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Hadoop MapReduce or Spark: What if you don’t have to decide now?

2014 was a tipping point for Apache Hadoop: it graduated from being simply a distributed file system and the MapReduce engine for high-performance batch processing to becoming a multi-purpose platform capable of handling a wide variety of workloads including machine learning, social graph analysis, interactive queries, real-time data processing, and much more.

One of the primary reasons that Hadoop is such a disruptive technology is that it provides highly scalable storage and data processing capabilities at price points that are orders of magnitude lower than legacy systems. Consistent with Moore’s Law, performance and cost improvements have made mobile devices, connected consumer electronics, and the Internet pervasive in every aspect of our lives, dramatically increasing the amount of generated data that needs to be analyzed.

Cloudera EDH and Syncsort

Read my entire blog on Cloudera Vision and learn about our innovations to enhance Apache Spark’s value proposition and our Hadoop products that are part of Cloudera’s Accelerator Program for Spark.

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