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28.4011 24H4.41235C2.31362 24 0.621094 22.2962 0.621094 20.2088V3.79124C0.621094 1.69251 2.3249 0 4.41235 0ZM28.4011 21.4612C29.1007 21.4612 29.6648 20.897 29.6648 20.1975V3.77995C29.6648 3.08037 29.1007 2.5162 28.4011 2.5162H4.41235C3.71277 2.5162 3.14859 3.08037 3.14859 3.77995V20.1975C3.14859 20.897 3.71277 21.4612 4.41235 21.4612H28.4011Z\" fill=\"#249EDC\"/\u003E\r\n\u003Cpath fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M29.6648 20.1975C29.6648 20.897 29.1007 21.4612 28.4011 21.4612H4.41235C3.71277 21.4612 3.14859 20.897 3.14859 20.1975V3.77995C3.14859 3.08037 3.71277 2.5162 4.41235 2.5162H28.4011C29.1007 2.5162 29.6648 3.08037 29.6648 3.77995V20.1975ZM27.7466 6.49927C28.04 6.17205 28.0174 5.66432 27.6902 5.35966V5.34837C27.363 5.055 26.8552 5.07755 26.5506 5.40477L18.7311 13.9577C18.686 14.0141 18.6296 14.0592 18.5731 14.1156C17.2981 15.3117 15.3009 15.2327 14.1162 13.9577L6.2854 5.39351C5.98075 5.05501 5.47299 5.03242 5.14577 5.33708C4.80727 5.64173 4.7847 6.1495 5.08935 6.47672L10.8327 12.7503L5.6084 17.4217C5.2699 17.7151 5.24733 18.2228 5.5407 18.5613C5.83407 18.8998 6.35311 18.9224 6.68033 18.629L11.9159 13.9464L12.9201 15.0409C13.8454 16.0564 15.0865 16.6093 16.418 16.6093C17.7495 16.6093 18.9906 16.0564 19.9159 15.0409L20.8637 14.0028L26.0428 18.6516H26.0654C26.4039 18.945 26.9117 18.9111 27.205 18.5726C27.4984 18.2341 27.4645 17.7264 27.126 17.433L21.9695 12.818L27.7466 6.49927Z\" fill=\"#249EDC\"/\u003E\r\n\u003C/svg\u003E\u003C/span\u003E\r\n\u003C/div\u003E","cssContent":".share-icon__svg *{pointer-events:none}.share-icon-group{display:flex;gap:24px;align-items:center}.share-icon{display:inline-block}.share-icon svg{height:32px}.share-icon__svg:hover path{fill:var(--ui-02);transition:300ms ease fill}.share-icon__svg path{transition:300ms ease fill}","jsContent":"const pageURL = window.location.href;\r\n\r\ndocument.addEventListener('click', (e) =\u003E {\r\n\r\nconst targetClassesArr = [...e.target.classList];\r\nconsole.log(targetClassesArr);\r\nif (targetClassesArr.includes('share-icon--linkedin')) {\r\nconst shareURL = `https://www.linkedin.com/sharing/share-offsite/?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--facebook')) {\r\nconst shareURL = `http://www.facebook.com/sharer/sharer.php?u=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--twitter')) {\r\nconst shareURL = `https://twitter.com/intent/tweet?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--email')) {\r\nconst shareURL = `mailto:?subject=Check out this news from Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["markup_editor"]}},":itemsOrder":["container_949147658"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/es/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"classNames":"aem-xf","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["text","experiencefragment"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-large",":items":{"container_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-669a39e05c","additionalClasses":"headline-decoration","type":"heading2","lines":["Descripción general"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-7be5d6a6b1","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ECualquiera que haya trabajado con un data lake conoce la frustración: un día, el panel muestra 200 000 transacciones; al día siguiente, 195 000, y nadie sabe por qué. Los data lakes tradicionales son flexibles, pero también son propensos a errores como registros perdidos, archivos duplicados o consultas lentas. \u003C/p\u003E\n\u003Cp\u003EDelta Lake aborda estos problemas. Se trata de un marco de almacenamiento de código abierto que se ubica sobre tu data lake actual y refuerza la fiabilidad y el rendimiento. En lugar de preguntarte si las cifras cuadran, puedes contar con Delta Lake para mantener los datos coherentes, precisos y listos para el análisis.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_copy_copy_":{"columnClassNames":{"container_115652453":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__480869401":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1444615495":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__222865509":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1413360324":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__455068363":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1500909857":"aem-GridColumn aem-GridColumn--default--12","container_1858830768":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1978652079":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"container_copy_copy_":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"a",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-2cbd902c7e","additionalClasses":"headline-decoration","type":"heading2","lines":["¿Qué es Delta Lake?