A survey on platforms for big data analytics

Abstract

The primary purpose of this paper is to provide an in-depth analysis of different platforms available for performing big data analytics. This paper surveys different hardware platforms available for big data analytics and assesses the advantages and drawbacks of each of these platforms based on various metrics such as scalability, data I/O rate, fault tolerance, real-time processing, data size supported and iterative task support. In addition to the hardware, a detailed description of the software frameworks used within each of these platforms is also discussed along with their strengths and drawbacks. Some of the critical characteristics described here can potentially aid the readers in making an informed decision about the right choice of platforms depending on their computational needs. Using a star ratings table, a rigorous qualitative comparison between different platforms is also discussed for each of the six characteristics that are critical for the algorithms of big data analytics. In order to provide more insights into the effectiveness of each of the platform in the context of big data analytics, specific implementation level details of the widely used k-means clustering algorithm on various platforms are also described in the form pseudocode.

A Review on Big Data: Views, Categories and Aspects

“…An average Internet user consumes large amount of digital content every day through social sites such as Facebook, Twitter, YouTube, Instagram and on many other similar chat applications [4]. The Cisco Internet business Solution Group (IBSG) predicted that 25 billion devices would be connected to the Internet by 2015 which would reach to 50 billion by 2020.…”

“…Integration of different data sources, to study complicated marketing problems because some complicated business problems require combining data from different sources. 4 Investigation, to deal with the heterogeneity among data sources. Data collection and analysis methods may be different due to different structure, quality, granularity and objective.…”

AHP Model for the Big Data Analytics Platform Selection

“…Chen et al (2014) and Che, Safran, and Peng (2013) reviewed of state of the art frameworks and platforms for processing and managing big data as well as the efforts expected on big data mining. Also Singh and Reddy (2014) provided an in-depth analysis of different platforms available for performing big data analytics and assessed the advantages and drawbacks of each of these platforms based on various metrics such as scalability, data I/O rate, fault tolerance, real-time processing, data size supported and iterative task support. Lee et al (2011) provided a timely remark on the status of MapReduce studies and related work to aim at improving and enhancing the MapReduce framework.…”

“…Thus, to perform any kind of analysis on such voluminous and complex data, scaling up the hardware platforms becomes imminent and choosing the right platform becomes a crucial decision. Researchers have been working on building novel data analysis techniques for big data more than ever before, which has led to the development of many different algorithms and platforms (Singh & Reddy, 2014).…”

An Efficient Bulk Synchronous Parallelized Scheduler for Bioinformatics Application on Public Cloud

“…The University of California at Berkeley presented the Spark platform [12] for cloud computing. A survey of all the available platforms for analysis of big data on the cloud is available in [13]. Of all the platforms that are available, Hadoop-MapReduce is by far a better and popular choice due to its open source nature and its acceptability by various industry and research academic organizations.…”