Explore strategies and techniques for managing out-of-order events in event-driven systems, ensuring accurate processing and maintaining system integrity.
Explore strategies for managing high-volume IoT events using event-driven architecture patterns, including efficient event ingestion, partitioning, data serialization, and real-time processing.
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 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.