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-2bc42c88a8","text":"\u003Cp\u003EDelta Lake es un formato de tabla de código abierto que aporta estructura y \u003Ca href=\"https://www.snowflake.com/es/fundamentals/data-governance/\"\u003Egobernanza\u003C/a\u003E a los \u003Ca href=\"https://www.snowflake.com/es/fundamentals/data-lakes-and-data-lake-architecture/\"\u003Edata lakes\u003C/a\u003E. En lugar de ser solo un repositorio de archivos, Delta Lake organiza esos datos en tablas que puedes consultar y actualizar de forma fiable. Añade funciones de las que carecen los data lakes con datos sin procesar, como transacciones ACID (atomicidad, coherencia, aislamiento y durabilidad) para lecturas y escrituras coherentes, aplicación de esquemas para evitar tipos de datos no coincidentes y consultas de “time travel” que te permiten volver a versiones anteriores de los datos. Como se ejecuta en tu data lake actual, no necesitas un sistema nuevo. Simplemente proporciona una forma más inteligente de gestionar el que ya tienes.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy__1444615495":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"use",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-aa8815c16b","additionalClasses":"headline-decoration","type":"heading2","lines":["Características de Delta Lake"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-4f29fcdada","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EDelta Lake se distingue de los data lakes estándar creados en archivos \u003Ca href=\"https://www.snowflake.com/en/fundamentals/avro-vs-parquet-choosing-a-data-format-for-modern-workflows/\"\u003EParquet\u003C/a\u003E u ORC porque añade un conjunto de funciones a nivel de tabla que hacen que los datos sean más fiables y fáciles de usar. Estas son algunas de las capacidades principales que lo distinguen:\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ETransacciones ACID\u003C/h3\u003E\r\n\u003Cp\u003ELa mayoría de los data lakes no pueden garantizar la coherencia cuando varios usuarios o procesos leen y escriben datos al mismo tiempo. Delta Lake admite transacciones ACID, lo que significa que cada cambio se registra y se aplica de forma fiable. Por ejemplo, si una \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/financial-services/\"\u003Einstitución financiera\u003C/a\u003E actualiza millones de registros de pagos durante la noche, Delta Lake se asegura de que se aplican todas las actualizaciones o de que no se aplica ninguna. No hay ningún estado intermedio en el que algunos registros se actualizan y otros no.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ETime Travel\u003C/h3\u003E\r\n\u003Cp\u003ELos errores ocurren. Con Delta Lake puedes volver a versiones anteriores de tus datos. La función Time Travel te permite consultar “a fecha de” un momento específico, ya sea para auditar cambios, recuperarse de un error o reproducir informes anteriores. Un minorista, por ejemplo, podría comparar las cifras de este trimestre con una instantánea de la temporada navideña anterior, o un analista podría depurar un modelo comprobando cómo eran los datos de entrada antes de modificarlos.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EGestión de metadatos escalable\u003C/h3\u003E\r\n\u003Cp\u003EEn los \u003Ca href=\"https://www.snowflake.com/es/fundamentals/data-lakes-and-data-lake-architecture/\"\u003Edata lakes\u003C/a\u003E a gran escala, los metadatos (es decir, la información sobre tus archivos) pueden convertirse en un cuello de botella. Delta Lake se encarga de ello mediante el almacenamiento de los metadatos en un registro de transacciones, en lugar de depender de una colección masiva de listados de archivos. De este modo, las consultas pueden procesar tablas a escala de petabytes con miles de millones de archivos o particiones sin interrupción. Piensa en un proveedor de telecomunicaciones que analiza registros de llamadas de cientos de millones de clientes. Sin el registro de transacciones de Delta Lake, el mero hecho de listar los archivos podría saturar el sistema antes de que se ejecute una sola consulta.