In recent years, a multitude of new buzzwords, frameworks, and solutions have emerged in the tech industry, with many developers eager to embrace the latest advancements. One such buzzword is Stream Processing, which has been around for some time but is still unfamiliar to many. The purpose of this session is to shed light on the when and why of incorporating Stream Processing into software systems.

Drawing on my experience with Apache Flink, Apache Beam, and Google Dataflow, I will share insights and answer questions such as:
- What is Stream Processing, and how is it different from Event-driven or Event Processing?
- What are the basic concepts, terms, and patterns associated with Stream Processing?
- What are the benefits of using Stream Processing, and what are some successful use cases?
- What tools and frameworks are available for Stream Processing
- What are the costs involved in terms of learning curve, development, deployment, and maintenance?
- How does Stream Processing compare to other solutions, and when is it appropriate to consider it for a particular scale of application?
- Where can one begin learning about Stream Processing?

This session is designed for intermediate to advanced-level participants who are interested in expanding their knowledge of Stream Processing and how it can be applied to their work.