The Enterprise Stream Processing Platform by the Original Creators of Apache Flink®
Ververica Platform enables every enterprise to take advantage and derive immediate insight from its data in real-time.
Powered by Apache Flink's robust streaming runtime, Ververica Platform makes this possible by providing an integrated solution for stateful stream processing and streaming analytics at scale.
Integrated platform for stateful stream processing & analytics with Apache Flink.
Build & deploy with confidence backed by Ververica’s Apache Flink experts.
Learn from the original creators of Apache Flink®
“When we migrated to Kubernetes with Ververica Platform it was much easier for our developers to define and configure jobs, as well as manage and monitor them in an integrated and efficient manner”
Flink Forward is the conference dedicated to Apache Flink and the stream processing community.
We’re excited to announce the 2023 Flink Forward event will be taking place November 6-8 in Seattle for an in-person event!
This tutorial will show how to quickly build streaming ETL for MySQL and Postgres based on Flink CDC. The examples in this article will all be done using the Flink SQL CLI, requiring only SQL and no Java/Scala code or the installation of an IDE.
Generic Log-based Incremental Checkpoint (GIC for short in this article) has become a production-ready feature since Flink 1.16 release.
This blog post will guide you through the Kafka connectors that are available in the Flink Table API. By the end of this blog post, you will have a better understanding of which connector is more suitable for a specific application.
Testing your Apache Flink SQL code is a critical step in ensuring that your application is running smoothly and provides the expected results. Flink SQL applications are used for a wide range of data processing tasks, from complex analytics to simple SQL jobs
This article will provide a more in-depth look at how to create a time window.