IMPLEMENTATION CHALLENGES INVOLVED IN BIG DATA ANALYTICS

In today's digital-era, we are bowed down by the massive data that is generated at exponential rates. Technically, this massive data is referred to as Big Data. Simultaneously, the need to manage Big Data arises. Big Data, due to its high volume, velocity, veracity, value, variety, leads to various issues. In this paper, we talk about the various challenges faced because of the exorbitant amount of data. We not only face challenges in processing, but also in designing, analysing, storage, management, privacy and security issues.

International Journal on Cybernetics & Informatics, 2016

Big Data has gained much interest from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that quickly exceeds the boundary range. As information is transferred and shared at light speed on optic fiber and wireless networks, the volume of data and the speed of market growth increase. Conversely, the fast growth rate of such large data generates copious challenges, such as the rapid growth of data, transfer speed, diverse data, and security. Even so, Big Data is still in its early stage, and the domain has not been reviewed in general. Hence, this study expansively surveys and classifies an assortment of attributes of Big Data, including its nature, definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a data life cycle that uses the technologies and terminologies of Big Data. Map/Reduce is a programming model for efficient distributed computing. It works well with semi-structured and unstructured data. A simple model but good for a lot of applications like Log processing and Web index building.

The Scientific World Journal, 2014

Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over 2 billion people worldwide are connected to the Internet, and over 5 billion individuals own mobile phones. By 2020, 50 billion devices are expected to be connected to the Internet. At this point, predicted data production will be 44 times greater than that in 2009. As information is transferred and shared at light speed on optic fiber and wireless networks, the volume of data and the speed of market growth increase. However, the fast growth rate of such large data generates numerous challenges, such as the rapid growth of data, transfer speed, diverse data, and security. Nonetheless, Big Data is still in its infancy stage, and the domain has not been reviewed in general. Hence, this study comprehensively surveys and classifies the various attributes of Big Data, including its nature, definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a data life cycle that uses the technologies and terminologies of Big Data. Future research directions in this field are determined based on opportunities and several open issues in Big Data domination. These research directions facilitate the exploration of the domain and the development of optimal techniques to address Big Data.

Big Data is the large amount of data that cannot be processed by making use of traditional methods of data processing. Due to widespread usage of many computing devices such as smartphones, laptops, wearable computing devices; the data processing over the internet has exceeded more than the modern computers can handle. Due to this high growth rate, the term Big Data is envisaged. However, the fast growth rate of such large data generates numerous challenges, such as data inconsistency and incompleteness, scalability, timeliness, and security. This paper provides a brief introduction to the Big data technology and its importance in the contemporary world. This paper addresses various challenges and issues that need to be emphasized to present the full influence of big data. The tools used in Big data technology are also discussed in detail. This paper also discusses the characteristics of Big data and the platform used in Big Data i.e. Hadoop.

Global Journal of Pure and Applied Mathematics, Research India Publications http://www.ripublication.com, 2017

The term Big Data describes the modern techniques and technologies to capture, store, distribute, manage and analyze more than exabyte or a very large-sized datasets with extreme velocity and various structures. Big data can be categorized into structured, unstructured or semi-structured, which is leading towards failure of traditional data management approach. Data is generated from variety of sources and can arrive in the system at various rates. This large amount of data needs to be processed in an economic and methodical approach which deals with the scalability, complexity, and diversity. Big Data will need effective techniques, high performance algorithms, and better analytics to categorize and extract valuable hidden information from it. This paper focused on parameters, challenges and techniques which are useful to handle large amount of data.

Social Network Forensics, Cyber Security, and Machine Learning, 2018

Data is the most important unit of information. Now a day, data is being generated in a phenomenal speed. Data is being collected from various sources like social media, sensors, machines, etc. To get vital information, it is very important that the data should get processed in very smart and intelligent way. Traditional approach of processing data is not capable of processing the humongous data generated these days. So to overcome the problem of smart processing of data, Big Data analytics came into existence. Many scientists are working to make it more efficient. This technique is using latest ways to process the data generated from various sources. It just not only store and process the data but keep the integrity of the data also, as some data are very confidential for the organizations. If some organization is sharing their data, their primary requirement is the confidentiality and integrity of the data. Big Data analytics takes care of the requirement of the organization. It has been proved a very power method for processing of data in area of surveillance, health care, fraud detection, reduction of crime, etc. The purpose of this paper is to discuss the features of Big Data and its applications. In this paper, the state of art and applications of Big Data will be discussed. We will discuss about the work already done in the field of improving the integrity and usability of data generated by using Big Data analytics techniques. This will also covers the latest solutions offered by the researchers for the challenges in Big Data analytics.

Advaita Innovative Research Association, 2016

Big Data is the large amount of data that cannot be processed by making use of traditional methods of data processing. Due to widespread usage of many computing devices such as smart phones, laptops, wearable computing devices; the data processing over the internet has exceeded more than the modern computers can handle. Due to this high growth rate, the term Big Data is envisaged. However, the fast growth rate of large data will generate numerous challenges, such as data inconsistency and incompleteness, scalability, timeliness, and security. This paper provides a brief introduction to the big data technology and its importance in the contemporary world. This paper addresses various challenges and issues that need to be emphasized to present the full influence of big data. The tools used in big data technology are also discussed in detail. This paper also discusses the characteristics of Big data and the platform used in Big Data i.e. Hadoop.

—The increasing amount of data and a need to analyze the given data in a timely manner for multiple purposes has created a serious barrier in the big data analysis process. This article describes the challenges that big data creates at each step of the big data analysis process. These problems include typical analytical problems as well as the most uncommon challenges that are futuristic for the big data only. The article breaks down problems for each step of the big data analysis process and discusses these problems separately at each stage. It also offers some simplistic ways to solve these problems.

Data is growing with tremendous rate not only in the form of volume but also in different formats mainly semi-structured or unstructured. With the growing use of social networking websites, use of smart phones this growth will increase day by day. Companies are showing their interest not only to store that data but also to analyze that data to extract knowledge from that data. This growing data is in the form of audio, video, images, text and transactional data. So, we can say thatBig data is collection of huge amount of data which is complex in nature. To analyze that huge amount of data requires huge storage, the use of high speed parallel and distributed computing and high ended analytical tools. There are various issues and challenges to handle the volume, velocity, distributed nature of data, and its analysis poses challengesin data analytics. This paper covers various challenges and issues to handle that Big Data for various organizations.

International Journal of Information Management, 2016

Big data is a potential research area receiving considerable attention from academia and IT communities. In the digital world, the amounts of data generated and stored have expanded within a short period of time. Consequently, this fast growing rate of data has created many challenges. In this paper, we use structuralism and functionalism paradigms to analyze the origins of big data applications and its current trends. This paper presents a comprehensive discussion on state-of-the-art big data technologies based on batch and stream data processing. Moreover, strengths and weaknesses of these technologies are analyzed. This study also discusses big data analytics techniques, processing methods, some reported case studies from different vendors, several open research challenges, and the opportunities brought about by big data. The similarities and differences of these techniques and technologies based on important parameters are also investigated. Emerging technologies are recommended as a solution for big data problems.