{"id":8090,"date":"2022-02-28T14:53:22","date_gmt":"2022-02-28T13:53:22","guid":{"rendered":"https:\/\/aprenderbigdata.com\/?p=8090"},"modified":"2025-08-17T10:28:09","modified_gmt":"2025-08-17T09:28:09","slug":"arquitectura-lambda","status":"publish","type":"post","link":"https:\/\/aprenderbigdata.com\/arquitectura-lambda\/","title":{"rendered":"Arquitectura Lambda en el Procesamiento de Datos"},"content":{"rendered":"\n<p>En esta entrada aprender\u00e1s las caracter\u00edsticas m\u00e1s importantes de la arquitectura lambda como framework de procesamiento de grandes cantidades de datos. Conocer\u00e1s de una manera sencilla las capas que la integran, las tecnolog\u00edas m\u00e1s comunes y algunas alternativas. \u00a1Sigue leyendo!<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><a href=\"#formacion\"><img decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/aprenderbigdata.com\/wp-content\/uploads\/Arquitectura-lambda-1024x576.jpg\" alt=\"Arquitectura lambda\" class=\"wp-image-8092\" srcset=\"https:\/\/aprenderbigdata.com\/wp-content\/uploads\/Arquitectura-lambda-1024x576.jpg 1024w, https:\/\/aprenderbigdata.com\/wp-content\/uploads\/Arquitectura-lambda-300x169.jpg 300w, https:\/\/aprenderbigdata.com\/wp-content\/uploads\/Arquitectura-lambda-768x432.jpg 768w, https:\/\/aprenderbigdata.com\/wp-content\/uploads\/Arquitectura-lambda.jpg 1280w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/a><\/figure>\n<\/div>\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Contenidos<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-69e1a8f547b93\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-69e1a8f547b93\" checked aria-label=\"Alternar\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/aprenderbigdata.com\/arquitectura-lambda\/#%C2%BFQue-es-la-Arquitectura-Lambda\" >\u00bfQu\u00e9 es la Arquitectura Lambda?<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/aprenderbigdata.com\/arquitectura-lambda\/#Capa-de-Ingesta-o-de-Consumo-de-datos\" >Capa de Ingesta o de Consumo de datos<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/aprenderbigdata.com\/arquitectura-lambda\/#Capa-Batch\" >Capa Batch<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/aprenderbigdata.com\/arquitectura-lambda\/#Capa-de-Streaming-o-Speed-Layer\" >Capa de Streaming o Speed Layer<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/aprenderbigdata.com\/arquitectura-lambda\/#Capa-de-servicio-o-Serving-Layer\" >Capa de servicio o Serving Layer<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/aprenderbigdata.com\/arquitectura-lambda\/#Alternativas-a-la-Arquitectura-Lambda\" >Alternativas a la Arquitectura Lambda<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/aprenderbigdata.com\/arquitectura-lambda\/#Formacion-Recomendada-Arquitecto-de-Datos\" >Formaci\u00f3n Recomendada Arquitecto de Datos<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/aprenderbigdata.com\/arquitectura-lambda\/#Preparacion-para-la-certificacion-de-Google-Cloud-Cloud-Engineer-Professional-Certificate-en-Espanol\" >Preparaci\u00f3n para la certificaci\u00f3n de Google Cloud: Cloud Engineer Professional Certificate en Espa\u00f1ol<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/aprenderbigdata.com\/arquitectura-lambda\/#Preguntas-Frecuentes-%E2%80%93-FAQ\" >Preguntas Frecuentes &#8211; FAQ<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/aprenderbigdata.com\/arquitectura-lambda\/#%C2%BFCuales-son-los-componentes-principales-de-la-arquitectura-Lambda\" >\u00bfCu\u00e1les son los componentes principales de la arquitectura Lambda?