Real-time analytics systems derive meaningful insights from continuous streams of data, enabling organizations to make swift decisions and react fast. However, not all real-time analytics systems are made equal. While they share the same goal in the end, there are differences in how they achieve it.

This talk aims to classify real-time analytics systems into four main groups based on five characteristics, discuss popular use cases for them, and identify the best technology choice for implementing them in production.

The first half of the talk introduces the five characteristics that you can use to assess any real-time analytics system: data freshness, query latency, concurrency, query complexity, and access to historical data. Then, we classify real-time analytics systems into four groups based on those characteristics and discuss their use cases while taking a fictitious train company as a reference.

The second half of the talk explores the best technology choices to implement for each group, including stream processors, streaming databases, and real-time OLAP databases. Finally, we draw a real-time analytics landscape for the above train company to achieve different analytical needs.

This talk would be a guide for beginner practitioners in the data analytics domain to identify, assess, and find the right technology stack for their real-time analytics use cases.