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Introducción a notebooks
En este documento, se proporciona una introducción a
los notebooks de Colab Enterprise en BigQuery. Puedes usar notebooks para completar
flujos de trabajo de análisis y aprendizaje automático (AA) con SQL, Python y otros
paquetes y API comunes. Los notebooks ofrecen una mejor colaboración y administración
con las siguientes opciones:
Comparte notebooks con usuarios y grupos específicos con
Identity and Access Management (IAM).
Revisa el historial de versiones del notebook.
Revierte o ramifica a partir de las versiones anteriores del notebook.
Los notebooks son recursos de código de BigQuery Studio
con tecnología de Dataform.
Las consultas guardadas también son recursos de código.
Todos los recursos de código se almacenan en una
región predeterminada. La actualización de la región predeterminada cambia
la región de todos los recursos de código creados después de ese punto.
Las funciones del notebook solo están disponibles en la consola de Google Cloud.
Ventajas
Los notebooks en BigQuery ofrecen los siguientes beneficios:
BigQuery DataFrames está
integrado en notebooks, no se requiere configuración. BigQuery DataFrames es
una API de Python que puedes usar para analizar datos a
gran escala con las API de DataFrame de Pandas
y
scikit-learn.
Un entorno de ejecución de notebook es una máquina virtual de Compute Engine asignada a un
usuario en particular para habilitar la ejecución de código en un notebook. Varios notebooks pueden
compartir el mismo entorno de ejecución. Sin embargo, cada entorno de ejecución pertenece a un solo usuario y no
lo pueden usar otros. Los entornos de ejecución de notebooks se crean según la plantilla, que
suelen definir los usuarios con privilegios administrativos. Puedes cambiar a un
entorno de ejecución que use un tipo de plantilla diferente en cualquier momento.
Seguridad para notebooks
Puedes controlar el acceso a los notebooks con los roles de Identity and Access Management (IAM). Para
obtener más información, consulta
Otorga acceso a los notebooks.
Regiones admitidas
BigQuery Studio te permite guardar, compartir y administrar versiones de notebooks. En la siguiente tabla, se enumeran las regiones en las que BigQuery Studio está disponible:
[[["Fácil de comprender","easyToUnderstand","thumb-up"],["Resolvió mi problema","solvedMyProblem","thumb-up"],["Otro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Información o código de muestra incorrectos","incorrectInformationOrSampleCode","thumb-down"],["Faltan la información o los ejemplos que necesito","missingTheInformationSamplesINeed","thumb-down"],["Problema de traducción","translationIssue","thumb-down"],["Otro","otherDown","thumb-down"]],["Última actualización: 2025-02-06 (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)."]]
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