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/aprenderbigdata.com\/arquitectura-lambda\/#%C2%BFComo-se-maneja-la-consistencia-de-datos-en-la-arquitectura-Lambda\" >\u00bfC\u00f3mo se maneja la consistencia de datos en la arquitectura Lambda?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/aprenderbigdata.com\/arquitectura-lambda\/#%C2%BFComo-se-compara-la-arquitectura-Lambda-con-la-arquitectura-Kappa\" >\u00bfC\u00f3mo se compara la arquitectura Lambda con la arquitectura Kappa?<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%C2%BFQue-es-la-Arquitectura-Lambda\"><\/span>\u00bfQu\u00e9 es la Arquitectura Lambda?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>La arquitectura lambda es un framework de dise\u00f1o para procesar big data que usa de forma simult\u00e1nea flujos de datos en streaming y en batch. El procesamiento en streaming consiste en procesar y analizar los datos al vuelo sin persistirlos en capas de almacenamiento. El procesamiento batch, por otro lado, procesa los datos de manera peri\u00f3dica una vez que han sido persistidos en bases de datos o en el <strong><a href=\"https:\/\/aprenderbigdata.com\/requisitos-data-warehouse\/\">data warehouse<\/a><\/strong>.<\/p>\n\n\n\n<p>La arquitectura lambda se puede usar para reducir las latencias, escalar la soluci\u00f3n y aumentar la tolerancia a fallos para procesar simult\u00e1neamente flujos de datos batch y streaming.<\/p>\n\n\n\n<p>Como hemos visto, este dise\u00f1o de arquitectura tiene dos <strong><a href=\"https:\/\/aprenderbigdata.com\/pipeline-de-datos\/\">pipelines de datos<\/a><\/strong> diferenciadas:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Pipeline de procesamiento Batch<\/li>\n\n\n\n<li>Pipeline de procesamiento en Streaming<\/li>\n<\/ul>\n\n\n\n<p>Ambas pipelines se ejecutan de forma distribuida y ayudan a analizar los datos y extraer m\u00e9tricas y vistas para tomar decisiones de negocio informadas. El caso de uso m\u00e1s com\u00fan es la recolecci\u00f3n de eventos que se a\u00f1aden a los datos existentes, de los que se determina el estado.<\/p>\n\n\n\n<p>El modelo se ide\u00f3 originalmente con 4 capas principales:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Capa-de-Ingesta-o-de-Consumo-de-datos\"><\/span>Capa de Ingesta o de Consumo de datos<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Esta capa es la responsable de obtener los datos de las fuentes. Las tecnolog\u00edas m\u00e1s populares aqu\u00ed son <strong><a href=\"https:\/\/aprenderbigdata.com\/introduccion-apache-kafka\/\">Apache Kafka<\/a><\/strong>, <strong><a href=\"https:\/\/aprenderbigdata.com\/introduccion-apache-nifi\/\">Apache NiFi<\/a><\/strong> o <strong><a href=\"https:\/\/aprenderbigdata.com\/introduccion-apache-flume\/\">Flume<\/a><\/strong>. La recolecci\u00f3n de datos puede suceder en modo batch o en streaming. Esto afectar\u00e1 a las tecnolog\u00edas que ser\u00e1n necesarias en el resto de la pipeline de datos. Las fuentes de datos usadas se pueden a\u00f1adir o eliminar seg\u00fan las demandas y necesidades del proyecto.