O Dataflow é um serviço gerenciado para executar uma ampla variedade de padrões de processamento de dados. A documentação neste site mostra como implantar os pipelines de processamento de dados de streaming e em lote usando o Dataflow, incluindo instruções de uso dos recursos de serviço.
O SDK do Apache Beam
é um modelo de programação de código aberto que permite desenvolver pipelines de lote e
de streaming. Você cria pipelines com um programa do Apache Beam
e os executa no serviço do Dataflow. A
documentação do
Apache Beam fornece informações conceituais aprofundadas e material de
referência para o modelo de programação, os SDKs e outros executores do Apache Beam.
Para aprender os conceitos básicos do Apache Beam, consulte o
Tour do Beam e o Beam Playground.
O repositório do
Manual do Dataflow também fornece pipelines prontos para lançamento e independentes,
além dos casos de uso mais comuns do Dataflow.
Apache, Apache Beam, Beam, o logotipo do Beam e o mascote Firefly são marcas registradas da Apache Software Foundation nos Estados Unidos e/ou em outros países.
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Caso de uso
Casos de uso
Executar cargas de trabalho de HPC altamente paralelas
Com o Dataflow, é possível executar cargas de trabalho altamente paralelas em um único pipeline, melhorando a eficiência e facilitando o gerenciamento do fluxo de trabalho.
Streaming
Caso de uso
Casos de uso
Executar inferência com o Dataflow ML
O Dataflow ML permite usar o Dataflow para implantar e gerenciar pipelines completos de machine learning (ML). Use modelos de ML para fazer inferências locais e remotas com pipelines de streaming e em lote. Use ferramentas de processamento de dados para preparar seus dados para o treinamento de modelo e processar os resultados dos modelos.
ML
Streaming
Caso de uso
Casos de uso
Criar um pipeline de streaming de e-commerce
Criar um aplicativo de amostra de e-commerce completo que transmite dados de uma loja on-line para o BigQuery e o Bigtable. O aplicativo de amostra ilustra casos comuns e práticas recomendadas para implementar a análise de dados de streaming e a inteligência artificial (IA) em tempo real.
e-commerce
Streaming
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 2025-08-18 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 2025-08-18 UTC."],[[["\u003cp\u003eDataflow is a managed service for executing batch and streaming data processing pipelines, with comprehensive documentation available on deployment and feature usage.\u003c/p\u003e\n"],["\u003cp\u003eThe Apache Beam SDK, an open-source programming model, is used to create pipelines that can be run on the Dataflow service, and its documentation can be found on the Apache website.\u003c/p\u003e\n"],["\u003cp\u003eVarious guides, references, and resources are provided, including quickstarts for creating pipelines in Java, Python, and Go, along with troubleshooting information.\u003c/p\u003e\n"],["\u003cp\u003eDataflow supports highly parallel workloads, machine learning inference, and the creation of ecommerce streaming pipelines, which are detailed in use case examples.\u003c/p\u003e\n"],["\u003cp\u003eThe documentation provides access to code samples, pricing information, quotas, release notes, support and billing help, all relevant to the managed service.\u003c/p\u003e\n"]]],[],null,["# Dataflow documentation\n======================\n\n[Read product documentation](/dataflow/docs/overview)\nDataflow is a managed service for executing a wide variety of data\nprocessing patterns. The documentation on this site shows you how to deploy\nyour batch and streaming data processing pipelines using\nDataflow, including directions for using service features.\n\n\nThe Apache Beam SDK\nis an open source programming model that enables you to develop both batch\nand streaming pipelines. You create your pipelines with an Apache Beam\nprogram and then run them on the Dataflow service. The\n[Apache Beam\ndocumentation](https://beam.apache.org/documentation/) provides in-depth conceptual information and reference\nmaterial for the Apache Beam programming model, SDKs, and other runners.