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Sin embargo, la solución planteaba varios retos para el equipo de datos, como la implementación y el mantenimiento manuales de Spark, los problemas de escalabilidad de los recursos, los frecuentes fallos del flujo por falta de capacidad de computación, los conflictos de simultaneidad durante los picos de uso y las complejidades asociadas al movimiento de datos entre distintas plataformas.\u003C/p\u003E\n\u003Cp\u003EPara atender mejor las necesidades de sus usuarios, el equipo de datos de Sanofi decidió rediseñar su motor analítico. 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Las ventajas de Snowflake, como la separación de almacenamiento y cómputo, el mantenimiento prácticamente nulo y la escalabilidad adaptable, permitieron a Sanofi gestionar con eficiencia los aumentos en los workloads y en los volúmenes de datos sin que en rendimiento se viera afectado y optimizando los costes.\u003C/p\u003E\n\u003Cp\u003EDesde el inicio de la migración, el equipo dio prioridad a una arquitectura centrada en el servicio. Su objetivo era crear un sistema sólido y eficiente con servicios independientes, de modo que se mejorara el aislamiento de fallos para garantizar que los problemas de un servicio no tuvieran un impacto general en todo el sistema. 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Al utilizar Snowpark como capa de computación para código Python en la plataforma de Snowflake, ya no tenemos que transferir datos y nuestros administradores mantienen todo el control de los datos y las bibliotecas”.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"title_v2_copy_copy_8":{"id":"title-v2-f05198a14a","type":"heading4","lines":["Un 50 % más de rendimiento con Snowpark"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"blue-black-text-color snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_63041":{"id":"text-6e6389973f","text":"\u003Cp\u003ECon su arquitectura anterior, Sanofi tenía que lidiar con una serie de dificultades al usar un motor de Spark gestionado como capa de computación. La implementación manual del motor implicaba que había que actualizarlo también manualmente cada vez que se añadían nuevas funciones o se realizaban cambios en los flujos de back-end. Todo esto aumentaba la dependencia de los procesos manuales y la necesidad de coordinarse en todos los procesos, por lo que se tardaba más en ejecutar el flujo completo.\u003C/p\u003E\n\u003Cp\u003EDesarrollar y configurar un clúster de Spark también requería muchos recursos. “Observamos que el clúster no podía ampliarse y que había que configurarlo manualmente para lanzar mayores instancias y ejecutar consultas complejas o exigentes, lo que ocasionaba problemas de rendimiento en el flujo”, destaca Ratan Roy, ingeniero de datos en Sanofi. “Tampoco había en marcha ninguna optimización para automatizar nada, y el procesamiento demandaba grandes cantidades de memoria”. \u003C/p\u003E\n\u003Cp\u003EEl equipo de datos sufrió muchos casos de fallos o retrasos en el flujo por falta de recursos de computación. Como el entorno gestionado de Spark era compartido, la disponibilidad de los recursos de computación dependía de la disponibilidad del clúster de Spark y no de la demanda según lo solicitado. \u003C/p\u003E\n\u003Cp\u003ELa plataforma web que usa la comunidad médica experimentó problemas de simultaneidad cuando varios usuarios realizaron solicitudes para ejecutar a la vez programas de la aplicación web. Teniendo en cuenta la escala que supone el procesamiento de más de mil millones de registros con el clúster de Spark, el tiempo medio de respuesta de las solicitudes de análisis durante las horas punta fue de 15 minutos.\u003C/p\u003E\n\u003Cp\u003ESi bien la separación del cómputo y el almacenamiento de datos es una característica nativa de la plataforma de Snowflake, la anterior solución gestionada de Spark no contaba con una capa de almacenamiento de datos integrada. 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