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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});","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"}},":itemsOrder":["markup_editor"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":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"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-ad9009a3d6",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"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",":items":{"title_v2":{"id":"title-v2-832a44ebc6","type":"heading2","lines":["Descripción general"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-55c05c041e","text":"\u003Cp\u003ELas empresas y organizaciones generan grandes cantidades de datos a diario, pero en su estado sin procesar representan más potencial que valor real. Cuando se elabora una receta o se fabrica un producto en una cadena de montaje, el resultado final es la suma de sus partes: todo se combina para crear algo útil (o delicioso). Del mismo modo, cuando una empresa se enfrenta a una maraña de datos sin procesar, necesita darles sentido antes de que puedan ser útiles para alguien. Esto se logra mediante una serie de pasos conocidos como procesamiento de datos.\u003C/p\u003E\n\u003Cp\u003EEl procesamiento de datos es el proceso mediante el cual los datos sin procesar y, a menudo, caóticos se estructuran en un formato útil. A través de una serie de operaciones, las empresas revelan el valor oculto que se encuentra en columnas de números, páginas de respuestas a encuestas y hojas de cálculo repletas de información. Es la base de la estrategia empresarial y hace posible todo, desde las analíticas empresariales hasta el aprendizaje automático (ML).\u003C/p\u003E\n\u003Cp\u003EEn esta guía exploramos qué es el procesamiento de datos y por qué es importante, sus fases, tipos y métodos, así como las herramientas y tecnologías asociadas. Y terminamos con algunas de las preguntas más frecuentes sobre esta operación crítica para el negocio.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"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":"a",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-519f8bd6fe","additionalClasses":"headline-decoration","type":"heading2","lines":["¿Qué es el procesamiento de datos?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-6c2bdfe2fc","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EEl procesamiento de datos es una serie sistemática de operaciones que toma datos sin procesar y no organizados y los transforma en información utilizable, a partir de la cual las organizaciones pueden obtener conclusiones relevantes y tomar decisiones fundamentadas. Es un elemento fundamental de la estrategia empresarial y resulta clave para que el análisis de datos sea posible.\u003C/p\u003E\r\n\u003Cp\u003EHistóricamente, el procesamiento de datos ha sido un proceso manual muy laborioso y lento. Las computadoras humanas (personas a las que se les asignaba el \u003Ca href=\"https://www.nasa.gov/centers-and-facilities/jpl/when-computers-were-human/\"\u003Etítulo de “computadora”\u003C/a\u003E) dependían de herramientas físicas como libros de contabilidad, formularios y calculadoras, así como de sistemas en papel, para recopilar, almacenar y analizar datos. \u003Ca href=\"https://www.rudderstack.com/learn/data-collection/data-collection-history/\"\u003ENotoriamente\u003C/a\u003E, Estados Unidos tardó \u003Ci\u003Esiete años\u003C/i\u003E en publicar los resultados del censo de 1880 debido a la lentitud de los procesos de recuento manual, lo que llevó a Herman Hollerith, un empleado de la Oficina del Censo, a inventar la máquina tabuladora. Esta redujo drásticamente el tiempo necesario para procesar los datos censales, de años a meses, y sentó las bases del procesamiento de datos moderno.\u003C/p\u003E\r\n\u003Cp\u003EHoy en día, el procesamiento de datos es un proceso electrónico gestionado por ordenadores y automatización, normalmente a cargo de analistas de datos, procesadores de datos, \u003Ca href=\"https://www.snowflake.com/es/product/data-engineering/\"\u003Eingenieros de datos\u003C/a\u003E y científicos de datos. La \u003Ca href=\"https://www.snowflake.com/es/product/ai/\"\u003EIA y el ML\u003C/a\u003E desempeñan un papel importante en la gestión de conjuntos de datos especialmente grandes. El procesamiento de datos suele describirse como un ciclo, en el que se sigue una serie de pasos para llevar los datos desde su estado sin procesar hasta su análisis, interpretación y posterior almacenamiento.