<\/p>\n\n\n<div class=\"wp-block-image is-resized\">\n<figure class=\"aligncenter size-full\"><img decoding=\"async\" width=\"800\" height=\"202\" src=\"https:\/\/aprenderbigdata.com\/wp-content\/uploads\/arquitectura-lambda-1.jpg\" alt=\"Esquema Arquitectura Lambda Big Data\" class=\"wp-image-8099\" srcset=\"https:\/\/aprenderbigdata.com\/wp-content\/uploads\/arquitectura-lambda-1.jpg 800w, https:\/\/aprenderbigdata.com\/wp-content\/uploads\/arquitectura-lambda-1-300x76.jpg 300w, https:\/\/aprenderbigdata.com\/wp-content\/uploads\/arquitectura-lambda-1-768x194.jpg 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/><figcaption class=\"wp-element-caption\">Esquema Arquitectura Lambda Big Data<\/figcaption><\/figure>\n<\/div>\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Capa-Batch\"><\/span>Capa Batch<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>En el caso del flujo batch, la herramienta de ingesta acumula los datos en un almacenamiento persistente antes de realizar un procesamiento. Es com\u00fan que esta capa de almacenamiento sea distribuida como lo es <strong><a href=\"https:\/\/aprenderbigdata.com\/hdfs\/\">HDFS<\/a><\/strong>, tambi\u00e9n con tolerancia a fallos y replicaci\u00f3n. El procesamiento se puede realizar de una forma distribuida como <strong><a href=\"https:\/\/aprenderbigdata.com\/introduccion-apache-spark\/\">Spark<\/a><\/strong> o con bases de datos relacionales y nosql como <strong><a href=\"https:\/\/aprenderbigdata.com\/snowflake\/\">Snowflake<\/a><\/strong> o <strong><a href=\"https:\/\/aprenderbigdata.com\/azure-synapse-analytics\/\">Synapse Analytics<\/a><\/strong>.<\/p>\n\n\n\n<p>La capa batch tiene disponible todos los datasets persistidos en el sistema, puede ejecutar procesos de consolidaci\u00f3n y actualizar las vistas con los datos m\u00e1s recientes.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Capa-de-Streaming-o-Speed-Layer\"><\/span>Capa de Streaming o Speed Layer<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>La capa de streaming o <em>speed layer<\/em> procesa los datos sin necesidad de persistirlos. El prop\u00f3sito de esta capa es reducir los intervalos de tiempo que la capa Batch no es capaz de procesar. Por lo tanto, permite que los datos est\u00e9n disponibles en el menor tiempo posible, aunque para ello se deba sacrificar algo de precisi\u00f3n. Las tecnolog\u00edas que se usan para realizar esta tarea pueden ser Spark Streaming, <a href=\"https:\/\/aprenderbigdata.com\/introduccion-apache-flink\/\"><strong>Flink<\/strong><\/a> o <strong><a href=\"https:\/\/aprenderbigdata.com\/kafka-streams-ksql\/\">Kafka Streams<\/a><\/strong>.<\/p>\n\n\n\n<p>Cada evento recibido puede desencadenar este procesamiento y un cambio en las vistas y tablas asociadas, de forma que se tenga una visi\u00f3n en tiempo real. Los resultados se almacenan en <strong><a href=\"https:\/\/aprenderbigdata.com\/bases-de-datos-nosql\/\">bases de datos NoSQL<\/a><\/strong> que reducen la latencia.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Capa-de-servicio-o-Serving-Layer\"><\/span>Capa de servicio o Serving Layer<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>La \u00faltima capa de la arquitectura es la de servicio. Es la encargada de combinar los resultados de las pipelines batch y de streaming para tener una vista unificada de los resultados. Las actualizaciones de streaming se producen en tiempo real y las actualizaciones de la capa batch de manera peri\u00f3dica.