{"id":3697,"date":"2020-11-28T14:09:08","date_gmt":"2020-11-28T13:09:08","guid":{"rendered":"https:\/\/aprenderbigdata.com\/?p=3697"},"modified":"2025-10-08T22:35:53","modified_gmt":"2025-10-08T21:35:53","slug":"apache-druid","status":"publish","type":"post","link":"https:\/\/aprenderbigdata.com\/apache-druid\/","title":{"rendered":"Una Introducci\u00f3n a Apache Druid"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">En esta entrada vamos a introducir qu\u00e9 es <strong>Apache Druid<\/strong>, una tecnolog\u00eda para anal\u00edtica Big Data con capacidad de realizar consultas en tiempo real sobre datos hist\u00f3ricos. Adem\u00e1s, aprenderemos acerca de su arquitectura, su funcionamiento y sus ventajas respecto a otras bases de datos. \u00a1No te lo pierdas!<\/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\/apache-druid-introduccion-1024x576.jpg\" alt=\"Introducci\u00f3n a Apache Druid\" class=\"wp-image-3719\" srcset=\"https:\/\/aprenderbigdata.com\/wp-content\/uploads\/apache-druid-introduccion-1024x576.jpg 1024w, https:\/\/aprenderbigdata.com\/wp-content\/uploads\/apache-druid-introduccion-300x169.jpg 300w, https:\/\/aprenderbigdata.com\/wp-content\/uploads\/apache-druid-introduccion-768x432.jpg 768w, https:\/\/aprenderbigdata.com\/wp-content\/uploads\/apache-druid-introduccion.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_85 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-6a662ff04735d\" 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-6a662ff04735d\" 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\/apache-druid\/#%C2%BFQue-es-Apache-Druid\" >\u00bfQu\u00e9 es Apache Druid?<\/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\/apache-druid\/#Caracteristicas-de-Apache-Druid\" >Caracter\u00edsticas de Apache Druid<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/aprenderbigdata.com\/apache-druid\/#Funcionamiento-de-Apache-Druid\" >Funcionamiento de Apache Druid<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/aprenderbigdata.com\/apache-druid\/#Arquitectura-de-Apache-Druid\" >Arquitectura de Apache Druid<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/aprenderbigdata.com\/apache-druid\/#Realtime\" >Realtime<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/aprenderbigdata.com\/apache-druid\/#Historical\" >Historical<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/aprenderbigdata.com\/apache-druid\/#Brokers\" >Brokers<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/aprenderbigdata.com\/apache-druid\/#Coordinador\" >Coordinador<\/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\/apache-druid\/#Ventajas-de-Apache-Druid\" >Ventajas de Apache Druid<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/aprenderbigdata.com\/apache-druid\/#Casos-de-Uso-para-Apache-Druid\" >Casos de Uso para Apache Druid<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/aprenderbigdata.com\/apache-druid\/#Curso-practico-de-Apache-Druid\" >Curso pr\u00e1ctico de Apache Druid<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/aprenderbigdata.com\/apache-druid\/#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-13\" href=\"https:\/\/aprenderbigdata.com\/apache-druid\/#%C2%BFQue-ventajas-ofrece-Apache-Druid-sobre-otras-bases-de-datos-analiticas\" >\u00bfQu\u00e9 ventajas ofrece Apache Druid sobre otras bases de datos anal\u00edticas?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/aprenderbigdata.com\/apache-druid\/#%C2%BFQue-es-un-%22segmento%22-en-Apache-Druid-y-cual-es-su-importancia\" >\u00bfQu\u00e9 es un &quot;segmento&quot; en Apache Druid y cu\u00e1l es su importancia?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/aprenderbigdata.com\/apache-druid\/#%C2%BFQue-es-un-%22rollup%22-en-Apache-Druid-y-como-se-utiliza\" >\u00bfQu\u00e9 es un &quot;rollup&quot; en Apache Druid y c\u00f3mo se utiliza?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/aprenderbigdata.com\/apache-druid\/#%C2%BFComo-se-realiza-el-escalado-horizontal-en-Apache-Druid\" >\u00bfC\u00f3mo se realiza el escalado horizontal en Apache Druid?<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%C2%BFQue-es-Apache-Druid\"><\/span>\u00bfQu\u00e9 es Apache Druid?