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EAplicación y evolución de esquemas\u003C/h3\u003E\r\n\u003Cp\u003ELos data lakes tradicionales aceptan cualquier archivo, incluso si el esquema no coincide con el resto del conjunto de datos. Eso es un problema porque permite que se incorporen datos defectuosos sin previo aviso. Si un equipo sube un archivo en el que una columna “fecha” está llena de cadenas de texto, u otro añade una nueva columna que no existe en el resto del conjunto de datos, toda la tabla puede dejar de ser fiable. Los analistas pueden encontrar errores al ejecutar consultas o, lo que es peor, obtener resultados que parecen válidos pero que en realidad son incorrectos.\u003C/p\u003E\r\n\u003Cp\u003EDelta Lake lo soluciona aplicando esquemas, de modo que los archivos no coincidentes se bloquean antes de que contaminen el conjunto de datos. Al mismo tiempo, admite la evolución de los esquemas, lo que permite añadir nuevas columnas o ajustar los tipos de datos a medida que cambien las necesidades de la empresa sin interrumpir los flujos de trabajo existentes. Esto significa que el equipo de marketing puede añadir una columna “campaign_id” a los datos de los clientes, mientras que el equipo de finanzas sigue utilizando el esquema antiguo hasta que estén listos para cambiar.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy__1978652079":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"benefits",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-deeb5114e4","additionalClasses":"headline-decoration","type":"heading2","lines":["Ventajas de Delta Lake"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-ee63ce9473","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EAñadir Delta Lake a una pila de datos moderna aporta ventajas tangibles más allá de las funciones técnicas. Estos son algunos de los beneficios que las organizaciones pueden ver en la práctica:\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EMejora de la fiabilidad de los datos\u003C/h3\u003E\r\n\u003Cp\u003EGracias a la integración de transacciones ACID y de la aplicación de esquemas, Delta Lake reduce el riesgo de archivos dañados, actualizaciones incompletas o tipos de datos no coincidentes. Esa estabilidad significa que los equipos pueden confiar en las cifras que ven, tanto si ejecutan un informe financiero como si entrenan un modelo de aprendizaje automático.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EArquitectura de datos simplificada\u003C/h3\u003E\r\n\u003Cp\u003ESin Delta Lake, muchas organizaciones añaden sistemas adicionales sobre el data lake para gestionar transacciones, auditorías o controles de versiones. Delta Lake consolida esas capacidades en una sola capa. El resultado es una arquitectura más sencilla, fácil de gestionar y menos costosa de mantener.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EMejora de la calidad de los datos\u003C/h3\u003E\r\n\u003Cp\u003EDado que Delta Lake valida los datos entrantes y aplica los esquemas, evita que se añadan registros defectuosos en el data lake. En combinación con su capacidad de revertir a versiones anteriores, ayuda a mantener los conjuntos de datos coherentes y limpios, por lo que los equipos de analíticas dedican menos tiempo a limpiar datos y más a utilizarlos.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EMejora de la gobernanza y la auditoría\u003C/h3\u003E\r\n\u003Cp\u003ECada cambio en Delta Lake se registra en un registro de transacciones. Este se convierte en un registro de auditoría integrado, ya que se puede ver exactamente qué usuario realizó qué actualización y cuándo. Para sectores regulados u organizaciones con necesidades de cumplimiento estrictas, esta transparencia integrada ayuda a cumplir los requisitos de gobernanza sin añadir capas adicionales de herramientas.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ERendimiento analítico más rápido\u003C/h3\u003E\r\n\u003Cp\u003ELa capacidad de Delta Lake para optimizar el diseño de los datos y gestionar los metadatos a escala implica que las consultas devuelven resultados más rápido. Los analistas ya no tienen que esperar horas para que una consulta analice terabytes de archivos sin procesar. En su lugar, el sistema identifica las particiones correctas y proporciona información en minutos o incluso segundos.