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Apache Spark started in 2009 as a research project at UC Berkley’s AMPLab, a collaboration involving students, researchers, and faculty, focused on data-intensive application domains. The goal of Spark was to create a new framework, optimized for fast iterative processing like machine learning, and interactive data analysis, while retaining the scalability, and fault tolerance of Hadoop MapReduce. The first paper entitled, “Spark: Cluster Computing with Working Sets” was published in June 2010, and Spark was open sourced under a BSD license. In June, 2013, Spark entered incubation status at the Apache Software Foundation (ASF), and established as an Apache Top-Level Project in February, 2014. Spark can run standalone, on Apache Mesos, or most frequently on Apache Hadoop.
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Today, Spark has become one of the most active projects in the Hadoop ecosystem, with many organizations adopting Spark alongside Hadoop to process big data. In 2017, Spark had 365,000 meetup members, which represents a 5x growth over two years. It has received contribution by more than 1,000 developers from over 200 organizations since 2009.