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Introdução ao Notebooks
Este documento apresenta uma introdução aos
notebooks do Colab Enterprise
no BigQuery. É possível usar notebooks para concluir
fluxos de trabalho de análise e machine learning (ML) usando SQL, Python, outros
pacotes e APIs comuns. O Notebooks oferece colaboração e gerenciamento aprimorados
com as seguintes opções:
Compartilhe notebooks com usuários e grupos específicos usando
o Identity and Access Management (IAM).
Consulte o histórico de versões do notebook.
Reverter ou ramificar a partir de versões anteriores do notebook.
Os notebooks são recursos de código do BigQuery Studio com tecnologia do Dataform.
As consultas salvas também são recursos de código.
Todos os recursos de código são armazenados em uma
região padrão. A atualização da região padrão altera
a região de todos os recursos de código criados depois desse ponto.
Os recursos do notebook estão disponíveis apenas no Google Cloud console.
Vantagens
Os notebooks no BigQuery oferecem os seguintes benefícios:
O BigQuery DataFrames é integrado aos notebooks. A configuração não é necessária. O BigQuery DataFrames é
uma API do Python que pode ser usada para analisar dados do BigQuery
em escala usando o
pandas do DataFrame
e as APIs do
scikit-learn.
Um ambiente de execução do notebook é uma máquina virtual do Compute Engine alocada a um
usuário específico para ativar a execução de código em um notebook. Vários notebooks podem
compartilhar o mesmo ambiente de execução. No entanto, cada ambiente de execução pertence a apenas um usuário e não pode
ser usado por outros. Os ambientes de execução do notebook são criados com base em modelos, que
normalmente são definidos por usuários com privilégios de administrador. É possível mudar para um
ambiente de execução que use um tipo de modelo diferente a qualquer momento.
Segurança do notebook
Você controla o acesso aos notebooks usando papéis do Identity and Access Management (IAM). Para
mais informações, consulte
Conceder acesso a notebooks.
O BigQuery Studio permite salvar, compartilhar e gerenciar versões de notebooks. A tabela a seguir lista as regiões em que o BigQuery Studio está disponível:
Para monitorar o uso de slots do notebook do BigQuery Studio, consulte o relatório do Cloud Billing no console do Google Cloud . No relatório do Cloud Billing, aplique um filtro com o rótulo goog-bq-feature-type e o valor BQ_STUDIO_NOTEBOOK para conferir o uso e os custos de slots do notebook do BigQuery Studio.
[[["Fácil de entender","easyToUnderstand","thumb-up"],["Meu problema foi resolvido","solvedMyProblem","thumb-up"],["Outro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Informações incorretas ou exemplo de código","incorrectInformationOrSampleCode","thumb-down"],["Não contém as informações/amostras de que eu preciso","missingTheInformationSamplesINeed","thumb-down"],["Problema na tradução","translationIssue","thumb-down"],["Outro","otherDown","thumb-down"]],["Última atualização 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)."]]
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