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_":{"columnClassNames":{"container_copy_copy__987904085":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1985496925":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__127842237":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_259718203":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__714116681":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1190438074":"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",":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":"is",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-add3dcb65e","additionalClasses":"headline-decoration","type":"heading2","lines":["¿Por qué es importante el procesamiento de datos?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-f0fbf52357","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ESin el procesamiento de datos, las enormes cantidades de datos que generan las organizaciones cada segundo no serían más que ruido digital. El procesamiento de datos tiende un puente entre la información sin procesar, que rara vez resulta útil en su estado original, y los conocimientos clave que pueden orientar las decisiones empresariales y proporcionar a las organizaciones una ventaja competitiva.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EMejora de la toma de decisiones: \u003C/b\u003ELas empresas no pueden basarse en suposiciones y conjeturas si quieren competir y crecer. La claridad que aporta el procesamiento de datos puede mejorar la toma de decisiones de múltiples formas, entre ellas:\u003Cbr\u003E\r\n\u003Cbr\u003E\r\n\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EIdentificación de tendencias del mercado:\u003C/b\u003E Los datos de ventas pueden proporcionar información sobre qué productos se venden mejor, qué grupos demográficos los compran, en qué épocas del año tienen mayor demanda, etc.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EMejora de la eficiencia operativa:\u003C/b\u003E El análisis de los datos de la cadena de suministro, de logística y de producción puede ayudar a las empresas a identificar gastos innecesarios y cuellos de botella, así como a optimizar sus procesos.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EPredicciones basadas en datos:\u003C/b\u003E Las analíticas predictivas utilizan datos históricos para prever resultados, lo que ayuda a las empresas a anticiparse a las necesidades de los clientes, gestionar el inventario y mitigar riesgos.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E\u003Cb\u003EMayor precisión y fiabilidad:\u003C/b\u003E Los datos sin procesar suelen contener errores, duplicados e incoherencias. En muchos sectores, como los de \u003Ca href=\"https://www.snowflake.com/es/fundamentals/governance-risk-compliance/\"\u003Egobernanza, riesgo y cumplimiento (GRC)\u003C/a\u003E, detección de fraudes y finanzas, un único error o discrepancia puede derivar en complicaciones aún mayores. La fase de limpieza del procesamiento de datos (que explicaremos más adelante) identifica y corrige estos problemas, lo que hace que los datos sean más precisos y fiables cuando llega el momento del análisis.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EMayor ventaja competitiva:\u003C/b\u003E Procesar y aprovechar los datos de forma eficaz es un factor diferenciador clave para las empresas que desean adelantarse a la competencia. Algunas de las ventajas que ofrece son las siguientes:\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EPersonalización de la experiencia del cliente:\u003C/b\u003E El procesamiento de datos de clientes permite a las empresas ofrecer recomendaciones personalizadas, campañas de marketing focalizadas y servicios pertinentes, lo que refuerza la fidelidad a la marca y la retención de clientes.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ERespuesta a los cambios del mercado:\u003C/b\u003E El procesamiento de datos en tiempo real permite a las empresas reaccionar rápidamente ante los cambios del mercado, ya sea el lanzamiento de un nuevo producto por parte de un competidor o un cambio en la demanda de los clientes.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E\u003Cb\u003EMayor seguridad y cumplimiento de los datos:\u003C/b\u003E El procesamiento de datos no solo consiste en hacerlos útiles, sino también en hacerlos seguros. La limpieza y la organización de los datos incorporan \u003Ca href=\"https://www.acceldata.io/blog/data-compliance\"\u003Emedidas de protección\u003C/a\u003E específicas, como el \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-masking/\"\u003Eenmascaramiento de datos\u003C/a\u003E, la \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-anonymization/\"\u003Eanonimización\u003C/a\u003E, el cifrado y la tokenización. Los sistemas de procesamiento de datos también aplican normas sobre quién puede acceder a los datos, modificarlos o eliminarlos. Además, muchas normativas de datos, como el RGPD y la HIPAA, establecen requisitos estrictos sobre qué datos se pueden recopilar, cómo se pueden usar, etc. Los sistemas de procesamiento de datos documentan meticulosamente cada fase del ciclo de vida de los datos, desde la recopilación hasta la eliminación. De este modo, se crea un registro de auditoría que demuestra que la organización cumple con las normativas.