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Introduction

While a rising tide will continue lifting big data investment, implementation paths for the next wave of adopters must be simpler. And while SQL reigns supreme as the onramp to big data analytics, Spark will become the fastest-growing workload.

Highlights

  • A rising tide of IT spending will continue lifting enterprise investment in big data analytics.
  • Appliance and cloud will drive the next wave of Hadoop adoption.
  • SQL still reigns supreme for big data analytics, but Spark will be the fastest-growing workload.

Features and Benefits

  • Discusses why the next wave of adopters will require simpler implementation paths.
  • Digs beneath the surface to chart emerging trends with machine learning and data lake adoption.

Key questions answered

  • What are the key trends for big data analytics in 2016?
  • Why will cloud and appliances prove important for the next wave of Hadoop adopters?
  • What will be the impact of machine learning on big data analytics?

Table of contents

Summary

  • Catalyst
  • Ovum view
  • Key messages
  • Recommendations

Appliance and cloud will drive next wave of Hadoop adoption

  • The wake-up call
  • The next frontier is organizations with less sophisticated IT operations
  • Growth of cloud underscores our predictions

…Spark becomes the fastest growing big data analytics workload

  • Spark won't displace Hadoop
  • Look for the business message
  • Spark technology roadmap for 2016

Machine learning becomes a checklist feature for smart analytics and data curation tools

  • Growing market presence
  • Data curation/data wrangling is the sweet spot
  • Machine learning still an evolving art

Appendix

  • Methodology
  • Further reading
  • Author

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