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EDisminución de los costes operativos\u003C/h3\u003E\r\n\u003Cp\u003EComo reduce la necesidad de herramientas adicionales para aplicar esquemas, realizar un seguimiento de las versiones o reparar los datos dañados, Delta Lake puede reducir el coste total de la propiedad. Los equipos dedican menos tiempo a corregir errores e invierten menos dinero en sistemas redundantes, lo que libera recursos para proyectos de mayor valor.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy__455068363":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"how",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-eb132c62f4","additionalClasses":"headline-decoration","type":"heading2","lines":["Consideraciones sobre Delta Lake "],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-d6e6304228","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EAunque Delta Lake ofrece varias ventajas, \u003Ca href=\"https://hevodata.com/learn/apache-iceberg-vs-delta-lake-key-differences/\"\u003Etambién presenta algunos desafíos\u003C/a\u003E, especialmente en comparación con otros formatos de tabla abiertos como \u003Ca href=\"https://www.snowflake.com/es/fundamentals/apache-iceberg-tables/\"\u003EApache Iceberg\u003C/a\u003E. Al igual que Delta Lake, Apache Iceberg aporta estructura y fiabilidad a los data lakes. Ambos buscan resolver las deficiencias del almacenamiento de archivos sin procesar, pero adoptan enfoques diferentes. Para elegir la solución más adecuada, es útil tener en cuenta algunas de las áreas clave en las que difieren ambos formatos de tabla:\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EMetadatos y gobernanza\u003C/h3\u003E\r\n\u003Cp\u003EDelta Lake mantiene la coherencia escribiendo cada cambio en un registro de transacciones, de modo que las actualizaciones se registran y se aplican de forma fiable. Iceberg utiliza instantáneas y archivos de manifiestos para capturar el estado de una tabla, lo que facilita revertir cambios o incluso ramificar los datos. La pregunta clave para los equipos es qué enfoque encaja mejor con la forma en que gestionan la gobernanza y las auditorías.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ECompatibilidad con el ecosistema\u003C/h3\u003E\r\n\u003Cp\u003EDelta Lake se usa ampliamente con Apache Spark y ahora admite la interoperabilidad multimotor a través de Delta UniForm. Iceberg, por el contrario, está diseñado para no depender de ningún motor y es compatible con una amplia gama de motores de consulta y plataformas de nube. La elección depende del grado de integración deseado: una integración profunda en un entorno o la flexibilidad de ejecutarlo en muchos entornos.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EOptimización del rendimiento\u003C/h3\u003E\r\n\u003Cp\u003EDelta Lake utiliza técnicas como ordenamiento en Z y la agrupación en clústeres líquidos (liquid clustering) para agrupar datos relacionados, lo que acelera las consultas con filtros complejos. Iceberg optimiza el rendimiento con particiones ocultas y poda de metadatos para reducir el número de archivos que una consulta tiene que analizar. El desafío para los equipos es averiguar qué enfoque se alinea mejor con sus workloads y volúmenes de datos.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EEvolución de esquemas\u003C/h3\u003E\r\n\u003Cp\u003ETanto Delta Lake como Iceberg permiten cambiar los esquemas con el tiempo. \u003Ca href=\"https://medium.com/@pmpranjalmittal29/understanding-schema-evolution-in-delta-lake-06f0470ce9b5\"\u003EDelta Lake es más estricto al inicio\u003C/a\u003E, ya que solo permite cambios controlados a medida que evolucionen las necesidades. Iceberg es más flexible por diseño: está pensado para gestionar particiones y esquemas cambiantes desde el principio, lo que resulta ventajoso si los datos cambian con frecuencia y rapidez.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EComunidad y madurez\u003C/h3\u003E\r\n\u003Cp\u003EDelta Lake existe desde hace más tiempo y cuenta con una amplia base de usuarios en entornos basados en Spark. Iceberg es más nuevo, pero ha ganado terreno rápidamente, con el respaldo de \u003Ca href=\"https://www.datanami.com/2021/02/08/apache-iceberg-the-hub-of-an-emerging-data-service-ecosystem/\"\u003ENetflix\u003C/a\u003E, Snowflake y otros importantes contribuyentes. La elección entre ambos suele depender de si se prefiere un proyecto más consolidado o uno más reciente que evoluciona más rápido.