Esto anuncio permite a los usuarios de Spark aprovechar la potencia del motor de Snowflake directamente con su c\u00f3digo Spark existente. Snowpark Connect para Apache Sparkse basa en la arquitectura cliente-servidor desacoplada que permite separar el c\u00f3digo del usuario del cl\u00faster de Spark donde se ejecuta el procesamiento. Esta nueva arquitectura ahora hace posible que los trabajos de Spark sean impulsados por Snowflake, y ha sido presentada por la comunidad de Apache Spark en la versi\u00f3n 3.4.<\/p>\n\n\n\n
Con Snowpark Connect, los clientes pueden aprovechar el motor vectorizado de Snowflake para su c\u00f3digo Spark, evitando la complejidad de mantener o ajustar entornos Spark separados, incluyendo la gesti\u00f3n de dependencias, compatibilidad de versiones y actualizaciones. Con Snowpark Connect se puede ejecutar todo el c\u00f3digo moderno de Spark DataFrame, Spark SQL y funciones definidas por el usuario (UDF) con Snowflake.<\/p>\n\n\n\n
Snowflake gestiona autom\u00e1ticamente toda la optimizaci\u00f3n del rendimiento y el escalado, liberando a los desarrolladores de la sobrecarga operativa de gestionar Spark. Adem\u00e1s, al llevar el procesamiento de datos a Snowflake, se establece un marco de gobernanza \u00fanico y robusto en la fase inicial, lo que ayuda a garantizar la coherencia y seguridad de los datos a lo largo de todo el ciclo de vida, sin necesidad de duplicar esfuerzos.<\/p>\n\n\n\n
Snowpark Connect para Spark aprovecha la arquitectura desacoplada de Spark Connect, que permite que las aplicaciones env\u00eden un plan l\u00f3gico no resuelto a un cl\u00faster Spark remoto para su procesamiento. Esta filosof\u00eda de separaci\u00f3n cliente-servidor ha sido fundamental en el dise\u00f1o de Snowpark desde sus inicios. Actualmente, Snowpark Connect es compatible con las versiones 3.5.x de Spark, asegurando la compatibilidad con las \u00faltimas funcionalidades y mejoras introducidas en dichas versiones.<\/p>\n\n\n\n
Esta innovaci\u00f3n elimina la necesidad de mover datos entre Spark y Snowflake, un proceso que hist\u00f3ricamente ha generado costes adicionales, latencia y complejidad de gobernanza. Ahora, las organizaciones pueden ejecutar c\u00f3digo Spark DataFrame, SQL y UDF en Snowflake a trav\u00e9s de Snowflake Notebooks, cuadernos Jupyter, procedimientos almacenados de Snowflake, VSCode, Airflow o Snowpark Submit, lo que permite una integraci\u00f3n perfecta en diferentes almacenamientos en Snowflake, Iceberg (en Snowflake o gestionado externamente) y opciones de almacenamiento en la nube.<\/p>\n\n\n\n
snowflake.com<\/a><\/p>","protected":false},"excerpt":{"rendered":" Snowflake, la empresa detr\u00e1s de la plataforma AI Data Cloud, ha anunciado la disponibilidad en vista previa p\u00fablica de Snowpark Connect para Apache Spark. 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Snowflake, das Unternehmen, das hinter der AI-Data-Cloud-Plattform steht, hat die Verf\u00fcgbarkeit von Snowpark Connect f\u00fcr Apache Spark angek\u00fcndigt. <\/p>\n","category_list_v2":"Community Nachrichten<\/a>","author_info_v2":{"name":"E3 Magazine","url":"https:\/\/e3mag.com\/es\/author\/e3_magazin\/"},"comments_num_v2":"0 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