<\/p>\n\n\n\n<p>La capa de servicio da respuesta a las consultas espec\u00edficas sobre los datos preprocesados. Algunas tecnolog\u00edas adecuadas para realizar esta tarea son <strong><a href=\"https:\/\/aprenderbigdata.com\/apache-druid\/\">Apache Druid<\/a><\/strong>, <strong><a href=\"https:\/\/aprenderbigdata.com\/apache-impala\/\">Apache Impala<\/a><\/strong>, <strong><a href=\"https:\/\/aprenderbigdata.com\/apache-hive\/\">Apache Hive<\/a><\/strong>, <strong><a href=\"https:\/\/aprenderbigdata.com\/cosmosdb\/\">Cosmos DB<\/a><\/strong> o <a href=\"https:\/\/aprenderbigdata.com\/elasticsearch\/\"><strong>Elasticsearch<\/strong><\/a>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Alternativas-a-la-Arquitectura-Lambda\"><\/span>Alternativas a la Arquitectura Lambda<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>La arquitectura Lambda tiene algunas limitaciones. Cada una de las capas necesita un mantenimiento espec\u00edfico, tecnolog\u00edas y aplicaciones diferentes para funcionar correctamente. Adem\u00e1s, las dos pipelines est\u00e1n totalmente acopladas, ya que la capa batch necesita sincronizar los nuevos eventos generados por la capa de streaming cuando se ejecuta.<\/p>\n\n\n\n<div class=\"wp-block-group pre-su\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<pre class=\"wp-block-preformatted\"><div class=\"su-service\"><div class=\"su-service-title\" style=\"padding-left:44px;min-height:30px;line-height:30px\"><i class=\"sui sui-info-circle\" style=\"font-size:30px;color:#e74273\"><\/i> \u00bfQuieres Convertirte en Ingeniero de Datos?<\/div><div class=\"su-service-content su-u-clearfix su-u-trim\" style=\"padding-left:44px\"><br>Consigue empleo con el <a href=\"https:\/\/aprenderbigdata.com\/curso-ingeniero-datos\/\">programa acelerado de Data Engineer<\/a><br><\/div><\/div><\/pre>\n<\/div><\/div>\n\n\n<style>.pre-su pre {<br \/>\n\tbackground-color: #fffae9 !important;<br \/>\n}<br \/>\n<\/style>\n\n\n<p>Las dos alternativas m\u00e1s usadas en sistemas de procesamiento big data son la <strong>arquitectura Kappa<\/strong> y la <strong>arquitectura Delta<\/strong>. La arquitectura Kappa se ejecuta completamente en streaming, todos los datos se tratan como eventos y son procesados cuando entran en el sistema. La arquitectura Delta a\u00f1ade una capa de almacenamiento en forma de tabla, sobre la que se persisten todos los datos y se puede usar en cualquier aplicaci\u00f3n. La tecnolog\u00eda <a href=\"https:\/\/aprenderbigdata.com\/delta-lake\/\"><strong>Delta Lake<\/strong><\/a> desarrollada por <strong><a href=\"https:\/\/aprenderbigdata.com\/databricks\/\">Databricks<\/a><\/strong> es el ejemplo m\u00e1s directo de su aplicaci\u00f3n.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"formacion\"><span class=\"ez-toc-section\" id=\"Formacion-Recomendada-Arquitecto-de-Datos\"><\/span>Formaci\u00f3n Recomendada Arquitecto de Datos<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:33.33%\"><div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><a href=\"https:\/\/imp.i384100.net\/XxM9m5\" target=\"_blank\" rel=\"nofollow noopener\"><img decoding=\"async\" width=\"300\" height=\"300\" src=\"https:\/\/aprenderbigdata.com\/wp-content\/uploads\/google-cloud-architect-curso.jpeg\" alt=\"Curso Arquitecto Google Cloud\" class=\"wp-image-9049\" srcset=\"https:\/\/aprenderbigdata.com\/wp-content\/uploads\/google-cloud-architect-curso.jpeg 300w, https:\/\/aprenderbigdata.com\/wp-content\/uploads\/google-cloud-architect-curso-150x150.jpeg 