Streaming analytics may include a wide variety of data sources, such as telemetry from connected devices, log files generated by customers using web applications, ecommerce transactions, or information from social networks or geospatial services. It’s often used for real-time aggregation and correlation, filtering, or sampling.

Data traditionally is moved in batches. Batch processing often processes large volumes of data at the same time, with long periods of latency. For example, a process may be run every 24 hours. While this can be an efficient way to handle large volumes of data, it doesn’t work with time-sensitive data that’s meant to be streamed, because that data can be stale by the time it’s processed.