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=== History of Apache Spark === | === History of Apache Spark === | ||
| − | 2009: a distributed system framework initiated at UC Berkeley AMPLab by MateiZaharia | + | 2009: a distributed system framework initiated at UC Berkeley AMPLab by MateiZaharia <br /> |
| − | 2010: Open sourced under a BSD license | + | 2010: Open sourced under a BSD license <br /> |
| − | 2013: The project was donated to the Apache Software Foundation and the license was changed to Apache 2.0 | + | 2013: The project was donated to the Apache Software Foundation and the license was changed to Apache 2.0 <br /> |
| − | 2014: Became an Apache Top-Level Project. Used by Databricks to set a world record in large-scale sorting in November | + | 2014: Became an Apache Top-Level Project. Used by Databricks to set a world record in large-scale sorting in November <br /> |
| − | 2014-present: Exists as a next generation real-time and batch processing framework | + | 2014-present: Exists as a next generation real-time and batch processing framework <br /> |
=== Why Apache Spark === | === Why Apache Spark === | ||
Revision as of 08:37, 26 November 2018
Contents
Team Members
Introduction
What is Apache Spark ?
An open-source distributed general-purpose cluster-computing framework for Big Data.
History of Apache Spark
2009: a distributed system framework initiated at UC Berkeley AMPLab by MateiZaharia
2010: Open sourced under a BSD license
2013: The project was donated to the Apache Software Foundation and the license was changed to Apache 2.0
2014: Became an Apache Top-Level Project. Used by Databricks to set a world record in large-scale sorting in November
2014-present: Exists as a next generation real-time and batch processing framework
Why Apache Spark
Data is exploded in volume, velocity and variety The need to have faster analytic results becomes increasingly important Support near real time analytics to answer business questions
Features
Easy to use Supporting python. Java and Scala Libraries for sql, ml, streaming General-purpose Batch like MapReduce is included Iterative algorithm Interactive queries and streaming which return results immediately Speed In memory computations Faster than MapReduce for complex application on disks