150w, https:\/\/aprenderbigdata.com\/wp-content\/uploads\/google-cloud-architect-curso-120x120.jpeg 120w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/figure>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:66.66%\">\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Preparacion-para-la-certificacion-de-Google-Cloud-Cloud-Engineer-Professional-Certificate-en-Espanol\"><\/span><a href=\"https:\/\/imp.i384100.net\/XxM9m5\" target=\"_blank\" rel=\"noopener nofollow\">Preparaci\u00f3n para la certificaci\u00f3n de Google Cloud: Cloud Engineer Professional Certificate en Espa\u00f1ol<\/a><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>En 2019, Global Knowledge clasific\u00f3 Google Cloud Professional Cloud Architect como una de las certificaciones mejor pagadas de TI.<\/p>\n\n\n\n<p>Este programa te proporciona habilidades para impulsar tu carrera de arquitectura en la nube y te ofrece una ruta para obtener la certificaci\u00f3n Google Cloud Professional Cloud Architect, reconocida en la industria. Tambi\u00e9n obtendr\u00e1s experiencia real a trav\u00e9s de proyectos de Qwiklabs que podr\u00e1s compartir con posibles empleadores.<\/p>\n\n\n\n<p>Para los participantes que quieran certificarse, este programa ofrece preguntas de muestra similares a las del examen y soluciones y cuestionarios de pr\u00e1ctica.<\/p>\n<\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Preguntas-Frecuentes-%E2%80%93-FAQ\"><\/span>Preguntas Frecuentes &#8211; FAQ<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\t\t<section\t\thelp class=\"sc_fs_faq sc_card    \"\n\t\t\t\t>\n\t\t\t\t<h3><span class=\"ez-toc-section\" id=\"%C2%BFCuales-son-los-componentes-principales-de-la-arquitectura-Lambda\"><\/span>\u00bfCu\u00e1les son los componentes principales de la arquitectura Lambda?<span class=\"ez-toc-section-end\"><\/span><\/h3>\t\t\t\t<div>\n\t\t\t\t\t\t<div class=\"sc_fs_faq__content\">\n\t\t\t\t\n\n<p>Los componentes principales de la arquitectura Lambda incluyen la capa de ingesti\u00f3n de datos, la capa de procesamiento en tiempo real (speed layer), la capa de procesamiento batch, y la capa de servicio (serving layer). La capa de ingesti\u00f3n recibe datos de m\u00faltiples fuentes, la speed layer procesa datos en tiempo real para proporcionar vistas actualizadas r\u00e1pidamente, la batch layer realiza an\u00e1lisis m\u00e1s exhaustivos y completos, y la serving layer combina los resultados de ambas capas para ofrecer datos listos para ser consumidos por aplicaciones y usuarios finales.<\/p>\n\n\t\t\t<\/div>\n\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section\t\thelp class=\"sc_fs_faq sc_card    \"\n\t\t\t\t>\n\t\t\t\t<h3><span class=\"ez-toc-section\" id=\"%C2%BFComo-se-maneja-la-consistencia-de-datos-en-la-arquitectura-Lambda\"><\/span>\u00bfC\u00f3mo se maneja la consistencia de datos en la arquitectura Lambda?<span class=\"ez-toc-section-end\"><\/span><\/h3>\t\t\t\t<div>\n\t\t\t\t\t\t<div class=\"sc_fs_faq__content\">\n\t\t\t\t\n\n<p>En la arquitectura Lambda, la consistencia de los datos se maneja mediante la combinaci\u00f3n de resultados de las capas de procesamiento en tiempo real y batch. La speed layer proporciona datos r\u00e1pidamente pero puede ser menos precisa debido a la inmediatez del procesamiento, mientras que la batch layer asegura la precisi\u00f3n y consistencia mediante el procesamiento de grandes vol\u00famenes de datos con un cierto retraso. La serving layer se encarga de fusionar estos resultados, proporcionando una vista consistente y actualizada de los datos.