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Apache Druid es una tecnolog\u00eda enfocada en an\u00e1lisis de datos en tiempo real y muy popular para trabajos <strong>OLAP (Online Analytical Processing)<\/strong>. Aporta alta disponibilidad al sistema de consultas y nos permite escalarlo con latencias muy bajas.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Se distribuye con licencia <a href=\"https:\/\/aprenderbigdata.com\/contribuir-open-source\/\"><strong>open source<\/strong><\/a> y es mantenida por la Apache Software Foundation.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Grandes empresas como Netflix o Airbnb usan Apache Druid para hacer consultas sobre flujos de datos y as\u00ed tomar decisiones en tiempo real.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Druid es una herramienta para almacenar datos. Combina caracter\u00edsticas de <a href=\"https:\/\/aprenderbigdata.com\/requisitos-data-warehouse\/\"><strong>Data Warehouses<\/strong><\/a>, de sistemas de b\u00fasqueda y de an\u00e1lisis de series temporales. De esta forma, puede tomar varias formas para resolver distintos problemas, y de ah\u00ed su nombre.<\/p>\n\n\n<div class=\"wp-block-image is-resized\">\n<figure class=\"aligncenter size-medium\"><img decoding=\"async\" width=\"300\" height=\"269\" src=\"https:\/\/aprenderbigdata.com\/wp-content\/uploads\/catacteristicas-apache-druid-300x269.jpg\" alt=\"Caracter\u00edsticas de Apache Druid\" class=\"wp-image-3710\" style=\"width:300px\" srcset=\"https:\/\/aprenderbigdata.com\/wp-content\/uploads\/catacteristicas-apache-druid-300x269.jpg 300w, https:\/\/aprenderbigdata.com\/wp-content\/uploads\/catacteristicas-apache-druid.jpg 595w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><figcaption class=\"wp-element-caption\">Caracter\u00edsticas de Apache Druid<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">En prestaciones, Apache Druid consigue ser m\u00e1s r\u00e1pido que tecnolog\u00edas como <a href=\"https:\/\/aprenderbigdata.com\/apache-hive\/\"><strong>Apache Hive<\/strong><\/a> o <strong><a href=\"https:\/\/aprenderbigdata.com\/prestodb\/\">Presto<\/a><\/strong> en cargas anal\u00edticas.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Caracteristicas-de-Apache-Druid\"><\/span>Caracter\u00edsticas de Apache Druid<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Entre las caracter\u00edsticas m\u00e1s importantes de Apache Druid podemos destacar los siguientes aspectos:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Almacenamiento columnar<\/strong>: Este tipo de bases de datos tratan cada columna de forma independiente. Por lo tanto, las consultas se optimizan leyendo solamente las columnas que sean necesarias.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>B\u00fasquedas indexada<\/strong>: Permite realizar b\u00fasquedas r\u00e1pidas y filtros sobre los datos. Usa \u00edndices invertidos, formando un mapa del contenido y permitiendo b\u00fasquedas de textos sobre documentos.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Ingestas de datos streaming y batch<\/strong>: Proporciona conectores ya listos para conectar a sistemas externos como Apache Kafka, Kinesis, S3 o HDFS. T\u00edpicamente, esto significa que <strong><a href=\"https:\/\/aprenderbigdata.com\/introduccion-apache-kafka\/\">Apache Kafka<\/a><\/strong> ser\u00e1 la fuente de datos para cargas en streaming y <strong><a href=\"https:\/\/aprenderbigdata.com\/hdfs\/\">HDFS<\/a><\/strong> o <strong><a href=\"https:\/\/aprenderbigdata.com\/amazon-s3\/\">S3<\/a><\/strong> las fuentes de datos para las cargas de tipo batch. Por tanto, la arquitectura en Druid es de tipo <strong><a href=\"https:\/\/aprenderbigdata.com\/arquitectura-lambda\/\">Lambda<\/a><\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Esquemas flexibles<\/strong>: Los esquemas de flexibles soportan su evoluci\u00f3n y cambios en el tiempo en funci\u00f3n de las necesidades y tipos de datos.