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Introduction

Spark will become the engine of choice for complex, iterative analytic problems. Spark excites developers today, but Ovum believes its biggest impact will ultimately be as an embedded engine inside packaged applications that powers real-time, operational analytics.

Highlights

  • Spark will replace MapReduce as the compute engine of choice for complex, analytic applications that can fully exploit cluster memory resources.
  • Spark’s in-memory processing will bring real-time, operational analytics for big data to the mainstream.
  • Spark is undergoing routine teething issues (common to any rapidly maturing technology) that should not prevent deployment.

Features and Benefits

  • Provides the technology deep dive on Spark.
  • Discusses the extensibility of Spark.
  • Provides a sample listing of the emerging commercial vendor ecosystem based on Spark technology.
  • Analyzes the readiness of Spark and the roadmap capabilities that are essential for Spark to become a foundation of operational, predictive, and prescriptive analytics.

Key questions answered

  • What is Spark technology and how does it differ from MapReduce?
  • Why is Spark attractive to developers? What is the key to its programming simplicity?
  • What is the key to Spark's versatility and how will that impact the development and implementation of analytics programs and commercial software?
  • What is the future for Spark, and what are the essential items on the technology roadmap for Spark to mature?

Table of contents

Summary

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

What is Spark?

  • Spark 101: The basics
  • Spark alternatives for big data analytics

Why the excitement?

  • Productive for geeks, but also extended BI and data transformation for enterprises
  • It's fast
  • A simpler programming model
  • Better fault tolerance
  • Extensibility

Market landscape

  • The role of Databricks
  • IBM's emerging role
  • Third-party support

The road ahead

  • Reinvention of the dual-tiered analytics market
  • Technology roadmap and issues

Appendix

  • Methodology
  • Further reading
  • Author

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