In the last decade, advanced data analytics, machine learning, and AI have spurred the development of technologies like Apache Spark, Apache Flink, and KSQL for processing streaming data. Unfortunately, streaming queries remain difficult to create, comprehend, and maintain - you’re forced to either use complex low-level API’s or work around the limitations of query languages like SQL that were designed to solve a very different problem. To tackle these challenges, we introduce Kaskada - a modern event processor built on a new query language for time-based data, built on the idea of a timeline.
Timelines organize data by time and entity, offering an ideal structure for event-based data. We designed the Kaskada system around this abstraction, providing a streamlined, composable query language for expressing temporal queries on event-based data and efficient query execution and optimization.
Join us to see how Kaskada’s temporal queries allow you to finally understand your real-time data. We explore the timeline concept and show how it improves existing approaches such as streaming-SQL and how timelines solve a wide range of real-world problems.