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Particionado de datos temporales<\/strong>: Al realizar un particionado de los datos en funci\u00f3n de sus fechas, se aceleran las consultas sobre rangos de tiempo considerablemente. Adem\u00e1s, puede realizar una preagregaci\u00f3n de los datos (rollup) para reducir o colapsar el tama\u00f1o de las columnas.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Soporte SQL<\/strong>: Adem\u00e1s del lenguaje nativo (<strong><a href=\"https:\/\/aprenderbigdata.com\/json\/\">JSON<\/a><\/strong>), Druid soporte el lenguaje SQL a trav\u00e9s de APIs JDBC y HTTP.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Escalabilidad horizontal<\/strong>: El rendimiento de Druid se puede incrementar a\u00f1adiendo nodos al sistema. Tambi\u00e9n es posible desescalar eliminando nodos. El sistema rebalancea la carga autom\u00e1ticamente y proporciona una arquitectura resistente a fallos.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Funcionamiento-de-Apache-Druid\"><\/span>Funcionamiento de Apache Druid<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Druid convierte los datos almacenados en las tecnolog\u00edas fuentes, como HDFS, a un formato optimizado para la lectura mediante un proceso de indexaci\u00f3n. El resultado de este proceso de conversi\u00f3n se denomina <strong>segmentos<\/strong> de Druid. Tambi\u00e9n se aplican mecanismos de compresi\u00f3n para optimizar el espacio utilizado.<\/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 class=\"wp-block-paragraph\">En ocasiones, Druid tambi\u00e9n puede <strong>pre-agregar los datos<\/strong> a medida que se ingestan. Esta operaci\u00f3n permite reducir el espacio empleado, ya que se reduce el n\u00famero de registros para cada dato.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Adem\u00e1s, Druid realiza <strong>replicaci\u00f3n de datos y backups<\/strong> peri\u00f3dicos a sistemas externos.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Arquitectura-de-Apache-Druid\"><\/span>Arquitectura de Apache Druid<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Apache Druid se sit\u00faa en una arquitectura Big Data como una capa entre los sistemas de almacenamiento de datos y el servicio al usuario. Act\u00faa como una capa de consulta r\u00e1pida de datos.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>La arquitectura de Druid se basa en <a href=\"https:\/\/aprenderbigdata.com\/microservicios\/\">microservicios<\/a><\/strong>, con servicios de ingesta, de consulta y de coordinaci\u00f3n. Estos servicios se pueden distribuir de varias maneras en el hardware disponible. Cada uno de ellos tiene mecanismos de tolerancia a fallos para evitar que el sistema sufra p\u00e9rdidas de servicio.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Como muchas otras tecnolog\u00edas distribuidas, Apache Druid <strong>se apoya en <a href=\"https:\/\/aprenderbigdata.com\/zookeeper\/\">Apache Zookeeper<\/a><\/strong> para realizar la coordinaci\u00f3n de los nodos del cl\u00faster.<\/p>\n\n\n<div class=\"wp-block-image is-resized\">\n<figure class=\"aligncenter size-full\"><img decoding=\"async\" width=\"991\" height=\"506\" src=\"https:\/\/aprenderbigdata.com\/wp-content\/uploads\/arquitectura-apache-druid-diagrama.jpg\" alt=\"Diagrama de Arquitectura de Apache Druid\" class=\"wp-image-3714\" style=\"width:600px\" srcset=\"https:\/\/aprenderbigdata.com\/wp-content\/uploads\/arquitectura-apache-druid-diagrama.jpg 991w, https:\/\/aprenderbigdata.com\/wp-content\/uploads\/arquitectura-apache-druid-diagrama-300x153.jpg 300w, https:\/\/aprenderbigdata.com\/wp-content\/uploads\/arquitectura-apache-druid-diagrama-768x392.jpg 768w\" sizes=\"(max-width: 991px) 100vw, 991px\" \/><figcaption class=\"wp-element-caption\">Diagrama de Arquitectura de Apache Druid<\/figcaption><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">A continuaci\u00f3n vamos a ver qu\u00e9 tipos de nodos existen en Druid y para qu\u00e9 sirve cada uno.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Realtime\"><\/span>Realtime<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Estos nodos son los encargados de gestionar las lecturas y las escrituras en tiempo real en streaming. Para aumentar el rendimiento, tienen un buffer en memoria que persisten peri\u00f3dicamente al disco. Estos datos, se almacenan con un <strong>formato columnar (segmentos)<\/strong> y sobre ellos se calculan los \u00edndices. