In this tutorial, developers, solution architects, and data engineers can learn how to build high-performance, scalable, and fault-tolerant applications that react to real-time data using Kafka and Hazelcast.
We will be using Wikimedia as a real-time data source. Wikimedia provides various streams and APIs (Application Programming Interfaces) to access real-time data about edits and changes made to their projects. For example, this source provides a continuous stream of updates on recent changes, such as new edits or additions to Wikipedia articles. Developers and solution architects often use such streams to monitor and analyze the activity on Wikimedia projects in real-time or to build applications that rely on this data, like this tutorial. Kafka is great for event streaming architectures, continuous data integration (ETL), and messaging systems of record (database). Hazelcast is a unified real-time stream data platform that enables instant action on data in motion by combining stream processing and a fast data store for low-latency querying, aggregation, and stateful computation against event streams and traditional data sources. It allows you to build resource-efficient, real-time applications quickly. You can deploy it at any scale from small edge devices to a large cluster of cloud instances.