As data increasingly becomes the backbone of business, having a resilient distributed data store becomes a necessity. Over the past few years, Apache's Kafka has risen to the top as the most used real-time and scalable messaging system in most businesses. When my company began it's journey into Event Streaming with Kafka a few years ago, I was amazed at the simplicity of the API's and how quickly I could develop simple applications for producing and consuming data. But as our company began investing more into Event Streaming and Event Driven architectures, the need to go beyond the basics quickly became paramount. Once our apps were deployed into the real world with both good and bad data, head-scratching issues began to arise due to our lack of understanding of Kafka's message management. I quickly became inundated with questions from app teams I supported like:
"How can I retry a message that fails to process?"
"How do I guarantee I process messages in the correct order?"
"What can I do to speed up data processing?"
or my favorite...
"I'm stuck on a bad message, what do I do!?"

In this session, not only will these questions be answered, but we'll share the tools and techniques, used to put the power of understanding in the hands of the developer so you can have a clear picture into your event streaming applications.
This session will use live demos to to go beyond the basics to teach the skills and tools you can use locally to help build robust consumers and producers before you deploy.
Using Spring and Kafka libraries, this session will highlight various settings on consumers and providers to optimize the way our data is streamed and read. Using a local Kafka cluster running in a container, we will demonstrate how to see how your code changes impact your committed offsets. Come and learn how to pull back the curtain on Kafka and gain the critical knowledge to master your own Event Streaming systems.