Les utilisateurs qui utilisent Spark peuvent profiter de la puissance du moteur Snowflake directement avec leur code Spark existant. Snowpark Connect pour Apache Spark est bas\u00e9 sur une architecture client-serveur d\u00e9coupl\u00e9e qui permet de s\u00e9parer le code utilisateur du cluster Spark sur lequel le traitement est ex\u00e9cut\u00e9. Cette nouvelle architecture permet d\u00e9sormais d'ex\u00e9cuter des jobs Spark \u00e0 partir de Snowflake. Elle a \u00e9t\u00e9 pr\u00e9sent\u00e9e par la communaut\u00e9 Apache Spark dans la version 3.4.<\/p>\n\n\n\n
Snowpark Connect permet aux clients d'utiliser le moteur vectoris\u00e9 de Snowflake pour leur code Spark et d'\u00e9viter ainsi la complexit\u00e9 de la maintenance ou de l'adaptation d'environnements Spark s\u00e9par\u00e9s, y compris la gestion des d\u00e9pendances, la compatibilit\u00e9 des versions et les mises \u00e0 jour. Avec Snowpark Connect, tout le code moderne de Spark DataFrame, Spark SQL et les fonctions personnalis\u00e9es (UDF) peuvent \u00eatre ex\u00e9cut\u00e9s avec Snowflake.<\/p>\n\n\n\n
Snowflake s'occupe automatiquement de toute l'optimisation des performances et de la mise \u00e0 l'\u00e9chelle, de sorte que les d\u00e9veloppeurs n'ont plus \u00e0 se soucier de l'exploitation de Spark. En transf\u00e9rant le traitement des donn\u00e9es sur Snowflake, on met \u00e9galement en place d\u00e8s le d\u00e9part un cadre de gouvernance unifi\u00e9 et robuste qui garantit la coh\u00e9rence et la s\u00e9curit\u00e9 des donn\u00e9es tout au long de leur cycle de vie, sans faire double emploi.<\/p>\n\n\n\n
Snowpark Connect pour Spark utilise l'architecture d\u00e9coupl\u00e9e de Spark Connect, qui permet aux applications d'envoyer un plan logique non r\u00e9solu \u00e0 un cluster Spark distant pour traitement. Cette philosophie de s\u00e9paration du client et du serveur a \u00e9t\u00e9 un \u00e9l\u00e9ment essentiel de la conception de Snowpark d\u00e8s le d\u00e9but. Actuellement, Snowpark Connect prend en charge les versions 3.5.x de Spark, assurant ainsi la compatibilit\u00e9 avec les derni\u00e8res fonctionnalit\u00e9s et am\u00e9liorations de ces versions.<\/p>\n\n\n\n
Cette innovation \u00e9limine la n\u00e9cessit\u00e9 de d\u00e9placer les donn\u00e9es entre Spark et Snowflake, un processus qui, par le pass\u00e9, entra\u00eenait des co\u00fbts suppl\u00e9mentaires, de la latence et de la complexit\u00e9 de gouvernance. D\u00e9sormais, les entreprises peuvent ex\u00e9cuter le code Spark DataFrame, SQL et UDF dans Snowflake via les ordinateurs portables Snowflake, les ordinateurs portables Jupyter, les proc\u00e9dures Snowflake stock\u00e9es, VSCode, Airflow ou Snowpark Submit, ce qui permet une int\u00e9gration transparente avec les diff\u00e9rentes options de stockage dans Snowflake, Iceberg (g\u00e9r\u00e9 dans Snowflake ou en externe) et le cloud.<\/p>\n\n\n\n
snowflake.com<\/a><\/p>","protected":false},"excerpt":{"rendered":" Snowflake, la soci\u00e9t\u00e9 \u00e0 l'origine de la plateforme cloud de donn\u00e9es IA, a annonc\u00e9 la disponibilit\u00e9 de Snowpark Connect pour 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\/fr\/author\/e3_magazin\/"},"comments_num_v2":"0 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