\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"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"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":"use",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-a03d12781d","additionalClasses":"headline-decoration","type":"heading2","lines":["Fases del procesamiento de datos"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-4d6285868d","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EEn esta guía, hemos comparado el procesamiento de datos con una cadena de montaje industrial. Al igual que las etapas de la fabricación de un automóvil, desde el ensamblaje del chasis hasta la pintura y el acabado final, el procesamiento de datos sigue un flujo de trabajo estructurado de varios pasos. Cada paso es esencial para convertir datos desordenados y sin procesar en datos limpios y fiables, en los que las organizaciones confían para tomar decisiones fundamentadas y crear estrategias sólidas.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E1. Recopilación\u003C/h4\u003E\r\n\u003Cp\u003EEs hora de empezar a recopilar datos, que pueden proceder de infinidad de fuentes: registros de transacciones y bases de datos corporativas, estadísticas de la interacción en redes sociales y encuestas a clientes. A menudo se almacenan en \u003Ca href=\"https://www.snowflake.com/es/product/analytics/\"\u003Edata lakes y almacenes de datos\u003C/a\u003E. Es crucial que los datos extraídos durante este primer paso sean relevantes, precisos y procedan de fuentes fiables. De lo contrario, se corre el riesgo de sesgar los resultados finales y comprometer por completo el proyecto desde el principio.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E2. Preparación\u003C/h4\u003E\r\n\u003Cp\u003EA menudo llamada preprocesamiento, esta es la fase más crítica y lenta, en la que los datos se limpian y organizan para garantizar la calidad y la coherencia. Incluye varios pasos:\u003Cbr\u003E\r\n\u003Cbr\u003E\r\n\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003ELimpieza de datos: \u003C/b\u003ECorrección de errores, imputación de valores ausentes y eliminación de datos duplicados o irrelevantes\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/unlocking-value-through-data-transformation-in-modern-pipelines/\"\u003ETransformación de datos\u003C/a\u003E:\u003C/b\u003E Conversión de los datos a un formato uniforme, como la estandarización de fechas, la conversión de texto en código numérico, etc.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EValidación de datos:\u003C/b\u003E Verificación de los datos respecto a reglas definidas para garantizar su precisión\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E\u003Ca href=\"https://www.fullstory.com/blog/data-enrichment/\"\u003EEnriquecimiento de los datos:\u003C/a\u003E \u003C/b\u003EMejora del conjunto de datos con información adicional relevante procedente de fuentes externas\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch4\u003E\u003Cbr\u003E\r\n3. Entrada\u003C/h4\u003E\r\n\u003Cp\u003EEn esta fase es donde los datos preparados se incorporan al sistema de procesamiento. Por primera vez, los datos sin procesar comienzan a adoptar la forma de datos utilizables. Ejemplos de sistemas de procesamiento podrían ser un software o un algoritmo diseñado para tipos de datos u objetivos de análisis específicos, como \u003Ca href=\"https://www.ibm.com/think/topics/apache-spark\"\u003EApache Spark\u003C/a\u003E para grandes conjuntos de datos. La introducción manual (para conjuntos de datos pequeños), la importación desde fuentes externas o la captura automática son algunas de las formas de incorporar datos a estos sistemas.