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How Spark beats MapReduce: Event Streaming, Iterative Algorithms and Elasticity

In my previous post Why Enterprises of different sizes are adopting ‘Fast Data’ with Apache Spark, I gave a quick introduction to how massive petabyte data sets proved to be unmanageable in a cost-effective way with traditional tools, which paved the way for Hadoop and NoSQL databases. Hadoop has traditionally been an environment for batch processing, while NoSQL databases provided some subset of record-oriented CRUD operations. More recently, the need to process event streams has become more important. My Typesafe colleague Jonas Bonér calls this “Fast Data”.

 

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Why Enterprises of different sizes are adopting ‘Fast Data’ with Apache Spark

A couple of weeks ago, Typesafe launched the results of a survey in which over 2000 people were asked about the explosive adoption of Apache Spark. In the Slideshare presentation embedded above, you can see a sneak preview of some of the results of Apache Spark: Preparing for the Next Wave of Reactive Big Databut the full version has a lot more to offer. The Scala community is showing intense interest in Apache Spark as well (according to the report, 88% of Spark users are working in Scala, 44% in Java, 22% in Python). So as resident “Apache Spark guy”, I thought it would be nice to put the popularity of Apache Spark in context, looking at what led us here, how enterprises are reacting, and what the needs of the mid-market really are.

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Spark Survey

@typesafe
December 3, 2014
spark

Back in September, we ran a survey to gather people’s thoughts and upgrade plans around Java 8. We were surprised to find that among the 3,000 respondents, more than 17% are already using Apache Spark in production. Considering how Spark support by the major Hadoop vendors is only about a year old, this number took many by surprise.

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