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For Application Development & Delivery Professionals

Apache Spark Is Powerful And Promising

Spark's Primary Processing Prowess Is Exponential When Compared To Hadoop's MapReduce

February 13, 2015

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Why Read This Report

Apache Spark is an open source cluster computing platform designed to process big data as efficiently as possible. Sound familiar? That's what Hadoop is designed to do. However, these are distinctly different, but complementary, platforms. Hadoop is designed to process large volumes of data that lives in an Hadoop distributed file system (HDFS). Spark is also designed to process large volumes of data, but much more efficiently than MapReduce, in part, by caching data in-memory. But, to say that Spark is just an in-memory data processing platform is a gross oversimplification and a common misconception. It also has a unique development framework that simplifies the development and efficiency of data processing jobs. You'll often hear Hadoop and Spark mentioned in the same breath. That's because, although they are independent platforms in their own right, they have an evolving, symbiotic relationship. Application development and delivery professionals (AD&D) must understand the key differences and synergies between this next-generation cluster-computing power couple to make informed decisions about their big data strategy and investments.

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Table of Contents

  • Spark Is Designed For Speed
  • recommendations

  • Add Spark To Hadoop
  • WHAT IT MEANS

  • Spark's Marriage To Hadoop Will Be Bigger Than Kim And Kanye
  • Supplemental Material

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