Big Data Analytics Tools and Platform in Big Data Landscape

The term big data analytics refers to mining and analyzing of the voluminous amount of data in big data by using various tools and platforms. Some of the popular tools are Apache Hadoop, Apache Spark, HBase, Storm, Grid Gain, HPCC, Casandra, Pig, Hive, and No SQL, etc. These tools are used depending on the parameter taken for big data analysis. So, we need a comparative analysis of such analytical tools to choose best and simpler way of analysis to gain more optimal throughput and efficient mining. This chapter contributes to a comparative study of big data analytics tools based on different aspects such as their functionality, pros, and cons based on characteristics that can be used to determine the best and most efficient among them. Through the comparative study, people are capable of using such tools in a more efficient way.

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Metamorphosis of data (small to big) and the comparative study of techniques (HADOOP, HIVE and PIG) to handle big data

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.27.11206 ◽

2018 ◽

Vol 7 (2.27) ◽

pp. 1

Author(s):

Rapinder Kaur ◽

Vaishali Chauhan ◽

Urvashi Mittal

Keyword(s):

Big Data ◽

Comparative Study ◽

Data Analytics ◽

Big Data Analytics ◽

Structured Data ◽

Contrast Study ◽

The Individual ◽

Or Organization ◽

Tools And Techniques ◽

The Comparative Study

Immoderate amount of data is being generated everyday across the world via miscellaneous sources or fields which create issues to the users. Due to this rapid growth, the crucial issue is to analyse the big data with the help of traditional data processing tactics. Structured data is not the peerless but moreover unstructured data and semi-structured data charge up the supplementary consequences to handle this voluminous data. As in this gigantic bulk of data highly advantageous information is hidden which can be good for what ails the individual, group or organization and for adding up to more sophisticated or valuable decisions. So in order to deal with this many new tools and techniques have been excogitated. These tools can analyse the large volume of data being generated at unprecedented speed. This paper shows the comparative study of some of the data analytics techniques which can untangle the big data analytics issues by examining it in more précised manner. The contrast study of Hadoop, Hive and Pig has been illustrated which covers the working of these techniques.

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Comparative Study of Big data Analytics Tools: R and Tableau

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/263/4/042052 ◽

2017 ◽

Vol 263 ◽

pp. 042052

Author(s):

C Rajeswari ◽

Dyuti Basu ◽

Namita Maurya

Keyword(s):

Big Data ◽

Comparative Study ◽

Data Analytics ◽

Big Data Analytics

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A multilevel approach to big data analysis using analytic tools and actor network theory

SA Journal of Information Management ◽

10.4102/sajim.v20i1.914 ◽

2018 ◽

Vol 20 (1) ◽

Cited By ~ 4

Author(s):

Tiko Iyamu

Keyword(s):

Big Data ◽

Data Analysis ◽

Network Theory ◽

Data Analytics ◽

Big Data Analytics ◽

Actor Network Theory ◽

Big Data Analysis ◽

Data Sets ◽

Multilevel Approach ◽

Actor Network

Background: Over the years, big data analytics has been statically carried out in a programmed way, which does not allow for translation of data sets from a subjective perspective. This approach affects an understanding of why and how data sets manifest themselves into various forms in the way that they do. This has a negative impact on the accuracy, redundancy and usefulness of data sets, which in turn affects the value of operations and the competitive effectiveness of an organisation. Also, the current single approach lacks a detailed examination of data sets, which big data deserve in order to improve purposefulness and usefulness.Objective: The purpose of this study was to propose a multilevel approach to big data analysis. This includes examining how a sociotechnical theory, the actor network theory (ANT), can be complementarily used with analytic tools for big data analysis.Method: In the study, the qualitative methods were employed from the interpretivist approach perspective.Results: From the findings, a framework that offers big data analytics at two levels, micro- (strategic) and macro- (operational) levels, was developed. Based on the framework, a model was developed, which can be used to guide the analysis of heterogeneous data sets that exist within networks.Conclusion: The multilevel approach ensures a fully detailed analysis, which is intended to increase accuracy, reduce redundancy and put the manipulation and manifestation of data sets into perspectives for improved organisations’ competitiveness.