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E4. Procesamiento\u003C/h4\u003E\r\n\u003Cp\u003EComo su nombre indica, este es el núcleo del ciclo de procesamiento de datos. Para transformar los datos en información significativa, se utilizan varias técnicas en función del resultado deseado o de los conocimientos que se necesiten. Estas son algunas de ellas:\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EClasificación: \u003C/b\u003EOrdenar los datos en un orden determinado\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EFiltrado:\u003C/b\u003E Seleccionar subconjuntos específicos de datos\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ECálculo: \u003C/b\u003ERealizar operaciones matemáticas, como calcular totales o promedios\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EAgregación:\u003C/b\u003E Resumir datos procedentes de varios registros\u003Cbr\u003E\r\n \u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch4\u003E5. Resultados e interpretación\u003C/h4\u003E\r\n\u003Cp\u003EDespués del procesamiento, los datos se presentan en un formato claro y fácil de entender. El resultado es el producto final, que puede ser un gráfico, un panel u otra representación visual. La fase de interpretación consiste en analizar el resultado para extraer conclusiones, identificar tendencias y tomar decisiones fundamentadas; es ahí donde, por fin, se materializa el valor de los datos procesados.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E6. Almacenamiento\u003C/h4\u003E\r\n\u003Cp\u003EEl último paso consiste en almacenar de forma segura los datos procesados en bases de datos o almacenes de datos para su uso y recuperación futuros. Este paso es crucial por varias razones:\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EAuditoría y cumplimiento:\u003C/b\u003E Crea un registro con fines legales y normativos.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EAnálisis futuros:\u003C/b\u003E Los datos pueden utilizarse como base para análisis sucesivos más complejos.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EReferencia:\u003C/b\u003E Proporciona una fuente fiable de información histórica para la toma de decisiones.\u003C/li\u003E\r\n\u003C/ul\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"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_259718203":{"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":"and",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-8ae569d7dd","additionalClasses":"headline-decoration","type":"heading2","lines":["Tipos de procesamiento de datos"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-16b1e4188b","text":"\u003Cp\u003ESe utilizan distintos métodos para transformar datos sin procesar en información significativa y utilizable. Aunque hay bastantes, y cada uno se adapta mejor a distintos escenarios y requisitos, el procesamiento por lotes, el procesamiento en tiempo real y el procesamiento en línea son tres de los más comunes.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E1. Procesamiento por lotes\u003C/h4\u003E\n\u003Cp\u003EEl \u003Ca href=\"https://www.snowflake.com/en/product/use-cases/streaming-data-pipelines/\"\u003Eprocesamiento por lotes\u003C/a\u003E es un método en el que se recopila un gran volumen de datos durante un periodo de tiempo y, a continuación, se procesa todo de una sola vez, en un lote. Este enfoque es ideal para tareas que no son urgentes y que se pueden programar fuera de las horas punta para ahorrar recursos de computación. Entre los casos de uso habituales se incluyen sistemas de nóminas, facturación mensual, informes de fin de jornada y generación de extractos bancarios. Por ejemplo, una empresa de tarjetas de crédito puede recopilar todas las transacciones del día y procesarlas en un único lote durante la noche para actualizar las cuentas de los clientes.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E2. Procesamiento en tiempo real\u003C/h4\u003E\n\u003Cp\u003EEl procesamiento en tiempo real gestiona los datos a medida que se generan, proporcionando resultados inmediatos. Este método es fundamental cuando la transición de la entrada a la salida de datos debe ser instantánea, especialmente en sistemas donde un retraso podría tener consecuencias graves. La \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/financial-services/fraud-detection-and-financial-crimes/\"\u003Edetección de fraudes en transacciones financieras\u003C/a\u003E, los sistemas GPS y el control del tráfico aéreo son ejemplos típicos de este tipo de procesamiento de datos.