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Para mantener la persistencia de los datos, los segmentos se terminan almacenando en un sistema de ficheros distribuido, t\u00edpicamente HDFS (<a href=\"https:\/\/druid.apache.org\/docs\/latest\/dependencies\/deep-storage.html\" target=\"_blank\" rel=\"noreferrer noopener\">deep storage<\/a>). Sus metadatos se almacenan en una base de datos relacional como <strong><a href=\"https:\/\/aprenderbigdata.com\/mysql\/\">MySQL<\/a><\/strong> para hacerlos disponibles a otros nodos.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Historical\"><\/span>Historical<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Este tipo de nodos son los m\u00e1s comunes en Apache Druid. Se encargan de <strong>servir las consultas anal\u00edticas<\/strong> que se realizan al cl\u00faster. Para ello, cargan los segmentos almacenados en HDFS.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Para enterarse de los nuevos segmentos que se publican usan Zookeeper. Algunos de ellos los guardan en cach\u00e9 para acelerar las consultas.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Tambi\u00e9n se les suele llamar workers.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Brokers\"><\/span>Brokers<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Son los nodos encargados de gestionar las consultas que realizan los usuarios, redirigirlas a los nodos apropiados (historical o realtime) y agregar las respuestas.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Coordinador\"><\/span>Coordinador<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Este nodo coordina los nodos hist\u00f3ricos o workers indicando cu\u00e1ndo desalojar, cargar, compactar o replicar datos, as\u00ed como balancear la carga entre los dem\u00e1s nodos. El el responsable de gestionar la disponibilidad de los datos en el cl\u00faster.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Solo existe un coordinador o l\u00edder ejecutando en cada momento. En el caso de que sufra un fallo, otro nodo toma su lugar.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Ventajas-de-Apache-Druid\"><\/span>Ventajas de Apache Druid<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">La principal ventaja de Apache Druid es que aporta la capacidad de realizar consultas de datos muy r\u00e1pidas en un sistema de datos escalable de forma columnar.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Esta velocidad es fundamental en sistemas anal\u00edticos y para realizar consultas exploratorias sobre nuestros datos. La velocidad de agregaci\u00f3n de los datos es muy superior a sistemas de bases de datos tradicionales RDBMS como MySQL o <strong><a href=\"https:\/\/aprenderbigdata.com\/postgresql\/\">PostgreSQL<\/a><\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Si lo comparamos con otras <a href=\"https:\/\/aprenderbigdata.com\/bases-de-datos-nosql\/\"><strong>herramientas NoSQL<\/strong><\/a> escalables, debemos tener en cuenta las preagregaciones de datos que se realizan en Druid. Las optimizaciones que incluye para explorar datos hist\u00f3ricos y en tiempo real basados en rangos temporales no son f\u00e1ciles de implementar sobre otras tecnolog\u00edas de bases de datos. En este caso, ser\u00eda necesario preprocesar los datos y realizar agregados con determinadas granularidades, por ejemplo cada hora, cada minuto, etc. Es inviable almacenar estas agregaciones para todas las columnas, rangos de tiempo y combinaciones. Aqu\u00ed es donde realmente destaca Druid.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"formacion\"><span class=\"ez-toc-section\" id=\"Casos-de-Uso-para-Apache-Druid\"><\/span>Casos de Uso para Apache Druid<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Como hemos visto, Apache Druid es una tecnolog\u00eda muy flexible, capaz de adaptarse a multitud de casos de uso. Generalmente, estos casos de uso se caracterizan por tener una alta necesidad de <strong>inserciones masivas y r\u00e1pidas<\/strong>, mientras que las actualizaciones de registros son menos frecuentes.