Enterprise Stream Processing & Analytics

The Apache Flink® ConferenceSeattle 2023

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!

How-to guide: Build Streaming ETL for MySQL and Postgres based on Flink CDC

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

Generic Log-based Incremental Checkpoint (GIC for short in this article) has become a production-ready feature since Flink 1.16 release. 

Streaming modes of Flink-Kafka connectors

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.

How to test your Flink SQL 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

Flink SQL: How to detect patterns with MATCH_RECOGNIZE

Ververica Platform makes Flink SQL more accessible and efficiently scalable across teams. This article is part of our series introducing you to Flink SQL and will cover the MATCH_RECOGNIZE function. 

Flink SQL: Queries, Windows, and Time - Part 2

This article will provide a more in-depth look at how to create a time window.

Flink SQL: Queries, window and Time

Stream processing can be used for a variety of applications, such as monitoring systems, fraud detection, and any application that wants to provide real-time insights

Flink SQL: Deduplication

Flink SQL has emerged as the de facto standard for low-code data analytics. It has managed to unify batch and stream processing while simultaneously staying true to the SQL standard.