BigQuery 是 Google Cloud经济实惠的全托管式 PB 级分析数据仓库，可让您近乎实时地分析大量数据。借助 BigQuery，您无需设置或管理基础架构，即可专注于使用 GoogleSQL 寻找有意义的数据洞见，并在按需价格和固定价格方案中使用灵活的价格模式。了解详情
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培训和教程
数据仓库与 BigQuery 快速起步解决方案
部署示例数据仓库并将其与 BigQuery 搭配使用。
培训
培训和教程
用于数据仓储的 BigQuery
了解使用 BigQuery 提取数据、转换数据以及将数据加载到 Google Cloud 中的最佳实践。
培训
培训和教程
在 Dataproc 上使用 PySpark 预处理 BigQuery 数据
了解如何在 Google Cloud上结合使用 Apache Spark 和 Dataproc 来创建数据处理流水线。数据科学和数据工程领域有一种常见的使用场景，即，从一个存储位置读取数据，对数据执行转换，然后将数据写入另一个存储位置。
培训
培训和教程
用于数据分析的 BigQuery
了解如何在 BigQuery 中使用 SQL 查询、提取、优化、直观呈现甚至构建机器学习模型。
培训
培训和教程
面向营销分析师的 BigQuery
通过了解如何使用 BigQuery 查询数据来获得可重复、可扩缩、有价值的数据洞见。
培训
培训和教程
面向机器学习的 BigQuery
在 BigQuery Machine Learning 中尝试不同的模型类型，并了解如何构建出色的模型。
使用场景
使用场景
将数据仓库迁移到 BigQuery
了解将本地数据仓库迁移到 BigQuery 的模式和建议。
迁移
模式
BigQuery
使用场景
使用场景
在 Jupyter 笔记本中直观呈现 BigQuery 数据
在 Jupyter 笔记本中使用 BigQuery Python 客户端库和 Pandas 来直观呈现 BigQuery 示例表中的数据。
代码示例
代码示例
客户端：创建具有范围的凭据
使用云端硬盘和 BigQuery API 范围创建凭据。
代码示例
代码示例
客户端：使用应用默认凭据创建凭据
使用应用默认凭据创建 BigQuery 客户端。
代码示例
代码示例
客户端：使用服务账号密钥创建
使用服务账号密钥文件创建 BigQuery 客户端。
代码示例
代码示例
Python 示例
将 BigQuery 与 Google Cloud Python 客户端库搭配使用
代码示例
代码示例
Node.js 示例
适用于 BigQuery 的 Node.js 客户端库示例
代码示例
代码示例
C# 简单示例
用于与 BigQuery 交互的简单 C# 程序和代码段
代码示例
代码示例
通过 Java 8 在 App Engine 上使用 BigQuery 和 Cloud Monitoring
此“API 展示”演示了如何在 BigQuery 和 Cloud Monitoring 上运行具有依赖项的 App Engine 标准环境应用。
代码示例
代码示例
所有示例
浏览 BigQuery 的所有示例
如未另行说明，那么本页面中的内容已根据知识共享署名 4.0 许可获得了许可，并且代码示例已根据 Apache 2.0 许可获得了许可。有关详情，请参阅 Google 开发者网站政策。Java 是 Oracle 和/或其关联公司的注册商标。
最后更新时间 (UTC)：2025-09-11。
[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC)：2025-09-11。"],[[["\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)"]]
