The project that we developed in this chapter is an example for streaming analytics. The incoming tweet stream is processed to compute aggregates which are visualized in real time with a Grafana dashboard. It shows how continuously generated data (in this case, tweets) can be analyzed and used to generate immediate insights. We have seen how existing building blocks from the Apex library (connectors, windowing) are used to accelerate application development and how integration with other infrastructure for data visualization can be accomplished.
The pipeline pattern is broadly applicable. Similar to the introductory ad-tech use case, it can be applied to other domains with data streams such as mobile, sensor, or financial transaction data. Instead of simple functionality (top words and counters), real-world applications may perform sentiment analysis, fraud detection, device health monitoring, and other complex processing.
The next chapter will go into more depth with windowing and...
