In this post, I want to share examples of how watsonx can enhance the event processing flows you create using IBM Event Processing.
I’ll start by describing how Event Processing and watsonx complement each other.
Then I’ll share a couple of simple examples of what this looks like in action.
Finally, I’ll walkthrough how I built the example flows to show you how you can try doing something like this for yourself, and share tips for how to create flows like this.
Written with Chris Patmore
IBM Event Automation helps companies to accelerate their event-driven projects wherever businesses are on their journey. It provides multiple components (Event Streams, Event Endpoint Management, and Event Processing) which together lay the foundation of an event-driven architecture that can unlock the value of the streams of events that businesses have.
A key goal of Event Automation is to be composable. The three components can be used together, or they can each be used to extend and enhance an existing event-driven deployment.
Amazon MSK (Managed Streaming for Kafka) is a hosted, managed Kafka service available in Amazon Web Services. If a business has started their event-driven journey using MSK, then components from Event Automation can help to enhance this. This could be by offering management and governance of their MSK topics. And it could be by providing an intuitive low-code authoring canvas to process the events on their MSK topics.
Working with Amazon MSK is a nice example of the benefits of the composability of Event Automation, by helping businesses to get more value from their existing MSK topics.
In this blog post, we want to show a few different examples of where this can be done. For each example, we’ll provide a high-level diagram and description. We’ll also share a demonstration that we created to show it in action.
Event Processing (one of the new capabilities of IBM Event Automation) makes it easy to perform stateful processing of streams of events from Kafka topics. In this post, I want to drill down a little into how windowed processing behaves.
I’ve enjoyed the chance to see the solutions that people have started to build with the tool. As part of this, I’ve been helping several people to understand the results produced by the event processing flows they’ve made.
These often started with different questions, such as:
However, these are often symptoms of a single common question: how windowed processing operations behave.
I’ve tried to come up with simple ways to demonstrate how it works, so in this post I want to share how I’m currently explaining it.