Fast Data Architectures for Streaming Applications
Explore fast data architectures for streaming applications in this 45-minute conference talk from GOTO Chicago 2017. Dive into the evolution of Big Data from batch-oriented to stream-oriented processing, focusing on architecture patterns, tools, and techniques for real-time data analysis. Learn about trade-offs guiding design decisions, challenges in stream analytics, major streaming data tools, and integration strategies. Discover how to handle late-arriving data, perform machine learning model training, and address high-scale problems through partitioning. Compare streaming engines like Spark, Kafka, and Flink, and understand the relationship between microservices and fast data architectures. Gain insights into reactive programming, merging streams, and the differences between batch and stream processing in modern Big Data ecosystems.