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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.

Tags

apache bigdata cluster clustercomputing distributed distributed_computing ec2 graph_computing hadoop hdfs in_memory java machine_learning mapreduce ml python scala sql streaming streamingdata

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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Project Security

Vulnerabilities per Version ( last 10 releases )

Project Vulnerability Report

Security Confidence Index

Poor security track-record
Favorable security track-record

Vulnerability Exposure Index

Many reported vulnerabilities
Few reported vulnerabilities

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About Project Security

Languages

Languages?height=75&width=75
Scala
69%
Java
15%
Python
8%
11 Other
8%

30 Day Summary

Sep 13 2018 — Oct 13 2018

12 Month Summary

Oct 13 2017 — Oct 13 2018
  • 4665 Commits
    Down -871 (15%) from previous 12 months
  • 381 Contributors
    Down -48 (11%) from previous 12 months

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