{"id":30516,"date":"2026-01-02T10:02:00","date_gmt":"2026-01-02T09:02:00","guid":{"rendered":"https:\/\/www.sysbus.eu\/?p=30516"},"modified":"2025-12-17T11:06:07","modified_gmt":"2025-12-17T10:06:07","slug":"confluent-tableflow-fuer-ki-gestuetzte-und-cloud-uebergreifende-echtzeitanalysen","status":"publish","type":"post","link":"https:\/\/www.sysbus.eu\/?p=30516","title":{"rendered":"Confluent: Tableflow f\u00fcr KI-gest\u00fctzte und Cloud-\u00fcbergreifende Echtzeitanalysen"},"content":{"rendered":"\n<p>Confluent erweitert Tableflow zu einer vollst\u00e4ndig verwalteten Stream-to-Table-L\u00f6sung f\u00fcr KI-gest\u00fctzte Echtzeitanalysen in Multi-Cloud-Umgebungen. Neue Integrationen f\u00fcr Delta Lake und den Databricks Unity Catalog (GA) sowie Early Access f\u00fcr Microsoft OneLake bringen Apache-Kafka-Daten direkt in verwaltete Delta- und Iceberg-Tabellen. Das erm\u00f6glicht vereinfachte Analytik, einheitliche Governance, automatisierte Upserts und Qualit\u00e4tspr\u00fcfungen, Dead-Letter-Queues sowie \u201eBring Your Own Key\u201c f\u00fcr mehr Sicherheit. Mit der nativen Anbindung an Azure, Microsoft Fabric und die OneLake Table APIs lassen sich Streaming-Daten ohne ETL-Prozesse in Echtzeit f\u00fcr Analysen und KI-Anwendungsf\u00e4lle nutzen.<\/p>\n\n\n\n<p>Link: <a href=\"https:\/\/www.confluent.io\/de-de\/product\/tableflow\/\"><a href=\"https:\/\/www.confluent.io\/de-de\/product\/tableflow\/\">Tableflow: Kafka-Topics in Iceberg- und Delta-Tabellen umwandeln | Confluent | DE<\/a><\/a><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/www.sysbus.eu\/wp-content\/uploads\/2025\/12\/Confluent_NewInTableflowCurrentNOLA-Image1-002-1024x576.webp\" alt=\"\" class=\"wp-image-30518\" srcset=\"https:\/\/www.sysbus.eu\/wp-content\/uploads\/2025\/12\/Confluent_NewInTableflowCurrentNOLA-Image1-002-1024x576.webp 1024w, https:\/\/www.sysbus.eu\/wp-content\/uploads\/2025\/12\/Confluent_NewInTableflowCurrentNOLA-Image1-002-300x169.webp 300w, https:\/\/www.sysbus.eu\/wp-content\/uploads\/2025\/12\/Confluent_NewInTableflowCurrentNOLA-Image1-002-768x432.webp 768w, https:\/\/www.sysbus.eu\/wp-content\/uploads\/2025\/12\/Confluent_NewInTableflowCurrentNOLA-Image1-002.webp 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Confluent: Tableflow f\u00fcr KI-gest\u00fctzte und Cloud-\u00fcbergreifende Echtzeitanalysen &#8211; Quelle: Confluent<\/figcaption><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>Confluent erweitert Tableflow zu einer vollst\u00e4ndig verwalteten Stream-to-Table-L\u00f6sung f\u00fcr KI-gest\u00fctzte Echtzeitanalysen in Multi-Cloud-Umgebungen. Neue Integrationen f\u00fcr Delta Lake und den Databricks Unity Catalog (GA) sowie Early Access f\u00fcr Microsoft OneLake bringen Apache-Kafka-Daten direkt in verwaltete Delta- und Iceberg-Tabellen. Das erm\u00f6glicht vereinfachte Analytik, einheitliche Governance, automatisierte Upserts und Qualit\u00e4tspr\u00fcfungen, Dead-Letter-Queues sowie \u201eBring Your Own Key\u201c f\u00fcr mehr Sicherheit. Mit der nativen Anbindung an Azure, Microsoft Fabric und die OneLake Table APIs lassen sich Streaming-Daten ohne ETL-Prozesse in Echtzeit f\u00fcr Analysen und KI-Anwendungsf\u00e4lle nutzen.<\/p>\n","protected":false},"author":81,"featured_media":30518,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"colormag_page_container_layout":"default_layout","colormag_page_sidebar_layout":"default_layout","footnotes":""},"categories":[4],"tags":[19981,23999,3578,23998],"class_list":["post-30516","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","tag-confluent","tag-daten-streaming","tag-echtzeit","tag-tableflow"],"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/www.sysbus.eu\/index.php?rest_route=\/wp\/v2\/posts\/30516","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.sysbus.eu\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.sysbus.eu\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.sysbus.eu\/index.php?rest_route=\/wp\/v2\/users\/81"}],"replies":[{"embeddable":true,"href":"https:\/\/www.sysbus.eu\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=30516"}],"version-history":[{"count":1,"href":"https:\/\/www.sysbus.eu\/index.php?rest_route=\/wp\/v2\/posts\/30516\/revisions"}],"predecessor-version":[{"id":30519,"href":"https:\/\/www.sysbus.eu\/index.php?rest_route=\/wp\/v2\/posts\/30516\/revisions\/30519"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.sysbus.eu\/index.php?rest_route=\/wp\/v2\/media\/30518"}],"wp:attachment":[{"href":"https:\/\/www.sysbus.eu\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=30516"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.sysbus.eu\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=30516"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.sysbus.eu\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=30516"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}