Streaming and near-real-time messaging with Kafka and ScyllaDB

The blog post explores a financial use case involving the integration of Apache Kafka and ScyllaDB for streaming and near-real-time messaging to track stock prices. It describes a system where stock price updates are pushed to a Kafka queue and then consumed by subscribers interested in specific companies, with the consumed messages being stored in ScyllaDB for future analysis. The post explains the data modeling approach using Kaggle's New York Stock Exchange dataset to simulate real-time messaging and outlines the data pipeline stages, including parsing data, defining Kafka topics, simulating message streaming and consumption, and using ScyllaDB for data storage. It also provides details on the software setup prerequisites, such as Java, Maven, Kafka, Zookeeper, and the Cassandra Java core driver, and includes links to code samples and configuration files to help readers build and run the described system. The post emphasizes the importance of speed, reliability, and scalability in handling financial data, highlighting the performance benefits of using Kafka and ScyllaDB.