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Streaming analytics isn't new, but a perfect storm of use cases, infrastructure, and technology is coming together to put this form of processing on the front burner for organizations across a wide range of vertical industry segments.


  • Business use cases centered on mobile data and IoT are finally expanding the addressable market for streaming analytics beyond traditional markets for CEP, such as capital markets and national security.
  • Open source compute engines – especially Spark Streaming and Storm – are expanding the practitioner base beyond what was available with older proprietary event-processing tools.
  • Machine learning will, in the long run, be a powerful approach that makes streaming analytics more flexible and adaptive compared to traditional rules-based approaches.

Features and Benefits

  • Analyzes how open source will lower barriers to entry, but will not eliminate the need for traditional rules-based solutions.
  • Discusses the appeal of open source to developers and data scientists that are embracing programmatic analytics – and how this will change as packaged software emerges.

Key questions answered

  • What are the use cases that are raising the urgency for enterprises to consider implementing streaming analytics solutions?
  • Why will open source and commodity infrastructure enable streaming analytics to break out to a wider market compared to that for CEP?
  • What is the state of commercial readiness of streaming analytics solutions?

Table of contents


  • Catalyst
  • Ovum view
  • Key messages


  • Recommendations for enterprises
  • Recommendations for vendors

Why is streaming hot again?

  • Disruptors
  • Use cases
  • Infrastructure price/performance
  • Open source
  • Cloud deployment
  • Machine learning

Preview: The streaming market landscape

  • The king is dead! Long live the king!
  • Established solutions are reinventing themselves for scale
  • The open source story is just beginning


  • Methodology
  • Further reading
  • Author

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