apache flink ai streaming Exploring Squirrly Apache Flink Performance Issues through Continual AI-Driven Evaluation Last month, I wrote up a blog post discussing a three-phase cycle (profile, analyze, improve) for leveraging LLMs and other AI tooling to evaluate Apache Flink jobs for potential performance improvements. This post goes through what that process might look like in a more automated fashion to help you track
apache flink streaming open-source Exploring Dynamic Sink Routing in Apache Flink via DemultiplexingSink Last year, I wrote a blog post discussing ideas on how to support use-cases in Apache Flink that required dynamically resolving (and potentially creating) sinks at runtime based on the incoming data. While this solution was fairly practical, it was written against older APIs as opposed leveraging the new Unified
apache flink ai streaming Incremental Optimization in Apache Flink ... with AI While performance always matters, streaming pipelines are where it can matter a lot. Concepts like Big O notation that you may have repressed from your days learning computer science can come back to bite you if neglected (I’m looking at you, quadruply nested loops). Often, the most minimal of
apache flink streaming kotlin Dynamic Sink Routing in Apache Flink Apache Flink makes it incredibly seamless to take your streaming data and throw it exactly where you want it to go. It supports tons of different sources and sinks out of the box to easily plug and play to your ETL'ing hearts desire. But what happens when you
