Real-Time OLAP Databases and Streaming Databases: A Comparison

The modern real-time data stack has gained significant popularity in recent times. Systems such as Apache Pinot, Apache Druid, ClickHouse, RisingWave, and Apache Flink have been widely adopted in companies' data stacks. However, there are still some areas of confusion when it comes to this emerging space. During my conversations with customers, I frequently encounter the following questions:

* What is the difference between stream processing and real-time OLAP? * Why do I need stream processing if I'm already using an OLAP store? * Are RisingWave/Flink competitors to Pinot/Druid/ClickHouse?

These are excellent questions, and I enjoy personally explaining my perspective on the modern real-time data stack to each individual. However, in order to reach a wider audience interested in data engineering, I have written a blog post on this topic.

In summary, stream processing and real-time OLAP databases differ significantly in their design, implementation, and use cases. Stream processing is better suited for monitoring, alerting, and automation scenarios, while real-time OLAP is more suitable for interactive and exploratory analytics. Incorporating both types of systems in your data stack may be beneficial for your overall data processing needs!