AATD doesn’t currently have real-time insight into the number of orders being placed or the revenue being generated.
The company would like to know if there are spikes or dips in the numbers of orders so that it can react more quickly in the operations part of the business.
The AATD engineering team is already familiar with Kafka Streams from other applications that they’ve built, so we’re going to create a Kafka Streams app that exposes an HTTP endpoint showing recent orders and revenue.
We’ll build this app with the Quarkus framework, starting with a naive version. Then we’ll apply some optimizations. We’ll conclude with a summary of the limitations of using a stream processor to query streaming data.
Figure 4-1 shows what we’ll be building in this chapter.
Figure 4-1. Kafka Streams architecture
What Is Kafka Streams?
Kafka Streams is a library for building streaming applications that transform input Kafka topics into output Kafka topics.
It is an example of the stream processor component of the real-time analytics stack described in Chapter 2.
Kafka Streams is often used for joining, filtering, and transforming streams, but in this chapter we’re going to use it to query an existing stream.
At the heart of a Kafka Streams application is a topology, which defines the stream processing logic of the application. A topology describes ...
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