Difference between revisions of "GPU621/ApacheSpark"
(→Introduction) |
(→What is Apache Spark) |
||
| Line 24: | Line 24: | ||
Support near real time analytics to answer business questions | 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 | ||
== How it works == | == How it works == | ||
Revision as of 16:15, 25 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