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy__222865509":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"ai",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-64852341e1","additionalClasses":"headline-decoration","type":"heading2","lines":["Configuración de Delta Lake"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-53e63011ab","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ESi se elige Delta Lake como la opción correcta para la organización, no se necesita reemplazar toda la infraestructura para empezar. Normalmente, consiste en colocar Delta Lake en capas sobre un data lake existente con la ayuda de un motor de procesamiento compatible. Estos son los tres pasos principales:\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E1. Elegir un motor de cómputo\u003C/h3\u003E\n\u003Cp\u003EDelta Lake se utiliza con más frecuencia con Apache Spark, aunque también funciona con motores como Snowflake, Presto, Trino, Hive y otros. La elección depende del entorno de datos actual y de cómo se van a consultar y procesar los datos.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E2. Configurar las bibliotecas\u003C/h3\u003E\n\u003Cp\u003EA continuación, se instalan y configuran las bibliotecas de Delta Lake dentro del motor de cómputo que se haya elegido. En este paso, se proporciona al motor la capacidad de leer y escribir en tablas Delta, aplicar esquemas y realizar un seguimiento de los cambios a través del registro de transacciones.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E3. Leer y escribir datos\u003C/h3\u003E\n\u003Cp\u003EUna vez configuradas, se pueden empezar a crear tablas Delta directamente en el almacenamiento del data lake existente. Los datos se escriben en formato Parquet, pero se gestionan a través del registro de transacciones de Delta, que permite realizar actualizaciones, eliminaciones y consultas de Time Travel. A partir de ahí, se pueden ejecutar comandos SQL conocidos para consultar, modificar o combinar conjuntos de datos.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy__1413360324":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"tools",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2_copy":{"id":"title-v2-1067bd30f1","additionalClasses":"headline-decoration","type":"heading2","lines":["5 prácticas recomendadas de Delta Lake"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-cb553fbbdb","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EPara obtener el máximo valor de una implementación de Delta Lake, los equipos deben tener en cuenta algunas prácticas consolidadas:\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003Ehacer Particiones de datos de forma eficiente\u003C/h3\u003E\r\n\u003Cp\u003EDivide grandes conjuntos de datos en particiones lógicas (por ejemplo, por fecha o región) para que las consultas puedan concentrarse en el subconjunto de archivos correcto en lugar de analizar todo el data lake. Por ejemplo, un sitio de noticias puede segmentar los datos de secuencia de clics por día, lo que facilita la ejecución de informes diarios sin tener que revisar meses de registros no relacionados.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EGestionar los metadatos a escala\u003C/h3\u003E\r\n\u003Cp\u003ESupervisa el registro de transacciones y los metadatos de las tablas a medida que los datos crecen. Asegúrate de compactar archivos pequeños con regularidad para evitar consultas lentas y un almacenamiento sobrecargado. Este simple paso puede evitar que miles de archivos pequeños ralenticen las consultas hasta dejarlas prácticamente paradas.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EUsar el ordenamiento en Z para acelerar las consultas\u003C/h3\u003E\r\n\u003Cp\u003EAplica el ordenamiento en Z para agrupar en clústeres datos relacionados. Esto mejora el rendimiento de las consultas que filtran varias columnas, ya que reduce el número de archivos que deben leerse. Por ejemplo, una \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/retail-consumer-goods/\"\u003Eempresa de comercio electrónico\u003C/a\u003E podría aplicar un ordenamiento en Z de los datos de los clientes por ubicación e historial de compras para acelerar las consultas de recomendaciones.