Streaming analytics (or fast data) is becoming an increasingly popular subject in enterprise organizations because customers want to have real-time experiences, such as notifications and advice based on their online behavior and other users’ actions. A typical streaming analytics solution follows a “pipes and filters” pattern that consists of three main steps: detecting patterns on raw event data (complex event processing), evaluating the outcomes with the aid of business rules and machine learning algorithms, and deciding on the next action.
In this talk, Bas will explore the architecture challenges that arise when dealing with streaming data, such as latency issues, event time versus server time, and exactly once processing. He provides architectural diagrams, explanations, a demo, and the source code. The solution (“Styx”) is open source and available on GitHub.
Bas is a technology leader in the AI and big data domain. His academic background is in Artificial Intelligence and Informatics. Trained as a software engineer and architect, he has 15 years experience in delivering successful data-driven projects with a wide range of companies and technologies. He occasionally teaches programming courses and is a regular speaker on conferences and informal meetin...