In January 2024, Gunnar Morling came out with a challenge: what is the fastest way to process one billion rows in Java? Java is great but what if you used columnar data structures and fast in-memory analytics instead?

The One Billion Row Challenge is well suited for columnar in-memory analytics in general, and for Oracle In-Memory in particular. What would be a better reason to talk about the columnar data structures, memory-optimised processing, and Oracle In-Memory? By the end of this presentation, we will have an answer to the question: what is the fastest way to aggregate over one billion rows?

This presentation briefly reviews some of the more popular columnar in-memory analytics data engines and what toolbox your friendly data scientist might use in Python. How do Apache Arrow and Polars compare to the Oracle In-Memory columnar store? We will discuss in-memory processing, SIMD and real-time analytics in the Oracle database, and outside of it.

This is an introductory session about in-memory analytics and Oracle In-Memory. Preferable session duration is 45 minutes.