](/bigquery/pricing#external_services).\n\nMonitor slot usage\n------------------\n\nYou can monitor your BigQuery Studio notebook slot usage by viewing your [Cloud Billing report](/billing/docs/reports) in the Google Cloud console. In the Cloud Billing report, apply a filter with the label **goog-bq-feature-type** with the value **BQ_STUDIO_NOTEBOOK** to view slot usage and costs from BigQuery Studio notebook.\n\nTroubleshooting\n---------------\n\nFor more information, see [Troubleshoot Colab Enterprise](/colab/docs/troubleshooting).\n\nWhat's next\n-----------\n\n- Learn how to [create notebooks](/bigquery/docs/create-notebooks).\n- Learn how to [manage notebooks](/bigquery/docs/manage-notebooks)."]]