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Debemos observar si la mayor parte de nuestras <strong>consultas ser\u00e1n de reporting y necesitar\u00e1n operaciones de agregaci\u00f3n (group by) o de b\u00fasqueda<\/strong>. Si adem\u00e1s tienen un componente temporal, entonces deberemos evaluar Druid como una soluci\u00f3n potente.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Casos de uso m\u00e1s comunes de Apache Druid:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Anal\u00edtica web y m\u00f3vil<\/li>\n\n\n\n<li>Anal\u00edtica de marketing digital<\/li>\n\n\n\n<li><a href=\"https:\/\/aprenderbigdata.com\/herramientas-bi\/\">Business Intelligence<\/a> (OLAP)<\/li>\n\n\n\n<li>M\u00e9tricas de rendimiento de redes y de aplicaciones<\/li>\n<\/ul>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-vertically-aligned-top is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image size-large is-resized\"><a href=\"https:\/\/trk.udemy.com\/OeqK3Z\" target=\"_blank\" rel=\"nofollow noopener\"><img decoding=\"async\" src=\"https:\/\/aprenderbigdata.com\/wp-content\/uploads\/3915338_8e0a-apache-druid.jpg\" alt=\"Curso pr\u00e1ctico\"\/><\/a><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<h3 class=\"wp-block-heading has-text-align-left\"><span class=\"ez-toc-section\" id=\"Curso-practico-de-Apache-Druid\"><\/span><a href=\"https:\/\/trk.udemy.com\/OeqK3Z\" target=\"_blank\" rel=\"noopener nofollow\">Curso pr\u00e1ctico de Apache Druid<\/a><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"has-text-align-left wp-block-paragraph\">No lo dudes y aprende Apache Druid a fondo con este curso de Udemy en ingl\u00e9s. Te ense\u00f1ar\u00e1 el detalle de todos sus componentes y los conceptos fundamentales para sacarle todo el partido posible.<\/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%BFQue-ventajas-ofrece-Apache-Druid-sobre-otras-bases-de-datos-analiticas\"><\/span>\u00bfQu\u00e9 ventajas ofrece Apache Druid sobre otras bases de datos anal\u00edticas?<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 class=\"wp-block-paragraph\"><strong>Latencias bajas:<\/strong> Optimizado para consultas de baja latencia y alta concurrencia. <strong>Escalabilidad:<\/strong> Arquitectura distribuida que permite escalar horizontalmente. <strong>Ingesta en tiempo real:<\/strong> Capacidad para ingerir y consultar datos en tiempo real sin retrasos. <strong>Compresi\u00f3n eficiente:<\/strong> Almacenamiento eficiente de datos mediante t\u00e9cnicas de compresi\u00f3n. <strong>Soporte para agregaciones complejas:<\/strong> Facilita el an\u00e1lisis avanzado de datos mediante agregaciones y filtrado.<\/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%BFQue-es-un-%22segmento%22-en-Apache-Druid-y-cual-es-su-importancia\"><\/span>\u00bfQu\u00e9 es un &quot;segmento&quot; en Apache Druid y cu\u00e1l es su importancia?<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 class=\"wp-block-paragraph\">Un \u00absegmento\u00bb en Apache Druid es una unidad de almacenamiento que contiene un conjunto de datos indexados para un intervalo de tiempo espec\u00edfico. Los segmentos son fundamentales porque permiten la partici\u00f3n y distribuci\u00f3n de datos en el cl\u00faster, facilitando la escalabilidad, el rendimiento de consultas y la recuperaci\u00f3n ante fallos.<\/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%BFQue-es-un-%22rollup%22-en-Apache-Druid-y-como-se-utiliza\"><\/span>\u00bfQu\u00e9 es un &quot;rollup&quot; en Apache Druid y c\u00f3mo se utiliza?<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 class=\"wp-block-paragraph\">El \u00abrollup\u00bb en Apache Druid es una t\u00e9cnica de preagregaci\u00f3n de datos durante el proceso de ingesta. Agrupa datos basados en claves espec\u00edficas y calcula agregaciones como sumas, promedios, etc., antes de almacenarlos en segmentos. Esto reduce el volumen de datos almacenados y mejora el rendimiento de consultas, aunque puede sacrificar el acceso a los datos detallados.