In the world of “big data”, businesses that can quickly discover and act upon insights from their users’ events have a decisive advantage. It is no longer sufficient for analytics systems to solely rely on daily batch processing. This is why our new column store, Nova, continues to use a lambda architecture. In addition to a batch layer, this architecture also has a real-time layer that processes event data as they come in, and the real-time layer only needs to maintain the last day’s events. In a previous post, we focused on the batch layer of Nova. Designing the real-time layer to support incremental updates for a column store creates a different set of requirements and challenges. We will discuss our approach in this post.