BigQuery adalah data warehouse analisis yang terkelola sepenuhnya, berskala petabyte, dan hemat biaya dari Google Cloud, yang memungkinkan Anda menjalankan analisis pada sejumlah besar data secara hampir real time. Dengan BigQuery, tidak ada infrastruktur yang harus disiapkan atau dikelola, sehingga Anda dapat fokus menemukan insight bermanfaat menggunakan GoogleSQL dan memanfaatkan model harga yang fleksibel dengan berbagai opsi on demand dan tarif tetap.
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Pelatihan
Pelatihan dan tutorial
Solusi Praktis Data Warehouse dengan BigQuery
Men-deploy dan menggunakan data warehouse sampel dengan BigQuery.
Pelatihan
Pelatihan dan tutorial
BigQuery untuk Data Warehousing
Pelajari praktik terbaik untuk mengekstraksi, mentransformasi, dan memuat data Anda ke Google Cloud dengan BigQuery.
Pelatihan
Pelatihan dan tutorial
Melakukan pra-pemrosesan Data BigQuery dengan PySpark di Dataproc
Pelajari cara membuat pipeline pemrosesan data menggunakan Apache Spark dengan Dataproc di Google Cloud. Biasanya dalam ilmu data dan rekayasa data, membaca data dari satu lokasi penyimpanan, menjalankan transformasi di dalamnya, dan menulisnya ke lokasi penyimpanan lain adalah kasus-kasus penggunaan yang umum terjadi.
Pelatihan
Pelatihan dan tutorial
BigQuery untuk Analisis Data
Pelajari cara membuat kueri, menyerap, mengoptimalkan, memvisualisasikan, dan bahkan membuat model machine learning di SQL di dalam BigQuery.
Pelatihan
Pelatihan dan tutorial
BigQuery untuk Analis Pemasaran
Dapatkan insight yang dapat diulang, skalabel, dan berharga tentang data Anda dengan mempelajari cara membuat kuerinya menggunakan BigQuery.
Pelatihan
Pelatihan dan tutorial
BigQuery untuk Machine Learning
Bereksperimenlah dengan berbagai jenis model di Machine Learning BigQuery, dan pelajari cara membuat model yang baik.
Kasus penggunaan
Kasus penggunaan
Memigrasikan data warehouses ke BigQuery
Pelajari pola dan rekomendasi untuk mentransisikan data warehouse lokal Anda ke BigQuery.
Migrasi
Pola
BigQuery
Kasus penggunaan
Kasus penggunaan
Memvisualisasikan data BigQuery dalam notebook Jupyter
Gunakan library klien Python BigQuery dan Pandas di notebook Jupyter untuk memvisualisasikan data dalam tabel sampel BigQuery.
Contoh kode
Contoh Kode
Klien: Membuat kredensial dengan cakupan
Buat kredensial dengan cakupan Drive dan BigQuery API.
Contoh kode
Contoh Kode
Klien: Membuat kredensial dengan kredensial default aplikasi
Membuat klien BigQuery menggunakan kredensial default aplikasi.
Contoh kode
Contoh Kode
Klien: Membuat dengan kunci akun layanan
Membuat klien BigQuery menggunakan file kunci akun layanan.
Contoh kode
Contoh Kode
Contoh Python
Bekerja dengan BigQuery menggunakan library klien Google Cloud Python
Contoh kode
Contoh Kode
Contoh Node.js
Contoh library klien Node.js untuk BigQuery
Contoh kode
Contoh Kode
Contoh sederhana C#
Program C# sederhana dan cuplikan kode untuk berinteraksi dengan BigQuery
Contoh kode
Contoh Kode
BigQuery dan Cloud Monitoring di App Engine dengan Java 8
Katalog API ini menunjukkan cara menjalankan aplikasi lingkungan standar App Engine dengan dependensi di BigQuery dan Cloud Monitoring.
Contoh kode
Contoh Kode
Semua contoh
Jelajahi semua contoh untuk BigQuery
Kecuali dinyatakan lain, konten di halaman ini dilisensikan berdasarkan Lisensi Creative Commons Attribution 4.0, sedangkan contoh kode dilisensikan berdasarkan Lisensi Apache 2.0. Untuk mengetahui informasi selengkapnya, lihat Kebijakan Situs Google Developers. Java adalah merek dagang terdaftar dari Oracle dan/atau afiliasinya.
Terakhir diperbarui pada 2025-08-17 UTC.
