In this post, I’ll share examples of how writing user-defined functions (UDFs) extends what is possible using built-in Flink SQL functions alone.
I’ll share examples of how UDFs can:
Archive for March, 2026
Extending Flink SQL
Sunday, March 29th, 2026Talking at TechCon about AI and EDA
Sunday, March 22nd, 2026IBM TechCon is an annual online technical event for engineers, creators, and integration specialists.
One of our sessions for this year was AI patterns in event-driven architectures:
You already have Kafka topics sending valuable event data through your systems. You’ve heard about the increasing adoption and promise of AI technologies. But how do these worlds overlap?
We’ll explain the ways that you can take your Kafka topics and use them not just for integration and analytics, but also to drive AI and ML — things like real-time anomaly detection, prediction models, personalization, decision pipelines, and more.
In this session, we’ll explain the four main patterns for how your existing Kafka topics (and the streams of events on them) can be leveraged as the foundation for AI/ML. We’ll show multiple technology approaches to implement each pattern – rather than convince you to use any single specific tool, help you understand the high level patterns, and how can you get started with them.
session recording on video.ibm.com
This was adapted from a talk I gave at Current last year.
I got to go to a fancy convention centre in New Orleans for that talk, but I gave this one from a poorly-lit meeting room in my office… so in that respect at least, this one was less fun! 😉
But it’s a topic I find super interesting, so I am always pleased to have another chance to share my thoughts.
Talking at TechCon about metrics and monitoring
Saturday, March 21st, 2026IBM TechCon is an annual online technical event for engineers, creators, and integration specialists.
One of our sessions for this year was an introduction to Monitoring your Event Driven Architecture:
This session will give you an insight into the life of an Event Automation administrator, responsible for a busy event-driven system where teams have been creating a variety of Kafka topics, integrations, stream processing apps, connectors, and much more. We’ll highlight the importance of metrics and monitoring for event driven architectures and introduce you to the tools that are available to help.
We’ll do this by showing you an event-driven environment where things have gotten out of hand. In our fictional scenario, users are being impacted by things like poorly configured topics, poorly written applications, poorly managed connectors, poorly configured stream processors…
In this session, we’ll walk you through to bring control to the chaos. We’ll step through how to get an insight into what is happening, find out where the problems are, and put controls in place to mitigate their impact.
session recording on video.ibm.com
It was an introduction for beginners, that you could sum up as a 40-minute plea for people to monitor their Kafka clusters and applications! Essentially, we set up a handful of naive and broken applications, and walked through how metrics and monitoring show you where the problems are hiding.
Watch it to be persuaded that metrics are important.
Or to watch how Matt had to jump in and help me when an Apple Magic Mouse decide it didn’t like scrolling any more, and I needed to get to things at the bottom of web pages!
Or just to marvel at how glamourous our offices are. 😉
Deploying Apache Flink jobs into Kubernetes
Friday, March 20th, 2026IBM TechCon is an annual online technical event for engineers, creators, and integration specialists.
One of our sessions for this year was Deploying an Apache Flink job into production:
You’ve maybe seen the low-code canvas in Event Processing or the simple expressiveness of Flink SQL, and how easy they make it to author event stream processing. A business user who understands the data in the event stream can easily describe the patterns they’re interested in or the insights they want to look for. But what comes next?
In this session, we’ll walk through the ops tasks involved in taking that event processing flow, and deploying it into Kubernetes as a Flink application ready for production.
We’ll outline the steps that are needed and describe the main decisions you need to make. This includes the sorts of values you will want to monitor to make sure that your Flink application continues to run correctly.
It was a live walk-through of the steps involved in deploying Flink jobs in Kubernetes. I used Event Processing to create the Flink job that I used for the demos, because low-code UI’s are easier to follow in a presentation, but most of what I showed is applicable however you’ve created your Flink job – and was a high-level introduction to using the Flink Kubernetes Operator.