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EAutomatizar las comprobaciones de calidad de los datos\u003C/h3\u003E\r\n\u003Cp\u003EAplica reglas para rangos, tipos o formatos válidos durante la ingesta de nuevos datos. La detección de errores en la fase inicial evita que los registros defectuosos se propaguen en etapas posteriores. Si un sensor devuelve una lectura de temperatura negativa cuando no debería, el sistema puede marcarla o bloquearla antes de que comprometa el conjunto de datos.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EAprovechar el Time Travel de forma estratégica\u003C/h3\u003E\r\n\u003Cp\u003ENo uses el Time Travel solo para la recuperación ante errores. Esta función es también valiosa para auditorías, reproducir analíticas anteriores y probar cómo habría funcionado una nueva lógica con datos antiguos. De esta forma, los equipos pueden validar un nuevo modelo de previsión con los datos del año anterior sin sobrescribir los valores de producción actuales.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"]},"container_1858830768":{"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"solutions",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{},":itemsOrder":[]},"container_115652453":{"columnClassNames":{"text_copy_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"last",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"title_v2_copy_copy_c":{"id":"title-v2-0401c789d2","additionalClasses":"headline-decoration","type":"heading2","lines":["Conclusión"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_copy_copy_":{"id":"text-fd9e693a39","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EDelta Lake cierra la brecha entre la flexibilidad de un data lake y la fiabilidad de un \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-lake-vs-data-warehouse-vs-data-mart/\"\u003Ealmacén de datos\u003C/a\u003E. La superposición de funciones de tabla en capas (como transacciones ACID, aplicación de esquemas, gestión de metadatos y Time Travel) sobre el almacenamiento existente crea una única fuente de datos fiable que puede respaldar tanto los paneles de inteligencia empresarial (BI) como los \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ml-models/\"\u003Emodelos de aprendizaje automático (ML)\u003C/a\u003E.\u003C/p\u003E\n\u003Cp\u003EPara las organizaciones que crean plataformas de datos modernas, Delta Lake se ha convertido en una tecnología fundamental, que simplifica la arquitectura, mejora la \u003Ca href=\"https://www.snowflake.com/es/fundamentals/a-practical-guide-to-data-quality/\"\u003Ecalidad de los datos\u003C/a\u003E y refuerza la gobernanza. De cara al futuro, la tendencia apunta a formatos abiertos, inteligentes e integrados que hagan los datos más útiles en toda la empresa. Delta Lake lidera esta evolución y anticipa el futuro de la arquitectura de datos.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy_copy_c","text_copy_copy_copy_"]},"container_copy_copy__1500909857":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","simple_snowflake_acc":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"faq",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-346b97048f","additionalClasses":"headline-decoration","type":"heading2","lines":["Preguntas frecuentes sobre Delta Lake"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-dc2c4484d9","additionalClasses":"list--blue-bullets","showDivider":false,"accordionItemsList":[{"title":"¿Qué son las tablas Delta?","richText":"\u003Cp\u003ELas tablas Delta son conjuntos de datos almacenados en un \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-lake-vs-data-warehouse-vs-data-mart/\"\u003Edata lake\u003C/a\u003E pero gestionados con el registro de transacciones de Delta Lake. Son tablas normales en cuanto a su aspecto y su comportamiento, lo que significa que se pueden ejecutar consultas SQL, actualizaciones y eliminaciones en ellas. El registro de transacciones realiza un seguimiento de cada cambio, lo que fomenta la coherencia y habilita funciones como el Time Travel.\u003C/p\u003E\r\n"},{"title":"¿Cuál es la diferencia entre un data lake y Delta Lake?","richText":"\u003Cp\u003EUn data lake tradicional es un repositorio flexible de archivos sin procesar, pero carece de funciones a nivel de tabla, como transacciones, aplicación de esquemas y control de versiones. Delta Lake añade esas capacidades al data lake. Es como convertir una pila de archivos en tablas fiables y consultables que varios equipos pueden utilizar con confianza.