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Dataflow se basa en el proyecto de código abierto de Apache Beam. Puedes usar el SDK de Apache Beam a fin de compilar canalizaciones para Dataflow.
En este documento, se enumeran algunos recursos para comenzar a usar la programación de Apache Beam.
Instala el SDK de Apache Beam:
se muestra cómo instalar el SDK de Apache Beam para que puedas ejecutar las
canalizaciones en el servicio de Dataflow.
Guía de programación de Apache Beam: proporciona orientación para usar las clases del SDK de Apache Beam para compilar y probar
tu canalización.
Recorrido por Apache Beam:
una guía de aprendizaje que puedes usar para familiarizarte con Apache Beam.
Las unidades de aprendizaje están acompañadas de ejemplos de código que puedes ejecutar y modificar.
Zona de pruebas de Apache Beam:
un entorno interactivo para probar transformaciones y ejemplos de Apache Beam
sin tener que instalar Apache Beam en tu entorno.
En el sitio web de Apache Beam, también puedes encontrar información para diseñar, crear y probar tu canalización:
Cómo diseñar tu canalización:
se muestra cómo determinar la estructura de tu canalización, cómo elegir qué
transformaciones se aplican a tus datos y cómo definir tus métodos de entrada
y salida.
Crea tu canalización:
se explica la mecánica de usar clases en los SDK de Apache Beam y los
pasos necesarios para compilar una canalización.
Prueba tu canalización: Se presentan las recomendaciones para probar las canalizaciones.
Puedes usar los siguientes ejemplos de GitHub de Apache Beam para comenzar
a compilar una canalización de transmisión:
[[["Fácil de comprender","easyToUnderstand","thumb-up"],["Resolvió mi problema","solvedMyProblem","thumb-up"],["Otro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Información o código de muestra incorrectos","incorrectInformationOrSampleCode","thumb-down"],["Faltan la información o los ejemplos que necesito","missingTheInformationSamplesINeed","thumb-down"],["Problema de traducción","translationIssue","thumb-down"],["Otro","otherDown","thumb-down"]],["Última actualización: 2025-03-24 (UTC)"],[[["\u003cp\u003eDataflow utilizes the open-source Apache Beam project, allowing users to construct pipelines with the Apache Beam SDK.\u003c/p\u003e\n"],["\u003cp\u003eResources are provided for installing the Apache Beam SDK, guiding users on how to run their pipelines within the Dataflow service.\u003c/p\u003e\n"],["\u003cp\u003eThe Apache Beam website offers resources covering pipeline design, creation, and testing best practices, using the classes in the Apache Beam SDK.\u003c/p\u003e\n"],["\u003cp\u003eThe Apache Beam playground offers an interactive environment to try out the Apache Beam transforms without needing to install Apache Beam.\u003c/p\u003e\n"],["\u003cp\u003eExample streaming pipelines, including word extraction, word count, and wordcap, are available on the Apache Beam GitHub repository in Java, Python, and Go.\u003c/p\u003e\n"]]],[],null,["# Use Apache Beam to build pipelines\n\nDataflow is built on the open source\n[Apache Beam](https://beam.apache.org/) project. You can\nuse the Apache Beam SDK to build pipelines for Dataflow.\nThis document lists some resources for getting started with Apache Beam\nprogramming.\n\nGet started\n-----------\n\n- [Install the Apache Beam SDK](/dataflow/docs/guides/installing-beam-sdk):\n Shows how to install the Apache Beam SDK so that you can run your\n pipelines in Dataflow.\n\n- [Create a Java pipeline](/dataflow/docs/guides/create-pipeline-java): Shows\n how to create a pipeline with the Apache Beam Java SDK and run the\n pipeline in Dataflow.\n\n- [Create a Python pipeline](/dataflow/docs/guides/create-pipeline-python):\n Shows how to create a pipeline with the Apache Beam Python SDK and run the\n pipeline in Dataflow.\n\n- [Create a Go pipeline](/dataflow/docs/guides/create-pipeline-go): Shows\n how to create a pipeline with the Apache Beam Go SDK and run the pipeline\n in Dataflow.\n\nLearn Apache Beam\n-----------------\n\nYou can use the following pages on the Apache Beam website to learn about\nApache Beam programming.\n\n- [Apache Beam programming guide](https://beam.apache.org/documentation/programming-guide/):\n Provides guidance for using the Apache Beam SDK classes to build and test\n your pipeline.\n\n- [Tour of Apache Beam](https://tour.beam.apache.org/):\n A learning guide you can use to familiarize yourself with Apache Beam.\n Learning units are accompanied by code examples that you can run and modify.\n\n- [Apache Beam playground](https://play.beam.apache.org/):\n An interactive environment to try out Apache Beam transforms and examples\n without having to install Apache Beam in your environment.\n\n- [Create your pipeline](https://beam.apache.org/documentation/pipelines/create-your-pipeline/):\n Explains the mechanics of using the classes in the Apache Beam SDKs and\n the necessary steps needed to build a pipeline.\n\nDevelop pipelines\n-----------------\n\n- [Plan your pipeline](/dataflow/docs/guides/plan-pipelines): Learn how to plan\n your pipeline before you begin code development.\n\n- [Develop and test pipelines](/dataflow/docs/guides/plan-pipelines): Learn best\n practices for developing and testing your Dataflow pipeline.\n\n- [Streaming pipelines](/dataflow/docs/concepts/streaming-pipelines): Learn\n about important design considerations for streaming pipelines, including\n windows, triggers, and watermarks.\n\nCode examples\n-------------\n\nYou can use the following examples from the Apache Beam GitHub to start\nbuilding a streaming pipeline:\n\n- [Streaming word extraction](https://github.com/apache/beam/blob/master/examples/java/src/main/java/org/apache/beam/examples/complete/StreamingWordExtract.java) (Java)\n- [Streaming word count](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/streaming_wordcount.py) (Python), and\n- [`streaming_wordcap`](https://github.com/apache/beam/blob/master/sdks/go/examples/streaming_wordcap/wordcap.go) (Go).\n\nWhat's next\n-----------\n\n- [Deploy Dataflow pipelines](/dataflow/docs/guides/deploying-a-pipeline).\n- [Use the Dataflow job monitoring interface](/dataflow/docs/guides/monitoring-overview)."]]