A Survey of Big Data Analytics Systems: Appliances, Platforms, and Frameworks

A Survey of Big Data Analytics Systems: Appliances, Platforms, and Frameworks

M. Baby Nirmala (Holy Cross College, India)
Copyright: © 2016 |Pages: 29
DOI: 10.4018/978-1-4666-9840-6.ch046
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Abstract

In this emerging era of analytics 3.0, where big data is the heart of talk in all sectors, achieving and extracting the full potential from this vast data is accomplished by many vendors through their new generation analytical processing systems. This chapter deals with a brief introduction of the categories of analytical processing system, followed by some prominent analytical platforms, appliances, frameworks, engines, fabrics, solutions, tools, and products of the big data vendors. Finally, it deals with big data analytics in the network, its security, WAN optimization tools, and techniques for cloud-based big data analytics.
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Categories Of Analytical Processing Systems

In his blog, Eckerson, (2013) explained, at a high-level, there are four categories of Analytical Processing Systems available in this era of Big data:

  • Transactional RDBM Systems.

  • Hadoop Distributions.

  • NoSQL Databases.

  • Analytic Platforms.

Other than these categories, there are analytical engines, frameworks, fabrics, etc., which also play a prominent role in big data analytics.

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Hadoop Distributions

Hadoop is an open source software project run within the Apache Foundation for processing data-intensive applications in a distributed environment with built-in parallelism and failover. The most important parts of Hadoop are the Hadoop Distributed File System, which stores data in files on a cluster of servers, and MapReduce, a programming framework for building parallel applications that run on HDFS (Hadoop Distributed File System)

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