Project Summary
Apache Spark is an open source cluster computing system that aims to make data analytics fast — both fast to run and fast to write.
To run programs faster, Spark provides primitives for in-memory cluster computing: your job can load data into memory and query it repeatedly more rapidly than with disk-based systems like Hadoop.
To make programming faster, Spark offers high-level APIs in Scala, Java and Python, letting you manipulate distributed datasets like local collections. You can also use Spark interactively to query big data from the Scala or Python shells.
Spark integrates closely with Hadoop to run inside Hadoop clusters and can access any existing Hadoop data source.
In a Nutshell, Apache Spark...
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has a well established, mature codebase
maintained by a very large development team
with stable Y-O-Y commits -
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took an estimated 471 years of effort (COCOMO model)
starting with its first commit in March, 2010
ending with its most recent commit about 15 hours ago
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Apache License 2.0
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These details are provided for information only. No information here is legal advice and should not be used as such.
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