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

2017 ◽  
Author(s):  
Sanjib Kumar Sahu ◽  
M Mary Jacintha ◽  
Amit Prakash Singh
Keyword(s):  
Big Data ◽  
Comparative Study ◽  
Data Analytics ◽  
Analytical Study ◽  
Big Data Analytics

Big Data Analytics Tools and Platform in Big Data Landscape

2018 ◽  
pp. 80-89
Author(s):  
Mohd Imran ◽  
Mohd Vasim Ahamad ◽  
Misbahul Haque ◽  
Mohd Shoaib
Keyword(s):  
Big Data ◽  
Comparative Analysis ◽  
Comparative Study ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Apache Hadoop ◽  
Pros And Cons ◽  
The Comparative Study ◽  
Analytical Tools

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.

Big Data Analytics Tools and Platform in Big Data Landscape

2022 ◽  
pp. 622-631
Author(s):  
Mohd Imran ◽  
Mohd Vasim Ahamad ◽  
Misbahul Haque ◽  
Mohd Shoaib
Keyword(s):  
Big Data ◽  
Comparative Analysis ◽  
Comparative Study ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Apache Hadoop ◽  
Pros And Cons ◽  
The Comparative Study ◽  
Analytical Tools

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.

scholarly journals Effect of SBI Bank Merger on Education Loan, a Comparative Study Using Big Data Analytics

2018 ◽  
Vol 7 (2.32) ◽  
pp. 452
Author(s):  
Anjali Mathur ◽  
K Vinitha ◽  
R Shubham ◽  
K Gowtham
Keyword(s):  
Big Data ◽  
Comparative Study ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Global Level ◽  
Analysis Techniques ◽  
Bank Merger ◽  
Huge Data ◽  
High Level ◽  
The Impact

A bank merger is a situation in which two banks or all branches of a bank join together to become one bank. The bank merger of State Bank of India was implemented on 1stApril 2017 in India. The bank merger is a good idea to centralize the customer’s data from nationwide. However, it is a difficult task for administrators and technologists. Some high level techniques are required to collect the data from the branches, of the bank present at nationwide, and merge them accordingly. For this huge data Big-Data Analysis techniques can be used to manage and access the data. The big data analytics provides algorithms to compare, classify and cluster the data at local and global level. This research paper proposes big data analytics for education loan provided by State Bank of India. The loan granting process becomes centralized after merger. It affects the processing of granting a loan, as earlier it was according to branches only. The proposed work is for comparative study of the impact of bank merger on education loan provided by State Bank of India.  

scholarly journals Traditional machine learning and big data analytics in virtual screening: a comparative study

2020 ◽  
Vol 10 (47) ◽  
pp. 72-88 ◽  
Author(s):  
Sahar K. Hussin ◽  
Yasser M. Omar ◽  
Salah M. Abdelmageid ◽  
Mahmoud I. Marie
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Virtual Screening ◽  
Comparative Study ◽  
Data Analytics ◽  
Big Data Analytics

scholarly journals Big Data Analytics in Healthcare using Machine Learning Algorithms: A Comparative Study

2020 ◽  
Vol 16 (13) ◽  
pp. 19
Author(s):  
Sai Hanuman Akundi ◽  
Soujanya R ◽  
Madhuri PM
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Comparative Study ◽  
Data Analytics ◽  
Learning Algorithms ◽  
Big Data Analytics ◽  
Large Data ◽  
Heterogeneous Data ◽  
Machine Learning Algorithms ◽  
Healthcare Sector

In recent years vast quantities of data have been managed in various ways of medical applications and multiple organizations worldwide have developed this type of data and, together, these heterogeneous data are called big data. Data with other characteristics, quantity, speed and variety are the word big data. The healthcare sector has faced the need to handle the large data from different sources, renowned for generating large amounts of heterogeneous data. We can use the Big Data analysis to make proper decision in the health system by tweaking some of the current machine learning algorithms. If we have a large amount of knowledge that we want to predict or identify patterns, master learning would be the way forward. In this article, a brief overview of the Big Data, functionality and ways of Big data analytics are presented, which play an important role and affect healthcare information technology significantly. Within this paper we have presented a comparative study of algorithms for machine learning. We need to make effective use of all the current machine learning algorithms to anticipate accurate outcomes in the world of nursing.

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

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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