[[["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 2025-08-17 UTC."],[[["\u003cp\u003eBigQuery is a fully managed, petabyte-scale data warehouse service by Google Cloud, designed for running real-time analytics on massive datasets.\u003c/p\u003e\n"],["\u003cp\u003eIt offers flexible pricing models, including on-demand and flat-rate options, allowing users to optimize costs based on their needs.\u003c/p\u003e\n"],["\u003cp\u003eBigQuery provides comprehensive documentation and guides for various tasks, including quickstarts, table management, data loading, and machine learning integration.\u003c/p\u003e\n"],["\u003cp\u003eResources are available for users, covering topics like pricing, release notes, locations, cost control, troubleshooting, and support.\u003c/p\u003e\n"],["\u003cp\u003eTraining, use cases, and code samples are provided to assist users with data warehousing, data analysis, machine learning, and migrating data warehouses to BigQuery, along with showcasing code for various client-side integrations.\u003c/p\u003e\n"]]],[],null,["# BigQuery documentation\n======================\n\n[Read product documentation](/bigquery/docs/introduction)\nBigQuery is Google Cloud's fully managed, petabyte-scale, and\ncost-effective analytics data warehouse that lets you run analytics over\nvast amounts of data in near real time. With BigQuery, there's\nno infrastructure to set up or manage, letting you focus on finding meaningful\ninsights using GoogleSQL and taking advantage of flexible pricing models\nacross on-demand and flat-rate options.\n[Learn more](/bigquery/docs/introduction)\n[Get started for free](https://console.cloud.google.com/freetrial) \n\n#### Start your proof of concept with $300 in free credit\n\n- Get access to Gemini 2.0 Flash Thinking\n- Free monthly usage of popular products, including AI APIs and BigQuery\n- No automatic charges, no commitment \n[View free product offers](/free/docs/free-cloud-features#free-tier) \n\n#### Keep exploring with 20+ always-free products\n\n\nAccess 20+ free products for common use cases, including AI APIs, VMs, data warehouses,\nand more.\n\nDocumentation resources\n-----------------------\n\nFind quickstarts and guides, review key references, and get help with common issues. \nformat_list_numbered\n\n### Guides\n\n-\n\n\n Quickstarts:\n [Console](/bigquery/docs/quickstarts/query-public-dataset-console),\n\n [Command line](/bigquery/docs/quickstarts/load-data-bq),\n or\n [Client libraries](/bigquery/docs/quickstarts/quickstart-client-libraries)\n\n\n-\n\n [Creating and using tables](/bigquery/docs/tables)\n\n-\n\n [Introduction to partitioned tables](/bigquery/docs/partitioned-tables)\n\n-\n\n [Introduction to BigQuery ML](/bigquery/docs/bqml-introduction)\n\n-\n\n [Predefined roles and permissions](/bigquery/docs/access-control)\n\n-\n\n [Introduction to loading data](/bigquery/docs/loading-data)\n\n-\n\n [Loading CSV data from Cloud Storage](/bigquery/docs/loading-data-cloud-storage-csv)\n\n-\n\n [Exporting table data](/bigquery/docs/exporting-data)\n\n-\n\n [Create machine learning models in BigQuery ML](/bigquery/docs/create-machine-learning-model)\n\n-\n\n [Querying external data sources](/bigquery/external-data-sources)\n\n-\n\n [Introduction to vector search](/bigquery/docs/vector-search-intro)\n\nfind_in_page\n\n### Reference\n\n-\n\n [Functions in GoogleSQL](/bigquery/docs/reference/standard-sql/functions-all)\n\n-\n\n [Operators in GoogleSQL](/bigquery/docs/reference/standard-sql/operators)\n\n-\n\n [Conditional expressions in GoogleSQL](/bigquery/docs/reference/standard-sql/conditional_expressions)\n\n-\n\n [Date functions in GoogleSQL](/bigquery/docs/reference/standard-sql/date_functions)\n\n-\n\n [Query syntax in GoogleSQL](/bigquery/docs/reference/standard-sql/query-syntax)\n\n-\n\n [String functions in GoogleSQL](/bigquery/docs/reference/standard-sql/string_functions)\n\n-\n\n [Using the bq command-line tool](/bigquery/docs/bq-command-line-tool)\n\n-\n\n [End-to-end journey for machine learning models](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-e2e-journey)\n\n-\n\n [BigQuery API Client Libraries](/bigquery/docs/reference/libraries)\n\n-\n\n [Creating and training models](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create)\n\n-\n\n [Public datasets](/bigquery/public-data)\n\n-\n\n [Feature preprocessing](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-preprocess-overview)\n\ninfo\n\n### Resources\n\n-\n\n [Pricing](/bigquery/pricing)\n\n-\n\n [Release notes](/bigquery/docs/release-notes)\n\n-\n\n [Locations](/bigquery/docs/locations)\n\n-\n\n [Getting support](/bigquery/docs/getting-support)\n\n-\n\n [Quotas and limits](/bigquery/quotas)\n\n-\n\n [Controlling costs](/bigquery/docs/controlling-costs)\n\n-\n\n [Creating custom cost controls](/bigquery/docs/custom-quotas)\n\n-\n\n [Troubleshooting BigQuery quota errors](/bigquery/docs/troubleshoot-quotas)\n\n-\n\n [Billing questions](/bigquery/docs/billing-questions)\n\nRelated resources\n-----------------\n\nTraining and tutorials \nUse cases \nCode samples \nExplore self-paced training, use cases, reference architectures, and code samples with examples of how to use and connect Google Cloud services. Training \nTraining and tutorials\n\n### Data Warehouse with BigQuery Jump Start Solution\n\n\nDeploy and use a sample data warehouse with BigQuery.