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5 Open Source Big Data Technologies To Look Out For!
 
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5 Open Source Big Data Technologies To Look Out For!  
 
The open source technology is at the core of most Big Data initiatives. We bring to you the top 5!    
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Saturday, November 09, 2013 With all the big companies storing more and more data for various purposes, Big Data is already becoming the next big thing, and Gartner has already announced that data analysis is going to be the most prosperous IT job in the world. Here we bring top 5 open source big data technologies to look out for!

big data, open source, data analsys, big data technologies, storing big data, hadoop, ElasticSearch, Scribe,  Cascading,




1. Apache Hadoop

The open source software framework, Apache Hadoop is meant for data-intensive distributed applications, which was created by Doug Cutting to support his open source Web search engine, Nutch. Cutting introduced Hadoop as the MapReduce facility and a distributed file system to meet Nutch's multimachine processing requirements. The name Hadoop came after his son's toy elephant. With this MapReduce facility Hadoop distributes Big Data in pieces over a series of nodes running on commodity hardware. Hadoop is now already amongst one of the most popular technologies for gather and store structured, semi-structured and unstructured data which together makes Big Data. Hadoop is available under the Apache License 2.0.

2. R

The open source programming language and software environment, R, was designed for statistical computing and visualization. Ross Ihaka and Robert Gentleman designed R at the University of Auckland, New Zealand in the year 1993. R is has lately become the go-to tool for statistical analysis for the large data sets. Company called Revolution Analytics is now commercialising R in a similar model alike of Red Hat. R is available under the GNU General Public License.

3. Cascading

Cascading is an open source software abstraction layer for Hadoop which with the use of JVM-based language lets the users create and execute data processing workflows on Hadoop clusters. Cascading intends to cover the complexities beneath the MapReduce jobs. Designed by Chris Wensel, it was initially created as an alternative API to MapReduce. The software layer is usually used for ad targeting, log file analysis, bioinformatics, machine learning, predictive analytics, Web content mining and ETL applications. A company founded by Wensel called Concurrent is providing the commercial support for Cascading. Big shot enterprises like Twitter and Etsy are already using Cascading, it is available under the Apache License.

4. Scribe

A server developed by Facebook, Scribe was released in 2008. The server intends to aggregate log data streaming in real time from a large number of servers. As Facebook is required to meet its own scaling challenges, it now uses Scribe to tackle with the tens of billions of messages coming every day. It is available under the Apache License 2.0.

5. ElasticSearch

Inspired by and based upon Apache Lucene, this open source search server ElasticSearch was developed by Shay Banon. This scalable solution supports real-time search and multitenancy without any special configuration. The server has is currently being used by numerous companies like StumbleUpon and Mozilla. ElasticSearch is available under the Apache License 2.0.

Additionally Apache CouchDB, MongoDB, Apache Cassandra and Apache Hbase are worth a mention.




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