Declarative Reasoning with Timelines: The Next Step in Event Processing

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Talk Abstract

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.

Speaker Biography

Ben Chambers is the ML CTO of Kaskada. He is a technology innovator with an extensive background in data processing, machine learning, cloud computing, and software engineering. Ben currently leads the Kaskada open-source project, a modern event-processing system that transforms how event-based data is processed and analyzed. Previously, Ben co-founded Kaskada, a startup specializing in advanced data platforms for machine learning applications. Prior to Kaskada’s acquisition by DataStax, Ben played a pivotal role in driving the company's technical vision and implementing what would become the Kaskada open-source project.

Prior to founding Kaskada, Ben was a software engineer at Google, where he made significant contributions to the development of the Apache Beam programming model, including the implementation of windowing and triggers. This open-source data processing framework introduced the first unified programming model offering a scalable approach to both batch and stream data processing pipelines.