O BigQuery é data warehouse para análise totalmente gerenciado, em escala de petabyte e econômico do Google Cloudque permite executar análises em vastos volumes de dados quase em tempo real. Com o BigQuery, não há
infraestrutura para configurar ou gerenciar, permitindo que você se concentre em conseguir insights
significativos com o SQL padrão e aproveitar modelos de preços flexíveis
em opções sob-demanda e de taxa fixa.
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Treinamento
Treinamento e tutoriais
Solução de início rápido para data warehouse com o BigQuery
Implantar e usar uma amostra de data warehouse com o BigQuery
Treinamento
Treinamento e tutoriais
BigQuery for Data Warehousing
Conheça as práticas recomendadas para extrair, transformar e carregar dados no Google Cloud com o BigQuery.
Treinamento
Treinamento e tutoriais
Como fazer o pré-processamento de dados do BigQuery com o PySpark no Dataproc
Aprenda a criar um pipeline de processamento de dados usando o Apache Spark com o Dataproc no Google Cloud. Ler dados de um local de armazenamento, realizar transformações nele e gravá-los em outro local de armazenamento é um caso de uso comum em ciência de dados e engenharia de dados.
Treinamento
Treinamento e tutoriais
BigQuery para análise de dados
Saiba como consultar, ingerir, otimizar, visualizar e até mesmo criar modelos de machine learning em SQL dentro do BigQuery.
Treinamento
Treinamento e tutoriais
BigQuery for Marketing Analysts
Acesse informações repetíveis, escalonáveis e valiosas sobre seus dados aprendendo a consultá-los com o BigQuery.
Treinamento
Treinamento e tutoriais
BigQuery for Machine Learning
Teste diferentes tipos de modelos no BigQuery Machine Learning e aprenda a criar um modelo.
Caso de uso
Casos de uso
Como migrar data warehouses para o BigQuery
Conheça padrões e recomendações sobre a transição do data warehouse local para o BigQuery.
Migração
Padrões
BigQuery
Caso de uso
Casos de uso
Como visualizar dados do BigQuery em um notebook do Jupyter
Use a biblioteca de cliente em Python do BigQuery e o Pandas em um notebook do Jupyter para visualizar os dados em uma tabela de amostra do BigQuery.
Exemplo de código
Exemplos de código
Cliente: criar credenciais com escopos
Crie credenciais com escopos das APIs do Drive e do BigQuery.
Exemplo de código
Exemplos de código
Cliente: criar credenciais com Application Default Credentials
Crie um cliente do BigQuery usando a Application Default Credentials.
Exemplo de código
Exemplos de código
Cliente: criar com a chave da conta de serviço
Crie um cliente do BigQuery usando um arquivo de chave da conta de serviço.
Exemplo de código
Exemplos de código
Amostras em Python
Como trabalhar com o BigQuery com a biblioteca de cliente Python do Google Cloud
Exemplo de código
Exemplos de código
Exemplos Node.js
Amostras da biblioteca de cliente Node.js para BigQuery
Exemplo de código
Exemplos de código
Amostra simples em C#
Um programa em C# e snippets de código simples para interagir com o BigQuery
Exemplo de código
Exemplos de código
BigQuery e Cloud Monitoring no App Engine com Java 8
Este demonstração de API mostra como executar um aplicativo de ambiente padrão do App Engine com dependências no BigQuery e no Cloud Monitoring.
Exemplo de código
Exemplos de código
Todas as amostras
Navegar por todas as amostras do BigQuery
Exceto em caso de indicação contrária, o conteúdo desta página é licenciado de acordo com a Licença de atribuição 4.0 do Creative Commons, e as amostras de código são licenciadas de acordo com a Licença Apache 2.0. Para mais detalhes, consulte as políticas do site do Google Developers. Java é uma marca registrada da Oracle e/ou afiliadas.
Última atualização 2025-08-17 UTC.
[[["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 2025-08-17 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)"]]
