In the first two parts of our Inside Flink blog series, we explored the benefits of stream processing with Flink and common Flink use cases for which teams are choosing to leverage the popular framework to unlock the full potential of streaming. Specifically, we broke down the key reasons why developers are choosing Apache Flink® as their stream processing framework, as well as the ways in which they are putting it into practice. These range from streaming data pipelines to train ML models, to real-time inventory management in retail and predictive maintenance in manufacturing.
Next, we’ll dive into Flink SQL, which is a powerful data processing engine that allows developers to process and analyze large volumes of data in real time. We’ll cover how Flink SQL relates to the other Flink APIs and showcase some of its built-in functions and operations with syntax examples.
I’m naturally predisposed to blog posts which validate Feasel’s Law, so of course I was going to pick this one to recommend.