Mastering Real-Time Data Streams with Apache Kafka for IoT and Financial Applications
Introduction to Real-Time Stream Processing Real-time stream processing is a critical component in modern data architectures, enabling applications to process and analyze continuous data streams with minimal latency. Unlike batch processing, which handles data in fixed-size chunks, stream processing deals with data as it arrives, making it ideal for time-sensitive applications like Internet of Things (IoT) and financial systems. Apache Kafka, a distributed streaming platform, has emerged as a leading solution for building robust, scalable, and fault-tolerant stream processing pipelines. This chapter explores the fundamentals of real-time stream processing with Apache Kafka, focusing on its application in IoT and finance. We’ll cover Kafka’s architecture, core components, and practical use cases, along with code examples and best practices for building efficient streaming applications. Understanding Apache Kafka Apache Kafka is an open-source distributed event streaming platfor...