<\/p>\n\n\t\t\t<\/div>\n\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section\t\thelp class=\"sc_fs_faq sc_card    \"\n\t\t\t\t>\n\t\t\t\t<h3><span class=\"ez-toc-section\" id=\"%C2%BFComo-se-compara-la-arquitectura-Lambda-con-la-arquitectura-Kappa\"><\/span>\u00bfC\u00f3mo se compara la arquitectura Lambda con la arquitectura Kappa?<span class=\"ez-toc-section-end\"><\/span><\/h3>\t\t\t\t<div>\n\t\t\t\t\t\t<div class=\"sc_fs_faq__content\">\n\t\t\t\t\n\n<p>La arquitectura Kappa es una alternativa a la arquitectura Lambda que simplifica el procesamiento de datos al eliminar la capa de procesamiento batch y enfocarse \u00fanicamente en el procesamiento en tiempo real. Mientras que la arquitectura Lambda utiliza tanto el procesamiento en tiempo real como batch para ofrecer flexibilidad y consistencia, la arquitectura Kappa trata todos los datos como streams y re-procesa los datos hist\u00f3ricos en el mismo pipeline de streaming cuando es necesario.<\/p>\n\n\t\t\t<\/div>\n\t\t<\/div>\n\t\t<\/section>\n\t\t\n<script type=\"application\/ld+json\">\n\t{\n\t\t\"@context\": \"https:\/\/schema.org\",\n\t\t\"@type\": \"FAQPage\",\n\t\t\"mainEntity\": [\n\t\t\t\t\t{\n\t\t\t\t\"@type\": \"Question\",\n\t\t\t\t\"name\": \"\u00bfCu\u00e1les son los componentes principales de la arquitectura Lambda?\",\n\t\t\t\t\"acceptedAnswer\": {\n\t\t\t\t\t\"@type\": \"Answer\",\n\t\t\t\t\t\"text\": \"<p>Los componentes principales de la arquitectura Lambda incluyen la capa de ingesti\u00f3n de datos, la capa de procesamiento en tiempo real (speed layer), la capa de procesamiento batch, y la capa de servicio (serving layer). La capa de ingesti\u00f3n recibe datos de m\u00faltiples fuentes, la speed layer procesa datos en tiempo real para proporcionar vistas actualizadas r\u00e1pidamente, la batch layer realiza an\u00e1lisis m\u00e1s exhaustivos y completos, y la serving layer combina los resultados de ambas capas para ofrecer datos listos para ser consumidos por aplicaciones y usuarios finales.<\/p>\"\n\t\t\t\t\t\t\t\t\t}\n\t\t\t}\n\t\t\t,\t\t\t\t{\n\t\t\t\t\"@type\": \"Question\",\n\t\t\t\t\"name\": \"\u00bfC\u00f3mo se maneja la consistencia de datos en la arquitectura Lambda?\",\n\t\t\t\t\"acceptedAnswer\": {\n\t\t\t\t\t\"@type\": \"Answer\",\n\t\t\t\t\t\"text\": \"<p>En la arquitectura Lambda, la consistencia de los datos se maneja mediante la combinaci\u00f3n de resultados de las capas de procesamiento en tiempo real y batch. La speed layer proporciona datos r\u00e1pidamente pero puede ser menos precisa debido a la inmediatez del procesamiento, mientras que la batch layer asegura la precisi\u00f3n y consistencia mediante el procesamiento de grandes vol\u00famenes de datos con un cierto retraso. La serving layer se encarga de fusionar estos resultados, proporcionando una vista consistente y actualizada de los datos.<\/p>\"\n\t\t\t\t\t\t\t\t\t}\n\t\t\t}\n\t\t\t,\t\t\t\t{\n\t\t\t\t\"@type\": \"Question\",\n\t\t\t\t\"name\": \"\u00bfC\u00f3mo se compara la arquitectura Lambda con la arquitectura Kappa?