<\/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-realiza-el-escalado-horizontal-en-Apache-Druid\"><\/span>\u00bfC\u00f3mo se realiza el escalado horizontal en Apache Druid?<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 class=\"wp-block-paragraph\">El escalado horizontal en Apache Druid se realiza a\u00f1adiendo m\u00e1s nodos a los diferentes tipos de servicios (nodos de datos, nodos de consulta, nodos hist\u00f3ricos, etc.). Druid est\u00e1 dise\u00f1ado para ser distribuido y puede autom\u00e1ticamente balancear la carga de trabajo entre los nodos disponibles. Esto permite manejar mayores vol\u00famenes de datos y m\u00e1s consultas concurrentes.<\/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\": \"\u00bfQu\u00e9 ventajas ofrece Apache Druid sobre otras bases de datos anal\u00edticas?\",\n\t\t\t\t\"acceptedAnswer\": {\n\t\t\t\t\t\"@type\": \"Answer\",\n\t\t\t\t\t\"text\": \"<p><strong>Latencias bajas:<\/strong> Optimizado para consultas de baja latencia y alta concurrencia. <strong>Escalabilidad:<\/strong> Arquitectura distribuida que permite escalar horizontalmente. <strong>Ingesta en tiempo real:<\/strong> Capacidad para ingerir y consultar datos en tiempo real sin retrasos. <strong>Compresi\u00f3n eficiente:<\/strong> Almacenamiento eficiente de datos mediante t\u00e9cnicas de compresi\u00f3n. <strong>Soporte para agregaciones complejas:<\/strong> Facilita el an\u00e1lisis avanzado de datos mediante agregaciones y filtrado.<\/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\": \"\u00bfQu\u00e9 es un \\\"segmento\\\" en Apache Druid y cu\u00e1l es su importancia?\",\n\t\t\t\t\"acceptedAnswer\": {\n\t\t\t\t\t\"@type\": \"Answer\",\n\t\t\t\t\t\"text\": \"<p>Un \\\"segmento\\\" en Apache Druid es una unidad de almacenamiento que contiene un conjunto de datos indexados para un intervalo de tiempo espec\u00edfico. Los segmentos son fundamentales porque permiten la partici\u00f3n y distribuci\u00f3n de datos en el cl\u00faster, facilitando la escalabilidad, el rendimiento de consultas y la recuperaci\u00f3n ante fallos.<\/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\": \"\u00bfQu\u00e9 es un \\\"rollup\\\" en Apache Druid y c\u00f3mo se utiliza?\",\n\t\t\t\t\"acceptedAnswer\": {\n\t\t\t\t\t\"@type\": \"Answer\",\n\t\t\t\t\t\"text\": \"<p>El \\\"rollup\\\" en Apache Druid es una t\u00e9cnica de preagregaci\u00f3n de datos durante el proceso de ingesta. Agrupa datos basados en claves espec\u00edficas y calcula agregaciones como sumas, promedios, etc., antes de almacenarlos en segmentos. Esto reduce el volumen de datos almacenados y mejora el rendimiento de consultas, aunque puede sacrificar el acceso a los datos detallados.<\/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 realiza el escalado horizontal en Apache Druid?\",\n\t\t\t\t\"acceptedAnswer\": {\n\t\t\t\t\t\"@type\": \"Answer\",\n\t\t\t\t\t\"text\": \"<p>El escalado horizontal en Apache Druid se realiza a\u00f1adiendo m\u00e1s nodos a los diferentes tipos de servicios (nodos de datos, nodos de consulta, nodos hist\u00f3ricos, etc.). Druid est\u00e1 dise\u00f1ado para ser distribuido y puede autom\u00e1ticamente balancear la carga de trabajo entre los nodos disponibles. Esto permite manejar mayores vol\u00famenes de datos y m\u00e1s consultas concurrentes.<\/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<hr class=\"wp-block-separator has-css-opacity\"\/>\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\" id=\"block-8ccefbf5-5cd9-405e-ae68-1237ad4e27ce\">A continuaci\u00f3n, un breve <a href=\"https:\/\/youtu.be\/xF0xadaUvC8\" target=\"_blank\" rel=\"noreferrer noopener\">v\u00eddeo-resumen<\/a>. \u00a1No te lo pierdas!<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<p class=\"responsive-video-wrap clr\"><iframe title=\"Apache DRUID - Aprender Big Data #36\" width=\"1200\" height=\"675\" src=\"https:\/\/www.youtube.com\/embed\/xF0xadaUvC8?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/p>\n<\/div><\/figure>\n\n\n\n<hr class=\"wp-block-separator has-css-opacity\"\/>\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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