\u003C/p\u003E\r\n"},{"title":"¿Qué es un data lakehouse?","richText":"\u003Cp\u003EUn data lakehouse es una arquitectura que combina los puntos fuertes de los data lakes y de los \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-warehouse-architecture-and-design/\"\u003Ealmacenes de datos\u003C/a\u003E. Utiliza formatos de tabla abiertos como Delta Lake para almacenar datos estructurados y sin procesar, a la vez que admite consultas fiables, gobernanza y alto rendimiento. El resultado es una única plataforma en la que las organizaciones pueden ejecutar workloads de \u003Ca href=\"https://www.snowflake.com/es/fundamentals/ai-for-business-intelligence/\"\u003EBI e IA\u003C/a\u003E sin tener que mover datos entre sistemas.\u003C/p\u003E\r\n"},{"title":"¿Es el formato Delta Lake de código abierto?","richText":"\u003Cp\u003ESí. Delta Lake es un proyecto de código abierto de Linux Foundation. Su modelo comunitario significa que sigue evolucionando rápidamente, con nuevas funciones e integraciones aportadas tanto por personas como por partners empresariales.\u003C/p\u003E\r\n"}],":type":"snowflake-site/components/simple-snowflake-accordion","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","simple_snowflake_acc"]},"container_copy_copy__480869401":{"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"customer",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-6b57b2c4df","additionalClasses":"headline-decoration","type":"heading2","lines":["Clientes que usan Snowflake"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"flexible_column_cont":{"id":"flexible-column-container-ec33822f2a","type":"2-column-even","alignColumns":"match-height","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-186d924f92",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"card_v2":{"id":"card-v2-224cbf095e","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Luminate amplía las analíticas del entretenimiento con un procesamiento de datos un 300 % más rápido e información más completa"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://www.snowflake.com/en/customers/all-customers/case-study/luminate/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Leer la historia"},"image":{"id":"image","lazyEnabled":true,"alt":"simon data logo","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--e6364738-9027-4e8f-9985-febcbb81ac1e/luminate-customer-card%25402x.jpg?quality=85&preferwebp=true","height":"720","width":"1680",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003ECon Snowflake como base, Luminate (que impulsa las emblemáticas listas musicales de Billboard y constituye el partner de datos más fiable del sector del entretenimiento) unificó sus datos para desarrollar productos más innovadores y procesar datos de forma óptima.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical"}},":itemsOrder":["card_v2"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-72a77edc2a",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"card_v2_copy":{"id":"card-v2-d6d616e67f","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Indeed reinventa la arquitectura y la colaboración de datos para ayudar a los demandantes de empleo y a los empleadores"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/customers/all-customers/case-study/indeed/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Leer la historia"},"image":{"id":"image","lazyEnabled":true,"alt":"penske logo","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--14eb361a-1be2-410e-824f-59d93961b512/indeed.png?quality=85&preferwebp=true","height":"1080","width":"2520",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003EGracias a una arquitectura de data lake moderna y a las data clean rooms de Snowflake, Indeed centraliza todos sus datos, lanza campañas más rápido y, en última instancia, ahorra millones de dólares a la empresa.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical"}},":itemsOrder":["card_v2_copy"]},"isBlogPage":false,":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false}},":itemsOrder":["title_v2","flexible_column_cont"]},"container_copy_copy__373061683":{"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"resources",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-c528524c11","type":"heading2","lines":["Recursos de analíticas de 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