\",\n\t\t\t\t\"acceptedAnswer\": {\n\t\t\t\t\t\"@type\": \"Answer\",\n\t\t\t\t\t\"text\": \"<p>La arquitectura Kappa es una alternativa a la arquitectura Lambda que simplifica el procesamiento de datos al eliminar la capa de procesamiento batch y enfocarse \u00fanicamente en el procesamiento en tiempo real. Mientras que la arquitectura Lambda utiliza tanto el procesamiento en tiempo real como batch para ofrecer flexibilidad y consistencia, la arquitectura Kappa trata todos los datos como streams y re-procesa los datos hist\u00f3ricos en el mismo pipeline de streaming cuando es necesario.<\/p>\"\n\t\t\t\t\t\t\t\t\t}\n\t\t\t}\n\t\t\t\t\t\t]\n\t}\n<\/script>\n\n\n\n<div class=\"wp-block-group newsletter-re\"><div class=\"wp-block-group__inner-container is-layout-flow wp-block-group-is-layout-flow\">\n<!-- Begin Brevo Form -->\n<!-- START - We recommend to place the below code in head tag of your website html  -->\n<style>\n  @font-face {\n    font-display: block;\n    font-family: Roboto;\n  }\n\n  @font-face {\n    font-display: fallback;\n    font-family: Roboto;\n    font-weight: 600;\n  }\n\n  @font-face {\n    font-display: fallback;\n    font-family: Roboto;\n    font-weight: 700;\n  }\n\n  #sib-container input:-ms-input-placeholder {\n    text-align: left;\n    font-family: \"Helvetica\", sans-serif;\n    color: #c0ccda;\n  }\n\n  #sib-container input::placeholder {\n    text-align: left;\n    font-family: \"Helvetica\", sans-serif;\n    color: #c0ccda;\n  }\n\n  #sib-container textarea::placeholder {\n    text-align: left;\n    font-family: \"Helvetica\", sans-serif;\n    color: #c0ccda;\n  }\n<\/style>\n<link rel=\"stylesheet\" href=\"https:\/\/sibforms.com\/forms\/end-form\/build\/sib-styles.css\">\n<!--  END - We recommend to place the above code in head tag of your website html -->\n\n<!-- START - We recommend to place the below code where you want the form in your website html  -->\n<div class=\"sib-form\" style=\"text-align: center;\n         background-color: #ffffff;                                 \">\n  <div id=\"sib-form-container\" class=\"sib-form-container\">\n    <div id=\"error-message\" class=\"sib-form-message-panel\" style=\"font-size:16px; text-align:left; font-family:&quot;Helvetica&quot;, sans-serif; color:#661d1d; background-color:#ffeded; border-radius:3px; border-color:#ff4949;max-width:600px;\">\n      <div class=\"sib-form-message-panel__text sib-form-message-panel__text--center\">\n        <svg viewBox=\"0 0 512 512\" class=\"sib-icon sib-notification__icon\">\n          <path d=\"M256 40c118.621 0 216 96.075 216 216 0 119.291-96.61 216-216 216-119.244 0-216-96.562-216-216 0-119.203 96.602-216 216-216m0-32C119.043 8 8 119.083 8 256c0 136.997 111.043 248 248 248s248-111.003 248-248C504 119.083 392.957 8 256 8zm-11.49 120h22.979c6.823 0 12.274 5.682 11.99 12.5l-7 168c-.268 6.428-5.556 11.5-11.99 11.5h-8.979c-6.433 0-11.722-5.073-11.99-11.5l-7-168c-.283-6.818 5.167-12.5 11.99-12.5zM256 340c-15.464 0-28 12.536-28 28s12.536 28 28 28 28-12.536 28-28-12.536-28-28-28z\" \/>\n        <\/svg>\n        <span class=\"sib-form-message-panel__inner-text\">\n                          Tu suscripci\u00f3n no ha podido guardarse. 