Explore strategies and techniques for managing out-of-order events in event-driven systems, ensuring accurate processing and maintaining system integrity.
Explore the implementation of real-time data processing in logistics and supply chain optimization using stream processing frameworks, IoT integration, and event-driven architectures.
A comprehensive guide to implementing an Event-Driven Architecture system using Kafka, Java, and modern tools. Learn to set up event brokers, develop producers and consumers, and deploy with Infrastructure as Code.
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.
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.
Explore the key criteria for comparing streaming frameworks, including Apache Kafka Streams, Apache Flink, and others, focusing on performance, scalability, and ease of use.
Explore the differences between stateful and stateless processing in stream architectures, including use cases, advantages, implementation considerations, and best practices.
Explore the intricacies of windowing and aggregations in stream processing, including types of windows, implementation strategies, and practical examples using Apache Flink.
Explore the critical differences between event time and processing time in stream processing, their advantages, trade-offs, and implementation in frameworks like Apache Flink and Kafka Streams.
Explore the architecture and components of Apache Kafka, including producers, brokers, topics, partitions, consumers, and more, to understand how Kafka enables scalable and resilient event-driven systems.
Explore Kafka Streams and kSQL for scalable, real-time stream processing and analytics in Apache Kafka. Learn about key features, implementation, and best practices.
Explore the use cases and best practices for implementing Apache Kafka in event-driven architectures, including real-time analytics, microservices communication, log aggregation, stream processing, and fraud detection.