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Introduction

The cloud, artificial intelligence/machine learning, and the processing of real-time data will drive major changes to the big data landscape in 2018.

Highlights

  • This report analyzes the evolving approaches to managing data lakes.
  • This report looks at new approaches to processing real-time data that will make data lakes more responsive and efficient

Features and Benefits

  • Discusses the need to approach cloud big data deployments as platform decisions and how that will impact the way enterprises choose cloud platform providers.
  • Looks at the make-versus-buy decision for AI capabilities and why most enterprises will rely on the "buy" option for applications and tools that embed machine learning.
  • Analyzes the significance of the new data pipeline products and services that are becoming available.

Key questions answered

  • What can enterprises implementing data lakes learn from past experience with enterprise data warehouses?
  • How can enterprises manage expectations, from executive to working level, on how to benefit from AI?
  • Why is there a growing ecosystem around data pipeline tools and services, and how will that impact how organizations master the processing of real-time data?

Table of contents

Summary

  • Catalyst
  • Ovum view
  • Key messages

Recommendations

  • Recommendations for enterprises
  • Recommendations for vendors

Data lakes are going virtual

  • Data lakes transcend Hadoop
  • Who owns the query?
  • Cloud object storage – the exception that proves the virtual data lake rule

Multicloud and hybrid cloud strategies become front-burner issues for big data adoption

  • Cloud platform decisions are making the issue strategic
  • Hybrid cloud becomes key criterion for big data cloud implementation

Cloud object storage is making Hadoop "disappear"

  • The cloud reimagines Hadoop …
  • … and it drives Hadoop vendors to reimagine their products

Data pipelines shift the center of gravity for managing real-time processes

  • What are data pipelines?
  • Adding integration to streaming analytics
  • Why data pipelining?

Machine learning is becoming a pillar of big data platforms and analytics

  • ML and AI are on their way to becoming product features
  • Make vs. buy AI? In 2018, more enterprises will buy
  • Demand for machine learning capabilities outshines data science

Appendix

  • Methodology
  • Further reading
  • Author

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