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Aprende aqu\u00ed las caracter\u00edsticas b\u00e1sicas de esta arquitectura de procesamiento de datos en big data<\/p>\n","protected":false},"author":1,"featured_media":8092,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"ocean_post_layout":"","ocean_both_sidebars_style":"","ocean_both_sidebars_content_width":0,"ocean_both_sidebars_sidebars_width":0,"ocean_sidebar":"ocs-posts-sidebar","ocean_second_sidebar":"0","ocean_disable_margins":"enable","ocean_add_body_class":"","ocean_shortcode_before_top_bar":"","ocean_shortcode_after_top_bar":"","ocean_shortcode_before_header":"","ocean_shortcode_after_header":"","ocean_has_shortcode":"","ocean_shortcode_after_title":"","ocean_shortcode_before_footer_widgets":"","ocean_shortcode_after_footer_widgets":"","ocean_shortcode_before_footer_bottom":"","ocean_shortcode_after_footer_bottom":"","ocean_display_top_bar":"default","ocean_display_header":"default","ocean_header_style":"","ocean_center_header_left_menu":"0","ocean_custom_header_template":"0","ocean_custom_logo":0,"ocean_custom_retina_logo":0,"ocean_custom_logo_max_width":0,"ocean_custom_logo_tablet_max_width":0,"ocean_custom_logo_mobile_max_width":0,"ocean_custom_logo_max_height":0,"ocean_custom_logo_tablet_max_height":0,"ocean_custom_logo_mobile_max_height":0,"ocean_header_custom_menu":"0","ocean_menu_typo_font_family":"0","ocean_menu_typo_font_subset":"","ocean_menu_typo_font_size":0,"ocean_menu_typo_font_size_tablet":0,"ocean_menu_typo_font_size_mobile":0,"ocean_menu_typo_font_size_unit":"px","ocean_menu_typo_font_weight":"","ocean_menu_typo_font_weight_tablet":"","ocean_menu_typo_font_weight_mobile":"","ocean_menu_typo_transform":"","ocean_menu_typo_transform_tablet":"","ocean_menu_typo_transform_mobile":"","ocean_menu_typo_line_height":0,"ocean_menu_typo_line_height_tablet":0,"ocean_menu_typo_line_height_mobile":0,"ocean_menu_typo_line_height_unit":"","ocean_menu_typo_spacing":0,"ocean_menu_typo_spacing_tablet":0,"ocean_menu_typo_spacing_mobile":0,"ocean_menu_typo_spacing_unit":"","ocean_menu_link_color":"","ocean_menu_link_color_hover":"","ocean_menu_link_color_active":"","ocean_menu_link_background":"","ocean_menu_link_hover_background":"","ocean_menu_link_active_background":"","ocean_menu_social_links_bg":"","ocean_menu_social_hover_links_bg":"","ocean_menu_social_links_color":"","ocean_menu_social_hover_links_color":"","ocean_disable_title":"default","ocean_disable_heading":"default","ocean_post_title":"","ocean_post_subheading":"","ocean_post_title_style":"","ocean_post_title_background_color":"","ocean_post_title_background":0,"ocean_post_title_bg_image_position":"","ocean_post_title_bg_image_attachment":"","ocean_post_title_bg_image_repeat":"","ocean_post_title_bg_image_size":"","ocean_post_title_height":0,"ocean_post_title_bg_overlay":0.5,"ocean_post_title_bg_overlay_color":"","ocean_disable_breadcrumbs":"default","ocean_breadcrumbs_color":"","ocean_breadcrumbs_separator_color":"","ocean_breadcrumbs_links_color":"","ocean_breadcrumbs_links_hover_color":"","ocean_display_footer_widgets":"default","ocean_display_footer_bottom":"default","ocean_custom_footer_template":"0","ocean_post_oembed":"","ocean_post_self_hosted_media":"","ocean_post_video_embed":"","ocean_link_format":"","ocean_link_format_target":"self","ocean_quote_format":"","ocean_quote_format_link":"post","ocean_gallery_link_images":"off","ocean_gallery_id":[],"footnotes":""},"categories":[5],"tags":[169,12],"class_list":["post-8090","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-big-data","tag-coursera","tag-faq","entry","has-media"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Arquitectura Lambda en el Procesamiento de Datos<\/title>\n<meta name=\"description\" content=\"\u25b7 \u00bfQu\u00e9 es la arquitectura lambda? 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