For many businesses, the batch-oriented architecture of Big Data–where data is captured in large, scalable stores, then processed later–is simply too slow: a new breed of “Fast Data” architectures has evolved to be stream-oriented, where data is processed as it arrives, providing businesses with a competitive advantage.
There are many stream processing tools, so which ones should you choose? It helps to consider several factors in the context of your applications:
* Low latency: How low (or high) is needed?
* High volume: How much volume must be handled?
* Integration with other tools: Which ones and how?
* Data processing: What kinds? In bulk? As individual events?
In this talk by Dean Wampler, PhD., VP of Fast Data Engineering at Lightbend, we’ll look at the criteria you need to consider when selecting technologies, plus specific examples of how four streaming tools–Akka Streams, Kafka Streams, Apache Flink and Apache Spark serve particular needs and use cases when working with continuous streams of data.