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Introduction

This report provides an overview and introduction to modern real-time streaming analytics technologies.

Highlights

  • Use cases and the lowering of technology barriers are making streaming analytics more accessible compared to the early days of CEP.
  • Not all streaming engines are the same; differences in technology and architecture impact the analysis of real-time flowing data in terms of speed, reliability, security, and manageability.
  • While IT should understand the architectural differences of different streaming engines, system selection should be made based on how the capabilities of those engines support the specific business use case.

Features and Benefits

  • Updates our 2012 fast data research, mapping the discussion to the streaming analytics engines that are emerging today.
  • Analyzes how modern streaming analytics technologies are descended from CEP -- and more importantly, how they differ.

Key questions answered

  • What is streaming analytics? What do streaming analytics engines do?
  • How do modern streaming analytics engines differ from the CEP technologies that emerged in the 1990s?
  • What is the impact of big data innovations, such as commodity scale-out infrastructure and machine learning, on streaming analytics technology?
  • What is the difference between streaming and microbatch?

Table of contents

Summary

  • Catalyst
  • Ovum view
  • Key messages

Recommendations

  • Recommendations for enterprises
  • Recommendations for vendors

What is fast data and what is streaming analytics?

  • Fast data – a recap
  • What does streaming analytics do?

What streaming analytics is not

  • It is not an alert system
  • It is not a counting, averaging, or summing system
  • It is not a file-scanning system
  • The solutions are not new
  • What's changed?

Semantics

  • Terminology can be misleading
  • Is it streaming or event processing?
  • Is it streaming or microbatch?
  • Reliability

Appendix

  • Methodology
  • Further reading
  • Author

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