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Introduction

For all but the biggest workloads, Spark will become the engine of choice for complex, iterative analytic problems. The early sweet spots will be machine learning and streaming analytics.

Highlights

  • Spark will replace MapReduce as the compute engine of choice for most complex, analytic applications – excluding those that cannot fit into available cluster memory resources.
  • Spark’s in-memory processing will bring real-time, operational analytics for big data mainstream. Spark will become a checklist item for enterprises running operational and predictive analytics, and for data transformation and integration. While developers are excited about Spark;s programming simplicity and performance, Spark's biggest impact to the enterprise will be as an embedded engine in commercial off-the-shelf applications and tools.
  • Spark’s inclusion of a machine-learning framework “off the shelf” will likely be the means by which many enterprises start to build it into operational analytics.

Features and Benefits

  • Introduces the enterprise message about the Spark compute engine.
  • Provides business use cases for Spark computing.

Key questions answered

  • How will Spark computing impact the business?
  • How will Spark computing change analytics?
  • What are some of the use cases for Spark computing?

Table of contents

Summary

  • Catalyst
  • Ovum view
  • Key messages
  • Recommendations for enterprises
  • Recommendations for vendors

What is Spark?

  • Spark 101: The basics

How Spark could be used

  • Not just fast, Spark provides better answers to more complex analytic problems
  • Versatility
  • Use cases for Spark
  • Are there alternatives to Spark?

Why the excitement?

  • Productive for geeks, but also extended BI and data transformation for enterprises

The road ahead

  • Reinvention of the dual-tiered analytics market

Appendix

  • Methodology
  • Further reading
  • Author

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