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Description:

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 to choose from. But which ones are the best fit for your organization? It helps to consider several factors in the context of your applications:

• Low latency: How low is necessary?
• High volume: How high is required?
• Integration with other tools: Which ones and how?
• Data processing: What kinds? In bulk? As individual events?

In this webcast, Dean Wampler walks you through the criteria you need to consider when selecting technologies. You'll dive into 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.

About Dean Wampler, VP of Fast Data Engineering at Lightbend.

Dean Wampler, Ph.D., is VP of Fast Data Engineering at Lightbend. He uses Scala and Functional Programming to build Big Data systems using Spark, Mesos, Hadoop, the Lightbend Reactive Platform, and other tools. Dean is the author or co-author of three O'Reilly books on Scala, Functional Programming, and Hive. He contributes to several open source projects (including Spark) and he co-organizes and speaks at many technology conferences and Chicago-based user groups. He has B.S. and M.S. degrees in Physics from the University of Virginia and a Ph.D. in Theoretical Physics from the University of Washington.