8.2 Implementing Streaming Systems

In this section

• Apache Kafka Streams: Building Real-Time Stream Processing Applications
  Explore Apache Kafka Streams, a powerful client library for building real-time, scalable, and fault-tolerant stream processing applications within the Kafka ecosystem. Learn about setting up Kafka Streams, defining stream processing topologies, and implementing stateful operations with practical examples.
• Apache Flink: High-Throughput Stream Processing for Event-Driven Architectures
  Explore Apache Flink, an open-source stream processing framework for high-throughput, low-latency data processing, with support for event time and stateful computations. Learn about its setup, programming model, and robust features for building scalable event-driven systems.
• Comparing Streaming Frameworks: Apache Kafka Streams vs. Apache Flink and More
  Explore the key criteria for comparing streaming frameworks, including Apache Kafka Streams, Apache Flink, and others, focusing on performance, scalability, and ease of use.