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The breakout use case for big data will be real-time streaming analytics. Machine learning will continue to grow, but in most cases, it will be embedded into applications and services.


  • IoT pushes real-time streaming analytics to the front burner.
  • Most machine learning will be embedded, not custom-developed.
  • Making data science a team sport will become a top priority in 2017.

Features and Benefits

  • Puts into perspective the growth of IoT, real-time streaming analytics, and machine learning.
  • Analyzes why the cloud sharpens Hadoop-Spark co-opetition.
  • Discusses how security and data preparation will drive data lake governance.

Key questions answered

  • What are the major trends for big data in 2017?
  • Why is IoT pushing real-time streaming analytics to the front burner?
  • How will most enterprises benefit from the reality, not the hype, of machine learning?
  • How does the cloud sharpen co-opetition between Hadoop and Spark?
  • What is the big challenge for enterprises hoping to profit from the insights of their data scientists?

Table of contents


  • Catalyst
  • Ovum view
  • Key messages


  • Recommendations for enterprises
  • Recommendations for vendors

Business trends and technology enablers

  • The hot growth area of information management
  • The big elephant and the cloud
  • Where there's smoke, there's fire

Machine learning is the big disruptor

  • It is already there, but you might not realize it
  • Analytic applications embedding machine learning are becoming the norm
  • Surprise: Demand for data scientists is actually soft

Making data science a team sport becomes top priority

  • A disconnect is impeding the work of data scientists
  • Tools rapidly emerging to connect data scientists with the business

IoT pushes real-time streaming analytics to the front burner

  • Hallelujah! Streaming analytics are reborn
  • SQL is the hotspot. Sound familiar?
  • The (re)emergence of PubSub

The cloud sharpens Hadoop-Spark co-opetition

  • Yin and yang, or just yang
  • The cloud sparks Spark/Hadoop realignment

Security, data prep to drive data lake governance

  • Data lakes entered the agenda last year
  • Governance depends on the target audience, use case


  • Methodology
  • Further reading
  • Author

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