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E3. Procesamiento en línea\u003C/h4\u003E\n\u003Cp\u003EEl \u003Ca href=\"https://www.snowflake.com/en/fundamentals/online-analytical-processing/\"\u003Eprocesamiento en línea\u003C/a\u003E es un tipo de procesamiento interactivo en tiempo real. Procesa las transacciones iniciadas por el usuario a medida que se producen, proporcionando una respuesta inmediata. Esto es lo que se experimenta a diario cuando se interactúa con sitios web y aplicaciones. En pocas palabras, un usuario inicia una solicitud o introduce datos, y el sistema los procesa de inmediato y devuelve una respuesta. Estos sistemas están siempre en línea y listos para gestionar solicitudes en cualquier momento. El comercio electrónico, la banca en línea, las reservas de vuelos y los juegos en línea utilizan este tipo de procesamiento en línea. ¿Alguna vez compraste entradas para un concierto o para el cine en línea? Así es como se procesa tu pago y el sistema se actualiza inmediatamente para garantizar que nadie más pueda comprar una entrada para los mismos asientos.\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"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__987904085":{"columnClassNames":{"text_copy_1241045613":"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":"how",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-f7064a7449","additionalClasses":"headline-decoration","type":"heading2","lines":["Métodos de procesamiento de datos"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-ca18ffb5ee","text":"\u003Cp\u003EExisten diferentes métodos para procesar los datos, y no todos son compatibles con todos los tipos de procesamiento.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E1. Procesamiento manual de datos\u003C/h4\u003E\n\u003Cp\u003EEste es el método de procesamiento de datos más antiguo y tradicional, que consiste en recopilar, organizar y analizar los datos completamente a mano, sin ayuda de máquinas. Es lento, requiere mucha mano de obra, es propenso a errores y no es ideal para grandes volúmenes de datos. Sin embargo, puede ser una buena opción para operaciones a pequeña escala o para casos en los que el criterio humano sea esencial, como realizar un recuento manual de papeletas durante unas elecciones.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E2. Procesamiento mecánico de datos\u003C/h4\u003E\n\u003Cp\u003ESi utilizas máquinas y dispositivos sencillos para procesar datos, como calculadoras, máquinas de escribir o máquinas de tarjetas perforadas, estás usando el método de procesamiento mecánico de datos. La máquina tabuladora de Hollerith que mencionamos antes en esta guía es un ejemplo de este método. El procesamiento mecánico de datos es adecuado para tareas sencillas y comete menos errores que el procesamiento manual, pero aun así no es una buena opción para conjuntos de datos enormes.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E3. Procesamiento electrónico de datos\u003C/h4\u003E\n\u003Cp\u003EEl \u003Ca href=\"https://www.lenovo.com/us/en/glossary/what-is-edp/?orgRef=https%253A%252F%252Fwww.google.com%252F&srsltid=AfmBOooBYFjcgNsJVG7eEUuzaIdrjkhzOJ_4Z3st5mhFK4tMT58CPgpa\"\u003Eprocesamiento electrónico de datos (EDP)\u003C/a\u003E es el método más moderno y difundido, ya que se basa en soluciones electrónicas como ordenadores, servidores y automatización para procesar datos. Es un enfoque muy eficiente, preciso y escalable que puede procesar grandes volúmenes de datos en tiempo real. El EDP automatiza todo el ciclo de procesamiento de datos, desde la entrada hasta la salida, y se utiliza en prácticamente todos los sectores, desde sistemas sencillos de nóminas hasta aplicaciones de big data.\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_copy_1241045613"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__714116681":{"columnClassNames":{"text_copy_1241045613":"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":"why",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-ac94d5dda0","additionalClasses":"headline-decoration","type":"heading2","lines":["Herramientas y tecnologías de procesamiento de datos"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-6d864aae57","text":"\u003Cp\u003EEl procesamiento de datos moderno se basa en una combinación de herramientas potentes y tecnologías emergentes para extraer información valiosa de datos sin procesar. Estas soluciones abarcan desde el almacenamiento básico de datos hasta análisis complejos y automatizados. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E1. Bases de datos y almacenes de datos\u003C/h4\u003E\n\u003Cp\u003ESe trata de herramientas fundamentales para la gestión y el almacenamiento de datos, pero tienen \u003Ca href=\"https://www.coursera.org/articles/data-warehouse-vs-database\"\u003Edistintos fines\u003C/a\u003E en el \u003Ca href=\"https://www.snowflake.com/es/fundamentals/modernizing-data-pipelines/\"\u003Eflujo de procesamiento\u003C/a\u003E.