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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.


  • 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


  • 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


  • Methodology
  • Further reading
  • Author

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