Apache Spark is an open-source engine developed specifically for handling large-scale data processing and analytics. Spark offers the ability to access data in a variety of sources, including Hadoop Distributed File System (HDFS), OpenStack Swift, Amazon S3 and Cassandra.
Apache Spark is designed to accelerate analytics on Hadoop while providing a complete suite of complementary tools that include a fully-featured machine learning library (MLlib), a graph processing engine (GraphX) and stream processing.
Apache Spark originated at UC Berkeley’s AMPLab in 2009 and was donated in 2013 to the Apache Software Foundation, where it has become the most active project in terms of contributions.
One of the key reasons behind Apache Spark’s popularity, both with developers and in enterprises, is its speed and efficiency. Spark runs programs in memory up to 100 times faster than Hadoop MapReduce and up to 10 times faster on disk. Spark is natively designed to run in-memory, enabling it to support iterative analysis and more rapid, less expensive data crunching.
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