\u003C/p\u003E\n\u003Cp\u003ELas \u003Cb\u003Ebases de datos\u003C/b\u003E sirven para almacenar y organizar información de una única fuente de datos para una función concreta del negocio. Es como una especie de archivador meticulosamente organizado para un único propósito. Están diseñadas para tareas rápidas y frecuentes, así como para consultas pequeñas. Entre las bases de datos más conocidas se incluyen sistemas basados en SQL como MySQL, PostgreSQL y Microsoft SQL Server.\u003C/p\u003E\n\u003Cp\u003EPor el contrario, los \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-warehouse-architecture-and-design/\"\u003Ealmacenes de datos\u003C/a\u003E son grandes repositorios centralizados para almacenar enormes volúmenes de datos históricos procedentes de múltiples fuentes. Se han diseñado para el análisis y, en esencia, son la biblioteca a la que acuden los analistas de datos para encontrar información con la que responder preguntas sobre tendencias empresariales complejas. Están pensados para ejecutar consultas complejas sobre grandes conjuntos de datos y generar informes e inteligencia empresarial. Los \u003Ca href=\"https://www.snowflake.com/es/product/platform/\"\u003Ealmacenes de datos\u003C/a\u003E suelen utilizar tecnologías de big data como Snowflake, Hadoop, Apache Spark y \u003Ca href=\"https://www.snowflake.com/es/fundamentals/data-lakes-and-data-lake-architecture/\"\u003Edata lakes\u003C/a\u003E. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E2. Inteligencia artificial y aprendizaje automático\u003C/h4\u003E\n\u003Cp\u003ELa IA y el ML son tecnologías potentes que automatizan y mejoran cada una de las fases del procesamiento de datos. Van más allá de los cálculos tradicionales, ya que permiten identificar patrones en los datos y generar predicciones. La IA puede automatizar la limpieza y preparación de datos, así como detectar y corregir automáticamente errores, completar valores ausentes y estandarizar formatos. Cuando los modelos de ML se entrenan con datos históricos, pueden hacer predicciones, encontrar anomalías y segmentar datos.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E3. Tecnología de nube y plataformas de analíticas de datos\u003C/h4\u003E\n\u003Cp\u003ELos proveedores de nube como Amazon Web Services (AWS), Google Cloud Platform (GCP) y Microsoft Azure permiten a las empresas ampliar o reducir sus recursos de procesamiento de datos según sus necesidades, sin tener que adquirir ni mantener costoso hardware on-premise. También permiten procesar big data a escala, algo que, de otro modo, sería imposible para la mayoría de las empresas.\u003C/p\u003E\n\u003Cp\u003ELas plataformas de analíticas de datos son soluciones de software, a menudo basadas en la nube, que proporcionan un entorno completo para el procesamiento de datos. Snowflake y Tableau ofrecen una plataforma unificada para almacenar datos, ejecutar consultas analíticas, crear visualizaciones y simplificar flujos de trabajo complejos. Por ejemplo, con el \u003Ca href=\"https://www.snowflake.com/es/product/platform/\"\u003EAI Data Cloud de Snowflake\u003C/a\u003E, los datos se optimizan para operaciones de alto rendimiento una vez cargados en la plataforma, y este servicio se ejecuta sobre las principales nubes públicas.\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_copy_1241045613"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1985496925":{"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":"customer",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-db145c16ea","additionalClasses":"headline-decoration","type":"heading2","lines":["Conclusión"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-a16612d446","text":"\u003Cp\u003EEl procesamiento de datos es el motor indispensable que transforma los datos sin procesar y no organizados en información valiosa para el negocio, que las empresas necesitan para tomar decisiones fundamentadas. Hemos avanzado significativamente desde los días puramente manuales de registro y análisis de datos. Hoy contamos con soluciones potentes y automatizadas, basadas en IA y ML, capaces de gestionar el enorme volumen de datos que generan las empresas, que sigue creciendo de forma exponencial. Un procesamiento de datos eficiente e inteligente es más importante que nunca para comprender la creciente marea de datos que generan las organizaciones a diario y garantizar su crecimiento y éxito futuros.