At the heart of modern data processing lies events. Events describe the roughest, most complete picture available of what has happened in the world, and practically every form of data processing ultimately begins with events.
While the power of event processing has increased since the emergence of streaming data processing, current systems are still difficult to use when working on problems that deal with time and order, such as predictive AI/ML. Handling these problems requires a new kind of query language - a way to declaratively reason about events over time.
In this talk, we introduce the concept of timelines. Timelines are an intuitive abstraction for reasoning about temporal values. They support a broad range of useful operations which can be efficiently computed at scale. We will demonstrate the power and differentiation of timelines:
- How timelines allow declarative queries over events and time in a simple and intuitive manner
- Why timelines are ideal for applications such as behavioral predictions, trend analysis, and forecasting, and how existing solutions such as streaming SQL fall short.
- How to execute timeline based queries using the open-source Kaskada event-processing engine.