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The importance of big data technology

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.5.21139 ◽

2018 ◽

Vol 7 (4.5) ◽

pp. 485

Author(s):

Samson Fadiya ◽

Arif Sari

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Management Systems ◽

Apache Hadoop ◽

Industry Standard ◽

Data Framework ◽

Efficiency And Effectiveness ◽

Relational Database Management ◽

Big Data Technology

The adoption of Web 2.0 technologies, Internet of Things, etc. by individuals and organization has led to an explosion of data. As it stands, existing Relational Database Management Systems (RDBMSs) are incapable of handling this deluge of data. The term Big Data was coined to represent these vast, fast and complex datasets that regular RDBMSs could not handle. Special tools or frameworks were developed to deal with processing, managing and storing this big data. These tools are capable of functioning in distributed industry- standard environments thereby maintaining efficiency and effectiveness at a business level. Apache Hadoop is an example of such a framework. This report discusses big data, it origins, opportunities and challenges that it presents, big data analytics and the application of big data using existing big data tools or frameworks. It also discusses Apache Hadoop as a big data framework and provides a basic overview of this technology from technological and business perspectives.

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Hidden big data analytics issues in the healthcare industry

Health Informatics Journal ◽

10.1177/1460458219854603 ◽

2019 ◽

Vol 26 (2) ◽

pp. 981-998 ◽

Cited By ~ 3

Author(s):

Kenneth David Strang ◽

Zhaohao Sun

Keyword(s):

Big Data ◽

Data Analysis ◽

Data Analytics ◽

Research Method ◽

Big Data Analytics ◽

Big Data Analysis ◽

Healthcare Industry ◽

Statistical Control ◽

Subject Matter Experts ◽

Journal Articles

The goal of the study was to identify big data analysis issues that can impact empirical research in the healthcare industry. To accomplish that the author analyzed big data related keywords from a literature review of peer reviewed journal articles published since 2011. Topics, methods and techniques were summarized along with strengths and weaknesses. A panel of subject matter experts was interviewed to validate the intermediate results and synthesize the key problems that would likely impact researchers conducting quantitative big data analysis in healthcare studies. The systems thinking action research method was applied to identify and describe the hidden issues. The findings were similar to the extant literature but three hidden fatal issues were detected. Methodical and statistical control solutions were proposed to overcome the three fatal healthcare big data analysis issues.

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Big Data and Big Data Analytics

Applications of Big Data in Large- and Small-Scale Systems - Advances in Data Mining and Database Management ◽

10.4018/978-1-7998-6673-2.ch001 ◽

2021 ◽

pp. 1-19

Author(s):

Sam Goundar ◽

Akashdeep Bhardwaj ◽

Shavindar Singh ◽

Mandeep Singh ◽

Gururaj H. L.

Keyword(s):

Big Data ◽

Data Analytics ◽

Business Processes ◽

New Technologies ◽

Big Data Analytics ◽

Small Island ◽

Island State ◽

Diverse Data ◽

Analytical Tools ◽

Small Island State

Big data is emerging, and the latest developments in technology have spawned enormous amounts of data. The traditional databases lack the capabilities to handle this diverse data and thus has led to the employment of new technologies, methods, and tools. This research discusses big data, the available big data analytical tools, the need to use big data analytics with its benefits and challenges. Through a research drawing on survey questionnaires, observation of the business processes, interviews and secondary research methods, the organizations, and companies in a small island state are identified to survey which of them use analytical tools to handle big data and the benefits it proposes to these businesses. Organizations and companies that do not use these tools were also surveyed and reasons were outlined as to why these organizations hesitate to utilize such tools.

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Comparative study of tools for big data analytics: An analytical study

2017 International Conference on Computing, Communication and Automation (ICCCA) ◽

10.1109/ccaa.2017.8229827 ◽

2017 ◽

Cited By ~ 5

Author(s):

Sanjib Kumar Sahu ◽

M Mary Jacintha ◽

Amit Prakash Singh

Keyword(s):

Big Data ◽

Comparative Study ◽

Data Analytics ◽

Analytical Study ◽

Big Data Analytics

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Research of Big Data Processing Platform

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.484-485.922 ◽

2014 ◽

Vol 484-485 ◽

pp. 922-926

Author(s):

Xiang Ju Liu

Keyword(s):

Cloud Computing ◽

Big Data ◽

Data Analysis ◽

Network Architecture ◽

Data Analytics ◽

Big Data Analytics ◽

Big Data Analysis ◽

Computing Platform ◽

Operational Characteristics ◽

Analysis Platform

This paper introduces the operational characteristics of the era of big data and the current era of big data challenges, and exhaustive research and design of big data analytics platform based on cloud computing, including big data analytics platform architecture system, big data analytics platform software architecture , big data analytics platform network architecture big data analysis platform unified program features and so on. The paper also analyzes the cloud computing platform for big data analysis program unified competitive advantage and development of business telecom operators play a certain role in the future.

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