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Building Streaming And Fast Data Applications With Spark Mesos Akka Cassandra And Kafka

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Building Streaming And Fast Data Applications With Spark Mesos Akka Cassandra And Kafka

With Sean Glover

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It’s become clear to many business that the ability to extract real-time actionable insights from data is not only a source of competitive advantage, but also a way to defend their existing business models from disruption. So while legacy models such as nightly batch jobs aren’t disappearing, an era of fast, streaming data (aka “Fast Data”) is upon us, and represents the state of the art for gaining real-time perishable insights that can then be used to serve existing customers better, acquiring new markets and keep the competition at bay.

That said, distributed, Fast Data architectures are much harder to build, and carry their own set of challenges. Enterprises looking to move quickly are presented with a growing ecosystem of technologies, which often delays fast decisions and provides its own set of risks:

  • With so many choices, what tools should you use?
  • How do you avoid making rookie mistakes?
  • e best patterns and practices for streaming applications?

In this webinar with Sean Glover, Senior Consultant at Lightbend and industry veteran, we examine the rise of streaming systems built around Spark, Mesos, Akka, Cassandra and Kafka, their role in handling endless streams of data to gain real-time insights. Sean then reviews how the Lightbend Fast Data Platform (FDP) brings them together in a comprehensive, easy to use, integrated platform, which includes installation, integration, and monitoring tools tuned for various deployment scenarios, plus sample applications.

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The term 'streams' has been getting pretty overloaded recently–it's hard to know where to best use different technologies with streams in the name. In this talk by noted hAkker Konrad Malawski, we'll disambiguate what streams are and what they aren't, taking a deeper look into Akka Streams (the implementation) and Reactive Streams (the standard).

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