\n\n\n[Learn more](https://cloud.google.com/architecture/big-data-analytics/data-warehouse) \nTraining \nTraining and tutorials\n\n### BigQuery for Data Warehousing\n\n\nLearn best practices for extracting, transforming, and loading your data into Google Cloud with BigQuery.\n\n\n[Learn more](https://www.cloudskillsboost.google/course_templates/679) \nTraining \nTraining and tutorials\n\n### Preprocessing BigQuery Data with PySpark on Dataproc\n\n\nLearn to create a data processing pipeline using Apache Spark with Dataproc on Google Cloud. It is a common use case in data science and data engineering to read data from one storage location, perform transformations on it and write it into another storage location.\n\n\n[Learn more](https://codelabs.developers.google.com/codelabs/pyspark-bigquery/) \nTraining \nTraining and tutorials\n\n### BigQuery For Data Analysis\n\n\nLearn how to query, ingest, optimize, visualize, and even build machine learning models in SQL inside of BigQuery.\n\n\n[Learn more](https://www.cloudskillsboost.google/course_templates/865) \nTraining \nTraining and tutorials\n\n### BigQuery for Marketing Analysts\n\n\nGet repeatable, scalable, and valuable insights into your data by learning how to query it using BigQuery.\n\n\n[Learn more](https://www.cloudskillsboost.google/course_templates/678) \nTraining \nTraining and tutorials\n\n### BigQuery for Machine Learning\n\n\nExperiment with different model types in BigQuery Machine Learning, and learn what makes a good model.\n\n\n[Learn more](https://www.cloudskillsboost.google/course_templates/680) \nUse case \nUse cases\n\n### Migrating data warehouses to BigQuery\n\n\nLearn patterns and recommendations for transitioning your on-premises data warehouse to BigQuery.\n\nMigration Patterns BigQuery\n\n\u003cbr /\u003e\n\n[Learn more](/solutions/migration/dw2bq/dw-bq-migration-overview) \nUse case \nUse cases\n\n### Visualizing BigQuery data in a Jupyter notebook\n\n\nUse the BigQuery Python client library and Pandas in a Jupyter notebook to visualize data in a BigQuery sample table.\n\n\n[Learn more](/bigquery/docs/visualize-jupyter) \nCode sample \nCode Samples\n\n### Client: Create credentials with scopes\n\n\nCreate credentials with Drive and BigQuery API scopes.\n\n\n[Get started](/bigquery/docs/samples/bigquery-auth-drive-scope) \nCode sample \nCode Samples\n\n### Client: Create credentials with application default credentials\n\n\nCreate a BigQuery client using application default credentials.\n\n\n[Get started](/bigquery/docs/samples/bigquery-client-default-credentials) \nCode sample \nCode Samples\n\n### Client: Create with service account key\n\n\nCreate a BigQuery client using a service account key file.\n\n\n[Get started](/bigquery/docs/samples/bigquery-client-json-credentials) \nCode sample \nCode Samples\n\n### Python samples\n\n\nWorking with BigQuery with the Google Cloud Python client library\n\n\n[Open GitHub\narrow_forward](https://github.com/googleapis/python-bigquery/tree/main/samples) \nCode sample \nCode Samples\n\n### Node.js samples\n\n\nSamples for the Node.js client library sfor BigQuery\n\n\n[Open GitHub\narrow_forward](https://github.com/googleapis/nodejs-bigquery/tree/main/samples) \nCode sample \nCode Samples\n\n### C# simple sample\n\n\nA simple C# program and code snippets for interacting with BigQuery\n\n\n[Open GitHub\narrow_forward](https://github.com/GoogleCloudPlatform/dotnet-docs-samples/tree/master/bigquery/api) \nCode sample \nCode Samples\n\n### BigQuery and Cloud Monitoring on App Engine with Java 8\n\n\nThis API Showcase demonstrates how to run an App Engine standard environment application with dependencies on both BigQuery and Cloud Monitoring.\n\n\n[Open GitHub\narrow_forward](https://github.com/GoogleCloudPlatform/java-docs-samples/tree/main/appengine-java8/bigquery) \nCode sample \nCode Samples\n\n### All samples\n\n\nBrowse all samples for BigQuery\n\n\n[Get started](/bigquery/docs/samples)\n\nRelated videos\n--------------\n\n### Try BigQuery for yourself\n\nCreate an account to evaluate how our products perform in real-world scenarios. \nNew customers also get $300 in free credits to run, test, and deploy workloads. \n[Try BigQuery free](https://console.cloud.google.com/freetrial)"]]