\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"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__127842237":{"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",":items":{"title_v2":{"id":"title-v2-d34977b4e7","additionalClasses":"headline-decoration","type":"heading2","lines":["Preguntas frecuentes sobre el procesamiento de datos"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-144c70be38","showDivider":false,"accordionItemsList":[{"title":"¿Qué tipos de software están disponibles para el procesamiento de big data?","richText":"\u003Cp\u003E\u003Cb\u003EMarcos de computación distribuida:\u003C/b\u003E Apache Hadoop, Apache Spark\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EAlmacenes de datos basados en la nube:\u003C/b\u003E Google BigQuery, Amazon Redshift, Microsoft Azure HDInsight\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EBases de datos NoSQL:\u003C/b\u003E MongoDB, Apache Cassandra\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ESistemas de procesamiento de flujos: \u003C/b\u003EApache Flink, Apache Storm\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EHerramientas de visualización e inteligencia empresarial (BI): \u003C/b\u003ETableau, Microsoft Power BI\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EPlataformas de datos integradas:\u003C/b\u003E Snowflake\u003C/p\u003E\r\n"},{"title":"¿Cuáles son algunas fuentes de datos que se utilizan habitualmente en big data?","richText":"\u003Cp\u003EEl big data procede de una amplia variedad de fuentes, que se pueden clasificar, a grandes rasgos, en tres tipos: \u003Ca href=\"https://www.snowflake.com/en/fundamentals/understanding-structured-semi-structured-and-unstructured-data/\"\u003Eestructurados, no estructurados y semiestructurados\u003C/a\u003E.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EEstructurados:\u003C/b\u003E Muy organizados, siguen un formato predeterminado. Normalmente se almacenan en tablas, lo que los convierte en el tipo de datos más fácil de buscar, gestionar y analizar con herramientas tradicionales. Ejemplos: transacciones financieras, datos de punto de venta (TPV), historias clínicas.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ENo estructurados:\u003C/b\u003E Carecen de un formato predefinido. Son el tipo más común de big data, pero plantean los mayores desafíos para el análisis. Incluyen texto, imágenes, audio y vídeo. Ejemplos: datos de redes sociales, PDF y correos electrónicos, datos de sensores de termostatos inteligentes o dispositivos ponibles.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/semi-structured-data/\"\u003ESemiestructurados\u003C/a\u003E:\u003C/b\u003E Un híbrido de los otros dos tipos. Carecen de una estructura rígida como los datos estructurados, pero tienen ciertas propiedades organizativas que facilitan su categorización y análisis en comparación con los datos no estructurados. Ejemplos: archivos XML y JSON, archivos de registro, páginas web.\u003C/p\u003E\r\n"},{"title":"¿Cuáles son algunos ejemplos de procesamiento de datos?","richText":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EProcesamiento de nóminas: \u003C/b\u003ELos datos de los empleados, incluidas las horas trabajadas, las deducciones y la información sobre salarios e impuestos, se utilizan para calcular y emitir los pagos según lo previsto.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ERecomendaciones de comercio electrónico:\u003C/b\u003E Cuando navegas por una tienda en línea, los sistemas de esa empresa procesan tu historial de búsqueda, compras anteriores, etc. para recomendarte productos que podrían gustarte.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EPrevisión meteorológica:\u003C/b\u003E Los meteorólogos procesan una enorme cantidad de datos procedentes de satélites, sensores terrestres y estaciones meteorológicas para crear modelos complejos que predicen patrones y emiten previsiones (aunque lo precisa que te parezca la previsión local ya es otra historia).\u003C/li\u003E\r\n\u003C/ul\u003E\r\n"}],":type":"snowflake-site/components/simple-snowflake-accordion","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","simple_snowflake_acc"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1190438074":{"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":"ai",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-1d686d8a2e","additionalClasses":"headline-decoration","type":"heading2","lines":["Clientes que usan el AI Data Cloud para el procesamiento de datos "],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"flexible_column_cont":{"id":"flexible-column-container-aba64d5619","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-45bc0537b5",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"card_v2":{"id":"card-v2-5d5afac2f6","configurationStatus":{"configured":true,"message":""},"title":{"id":"title","type":"heading4","lines":["Luminate amplía las analíticas del entretenimiento con un procesamiento de datos un 300 % más rápido e información más 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