Building your streaming analytics applications has never been easier and faster than with Ververica Cloud, a fully-managed cloud-native service for real-time data processing. Effortlessly develop and deploy your applications in the cloud, without worrying about the underlying infrastructure.
Leverage its ultra-fast performance, fault tolerance, elasticity, and data connectivity along with the built-in SQL editor to create your real-time applications for security, IoT, analytics, machine learning, and more.
Based on Apache Flink, an industry-standard framework for stream processing, used by tech giants such as Uber, Ebay, Alibaba, and more.
The Enterprise Stream Processing Platform available on-premise or in the private cloud.
Enjoy the ultimate security and data protection by keeping your streaming analytics in-house. Ververica Platform drastically simplifies streaming apps’ development and operations, enabling enterprises to derive immediate insight from their data in real-time, with privacy.
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.
Ultra-high performance cloud-native service for real-time data processing based on Apache Flink
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!
In the Online Transaction Processing (OLTP) system, to solve the problem of a large amount of data in a single table, the method of sub-database and table is usually used to split a single large table to improve the throughput of the system.
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