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There were no individual breakout headlines in 2016 to compare with the sudden introduction of Spark in 2015, which overshadowed much of last year. But our 2016 predictions have been borne out. A rising tide has lifted Hadoop (although not yet to profitability for vendors); those who reported revenues cited run rates averaging 40–45% higher year over year. But there are also new, alternative paths to big data, such as Spark-based services in the cloud, that reinforce the idea that big data is not automatically synonymous with Hadoop.

Ovum has also seen machine learning proliferate, from consumer services to enterprise applications and tooling; for instance, machine learning has become table stakes for data preparation and other tools related to managing curation of data for data lakes.

Key 2017 trends

  • Machine learning will be the biggest disruptor for big data analytics in 2017.
  • Making data science a team sport becomes a top priority.
  • IoT pushes real-time streaming analytics to the front burner.
  • The cloud sharpens Hadoop-Spark “co-opetition.”
  • Security and data preparation will drive data lake governance.

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