1 year ago

In the world of academia, the hard problems posed by machine learning revolve around building better, smarter models and finding more and better data. But when it comes to data engineering at data-centric enterprises, building offline models for offline data doesn’t cut it in the use cases that companies face today.

This feature is especially important for real-time AI applications, as their accuracy and efficiency often rely on continuous access to data for mathematical modeling and analysis.

Flexible data modeling: Cassandra’s NoSQL data model is schema-free, which makes it possible to store and query complex and diverse data types common in ML and AI applications.

Kaskada features include: A time-centric computational model: Kaskada assumes you are working with event data and computing stateful values over time for a set of entities.