{"id":24974,"date":"2025-04-03T11:55:05","date_gmt":"2025-04-03T18:55:05","guid":{"rendered":"https:\/\/tdengine.com\/?p=24974"},"modified":"2025-09-08T16:39:47","modified_gmt":"2025-09-08T23:39:47","slug":"tdengine-introduces-integration-with-apache-flink","status":"publish","type":"post","link":"https:\/\/tdengine.com\/tdengine-introduces-integration-with-apache-flink\/","title":{"rendered":"TDengine Introduces Integration with Apache Flink"},"content":{"rendered":"\n<p>The TDengine team is excited to announce that Apache Flink is now supported as an ecosystem integration. With TDengine 3.3.6, you can configure TDengine to ingest data from Flink through a new connector.<\/p>\n\n\n\n<h2 class=\"gb-text\">About Flink<\/h2>\n\n\n\n<p><a href=\"https:\/\/flink.apache.org\/\" rel=\"noopener\">Apache Flink<\/a> is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale.<\/p>\n\n\n\n<p>In time-series data scenarios, Flink is often used as a stream processing engine. Although TDengine provides stream processing as a built-in component, we also support integration with data architectures that include stream processing engines like Flink to ensure that our users have the freedom to construct the optimal data stacks for their workflows.<\/p>\n\n\n\n<h2 class=\"gb-text\">Integration Details<\/h2>\n\n\n\n<p>TDengine&#8217;s Flink connector efficiently and accurately ingests data processed in Flink from different data sources and operators and stores it in our high-performance time-series database. You can find instructions along with sample code in our <a href=\"https:\/\/docs.tdengine.com\/third-party\/collection\/flink\/\">documentation<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>With TDengine 3.3.6, you can configure TDengine to ingest data from Flink through a new connector.<\/p>\n","protected":false},"author":102,"featured_media":24981,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[21],"tags":[],"ppma_author":[181],"class_list":["post-24974","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-engineering"],"authors":[{"term_id":181,"user_id":102,"is_guest":0,"slug":"tdengine-team","display_name":"TDengine Team","avatar_url":{"url":"https:\/\/tdengine.com\/wp-content\/uploads\/29.03-01-tdengine.png","url2x":"https:\/\/tdengine.com\/wp-content\/uploads\/29.03-01-tdengine.png"},"1":"","2":"","3":"","4":"","5":"","6":"","7":"","8":""}],"_links":{"self":[{"href":"https:\/\/tdengine.com\/wp-json\/wp\/v2\/posts\/24974","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/tdengine.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/tdengine.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/tdengine.com\/wp-json\/wp\/v2\/users\/102"}],"replies":[{"embeddable":true,"href":"https:\/\/tdengine.com\/wp-json\/wp\/v2\/comments?post=24974"}],"version-history":[{"count":3,"href":"https:\/\/tdengine.com\/wp-json\/wp\/v2\/posts\/24974\/revisions"}],"predecessor-version":[{"id":28364,"href":"https:\/\/tdengine.com\/wp-json\/wp\/v2\/posts\/24974\/revisions\/28364"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/tdengine.com\/wp-json\/wp\/v2\/media\/24981"}],"wp:attachment":[{"href":"https:\/\/tdengine.com\/wp-json\/wp\/v2\/media?parent=24974"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/tdengine.com\/wp-json\/wp\/v2\/categories?post=24974"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/tdengine.com\/wp-json\/wp\/v2\/tags?post=24974"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/tdengine.com\/wp-json\/wp\/v2\/ppma_author?post=24974"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}