WEBINAR REPLAY |

Moving from Big Data to Fast Data? Here's How To Pick The Right Streaming Engine

With Dean Wampler, Ph.D., VP of Fast Data Engineering at Lightbend, Inc.

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 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 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.



WEBINAR REPLAY |

Moving from Big Data to Fast Data? Here's How To Pick The Right Streaming Engine

With Dean Wampler, Ph.D., VP of Fast Data Engineering at Lightbend, Inc. and Hugh McKee, Global Solutions Architect at Lightbend, Inc.

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 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 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.



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About Presenter

Dean Wampler, Ph.D., VP of Fast Data Engineering at Lightbend, Inc.

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.

About Presenters

Dean Wampler, Ph.D., VP of Fast Data Engineering at Lightbend, Inc.

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.

Hugh McKee, Global Solutions Architect at Lightbend, Inc.

Hugh is a solutions architect at Lightbend. He’s had a long career building applications that evolved slowly, that inefficiently utilized their infrastructure, and that were brittle and prone to failure. That all changed when he started building reactive, asynchronous, actor-based systems. This radically new way of building applications rocked his world. As an added benefit, building application systems became way more fun that it had ever been. Now he is focused on helping others to discover the significant advantages and joys of building responsive, resilient, elastic, message-based applications.

About Lightbend

Lightbend (Twitter: @Lightbend) is dedicated to helping developers build Reactive applications on the JVM. With the Lightbend Reactive Platform, developers can create message-driven applications that scale on multicore and cloud computing architectures by using projects like Lagom, Play Framework, Akka, Scala, Java, and Apache Spark. To help our customers succeed, Lightbend partners with technology pioneers such as Databricks, IBM, and Mesosphere.