\n\nTo learn basic Apache Beam concepts, see the [Tour of Beam](https://tour.beam.apache.org/) and [Beam Playground](https://play.beam.apache.org/).\nThe [Dataflow Cookbook](https://github.com/GoogleCloudPlatform/dataflow-cookbook) repository also provides ready-to-launch and self-contained pipelines\nand the most common Dataflow use cases. \n*Apache, Apache Beam, Beam, the\nBeam logo, and the Beam firefly mascot are registered trademarks of The Apache Software Foundation in the\nUnited States and/or other countries.* [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 [Create a Dataflow pipeline using Java](/dataflow/docs/quickstarts/create-pipeline-java)\n\n-\n\n [Create a Dataflow pipeline using Python](/dataflow/docs/quickstarts/create-pipeline-python)\n\n-\n\n [Create a Dataflow pipeline using Go](/dataflow/docs/quickstarts/create-pipeline-go)\n\n-\n\n [Create a streaming pipeline using a Dataflow template](/dataflow/docs/quickstarts/create-streaming-pipeline-template)\n\n-\n\n [Build and run a Flex Template](/dataflow/docs/guides/templates/using-flex-templates)\n\n-\n\n [Deploy Dataflow pipelines](/dataflow/docs/guides/deploying-a-pipeline)\n\n-\n\n [Develop with notebooks](/dataflow/docs/guides/interactive-pipeline-development)\n\n-\n\n [Troubleshooting and debugging](/dataflow/docs/guides/troubleshooting-your-pipeline)\n\nfind_in_page\n\n### Reference\n\n-\n\n [Install the Apache Beam SDK](/dataflow/docs/guides/installing-beam-sdk)\n\n-\n\n [Java SDK](https://beam.apache.org/documentation/sdks/javadoc/current/)\n\n-\n\n [Python SDK](https://beam.apache.org/documentation/sdks/pydoc/current/)\n\n-\n\n [Go SDK](https://pkg.go.dev/github.com/apache/beam/sdks/v2/go/pkg/beam)\n\n-\n\n [SDK version support status](/dataflow/docs/support/sdk-version-support-status)\n\n-\n\n [REST API](/dataflow/docs/reference/rest)\n\n-\n\n [gcloud command-line functions](/sdk/gcloud/reference/dataflow)\n\n-\n\n [Google-provided templates](/dataflow/docs/concepts/dataflow-templates)\n\ninfo\n\n### Resources\n\n-\n\n [Dataflow code samples](/dataflow/docs/samples)\n\n-\n\n [Pricing](/dataflow/pricing)\n\n-\n\n [Quotas and limits](/dataflow/quotas)\n\n-\n\n [Release Notes](/dataflow/docs/release-notes)\n\n-\n\n [Getting support](/dataflow/docs/support/getting-support)\n\n-\n\n [Billing questions](/dataflow/docs/support/billing-questions)\n\nRelated resources\n-----------------\n\nExplore self-paced training, use cases, reference architectures, and code samples with examples of how to use and connect Google Cloud services. Use case \nUse cases\n\n### Run HPC highly parallel workloads\n\n\nWith Dataflow, you can run your highly parallel workloads in a single pipeline, improving efficiency and making your workflow easier to manage.\n\nStreaming\n\n\u003cbr /\u003e\n\n[Learn more](/dataflow/docs/hpc-ep) \nUse case \nUse cases\n\n### Run inference with Dataflow ML\n\n\nDataflow ML lets you use Dataflow to deploy and manage complete machine learning (ML) pipelines. Use ML models to do local and remote inference with batch and streaming pipelines. Use data processing tools to prepare your data for model training and to process the results of the models.\n\nML Streaming\n\n\u003cbr /\u003e\n\n[Learn more](/dataflow/docs/machine-learning) \nUse case \nUse cases\n\n### Create an ecommerce streaming pipeline\n\n\nBuild an end-to-end ecommerce sample application that streams data from a webstore to BigQuery and Bigtable. The sample application illustrates common use cases and best practices for implementing streaming data analytics and real-time artificial intelligence (AI).\n\necommerce Streaming\n\n\u003cbr /\u003e\n\n[Learn more](/dataflow/docs/tutorials/ecommerce-retail-pipeline)\n\nRelated videos\n--------------"]]
