Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Pengantar notebook
Dokumen ini memuat pengantar
Notebook Colab Enterprise
di BigQuery. Anda dapat menggunakan notebook untuk menyelesaikan
alur kerja analisis dan machine learning (ML) dengan menggunakan SQL, Python, serta paket
dan API umum lainnya. Notebook menawarkan kolaborasi dan pengelolaan yang lebih baik
dengan opsi berikut:
Bagikan notebook kepada pengguna dan grup tertentu dengan Identity and Access Management (IAM).
Tinjau histori versi notebook.
Kembalikan ke atau cabang dari versi notebook sebelumnya.
Notebook adalah aset kode BigQuery Studio
yang didukung oleh Dataform.
Kueri tersimpan juga merupakan aset kode.
Semua aset kode disimpan di region
default. Mengupdate region default akan mengubah
region untuk semua aset kode yang dibuat setelah waktu tersebut.
Kemampuan notebook hanya tersedia di konsol Google Cloud .
Manfaat
Notebook di BigQuery menawarkan manfaat berikut:
BigQuery DataFrames
diintegrasikan dengan notebook, tanpa perlu penyiapan. BigQuery DataFrames adalah
API Python yang dapat Anda gunakan untuk menganalisis data BigQuery dalam
skala besar menggunakan
pandas DataFrame
dan
API scikit-learn.
Runtime notebook adalah mesin virtual Compute Engine yang dialokasikan kepada
pengguna tertentu untuk mengaktifkan eksekusi kode di notebook. Beberapa notebook mungkin
memiliki runtime yang sama. Namun, setiap runtime hanya dimiliki satu pengguna dan tidak dapat
digunakan pengguna lain. Runtime notebook dibuat berdasarkan template, yang
biasanya ditentukan oleh pengguna dengan hak istimewa administratif. Anda dapat beralih ke
runtime yang menggunakan jenis template berbeda kapan saja.
Keamanan notebook
Anda dapat mengontrol akses ke notebook dengan peran Identity and Access Management (IAM). Untuk
mengetahui informasi selengkapnya, lihat
Memberikan akses ke notebook.
BigQuery Studio memungkinkan Anda menyimpan, membagikan, dan mengelola berbagai versi notebook. Tabel berikut mencantumkan region tempat BigQuery Studio tersedia:
Untuk mengetahui informasi harga tentang notebook BigQuery Studio, lihat Harga runtime notebook.
Memantau penggunaan slot
Anda dapat memantau penggunaan slot notebook BigQuery Studio dengan melihat laporan Penagihan Cloud di konsol Google Cloud . Dalam laporan Penagihan Cloud, terapkan filter dengan label goog-bq-feature-type dengan nilai BQ_STUDIO_NOTEBOOK untuk melihat penggunaan dan biaya slot dari notebook BigQuery Studio.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 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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