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prácticas recomendadas de ingeniería de datos, de data lakehouses y Apache Iceberg a flujos modernos y DataOps, para preparar sus datos para la IA.","language":"es","title":"Guía fundamental de la ingeniería de datos","tags":[],"analyticsPageType":"homepage","analyticsCategory":"general","analyticsSubCategory":"","excludeFromAnalytics":false,":mappedPath":"/es/resources/ebook/the-essential-guide-to-data-engineering/",":type":"snowflake-site/components/structure/page",":items":{"root":{"columnClassNames":{"experiencefragment-banner":"aem-GridColumn aem-GridColumn--default--12","experiencefragment-header":"aem-GridColumn aem-GridColumn--default--12","markup_editor_1105360441":"aem-GridColumn aem-GridColumn--default--12","responsivegrid":"aem-GridColumn aem-GridColumn--default--12","experiencefragment-footer":"aem-GridColumn aem-GridColumn--default--12","experiencefragment":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn 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datos"],":type":"snowflake-site/components/title"},"image":{"id":"image","height":"403","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--fcadecbf-8567-411f-b545-75a1d3c23bae/default-simplified-lead-space.png?preferwebp=true&quality=85","alt":"Long exposure top down view of people walking on a pavement ","lazyEnabled":true,"width":"1280",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/simplified-lead-space"},"container":{"columnClassNames":{"marketo_container":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 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Después de todo, no hay estrategia de IA sin una estrategia de datos.\u003C/p\u003E\r\n\u003Cp\u003EPero, ¿por dónde se empieza una estrategia de datos? A partir de una visión y una sólida función de ingeniería de datos.\u003C/p\u003E\r\n\u003Cp\u003EEn pocas palabras, los ingenieros de datos son el alma operativa de toda organización basada en datos. Aceleran el tiempo de valorización y eliminan los cuellos de botella que ralentizan la productividad. La preparación de flujos de datos listos para la IA es fundamental para poder aprovechar los modelos de IA y aprendizaje automático (ML) y adoptar flujos de trabajo basados en agentes de IA. Por ello, es esencial comprender y reevaluar el impacto empresarial de la ingeniería de datos.\u003C/p\u003E\r\n\u003Cp\u003EEl propósito de esta guía es, precisamente, ayudarle a hacer eso. En ella, descubrirá:\u003C/p\u003E\r\n\u003Cp\u003E●      Cómo garantizar la integridad y la gobernanza de las transacciones adoptando arquitecturas y estándares abiertos, como data lakehouse y Apache Iceberg™.\u003C/p\u003E\r\n\u003Cp\u003E●      Estrategias para modernizar los flujos heredados con la incorporación de los workloads de Spark existentes en un motor gestionado, para impulsar la optimización del rendimiento y un ahorro significativo de costes.\u003C/p\u003E\r\n\u003Cp\u003E●      Cómo crear flujos eficientes con indicaciones sobre cuándo elegir un proceso de extracción, transformación y carga (ETL) o un proceso de extracción, carga y transformación (ELT), procesamiento por lotes o por transmisión, y cómo optimizar el rendimiento en tiempo real sin disparar sus costes operativos.\u003C/p\u003E\r\n\u003Cp\u003E●      Los principios de DataOps para optimizar la gestión del ciclo de vida de los datos, garantizar información verificable y de alta calidad, y reducir el tiempo 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