Tech Talk: Stream Processing in Java

This video discuss three focal points of modern streaming stacks: time, connectivity and scale.

About this course

You want to collect metrics across your infrastructure, aggregate it and trigger an alert if the performance drops. In milliseconds. Which tools would you consider? The ELK stack? A timeseries database? A log aggregator? There is no wrong answer. Fundamentally, what you really need is a query engine tuned to work with an append-only table - a stream processor. Stream processors are building blocks for applications that run computations driven by the streams of data. We're going to discuss three focal points of modern streaming stacks: time, connectivity and scale. The code samples used during the talk are build using Hazelcast Jet framework.

About this course

You want to collect metrics across your infrastructure, aggregate it and trigger an alert if the performance drops. In milliseconds. Which tools would you consider? The ELK stack? A timeseries database? A log aggregator? There is no wrong answer. Fundamentally, what you really need is a query engine tuned to work with an append-only table - a stream processor. Stream processors are building blocks for applications that run computations driven by the streams of data. We're going to discuss three focal points of modern streaming stacks: time, connectivity and scale. The code samples used during the talk are build using Hazelcast Jet framework.