scholarly journals NoSQL- A Solution for Handling Big Data

2019 ◽  
Vol 9 (1) ◽  
pp. 7606-7610
Keyword(s):  
Big Data ◽  
Special Reference ◽  
Unstructured Data ◽  
Huge Amount ◽  
Nosql Databases ◽  
Acid Properties ◽  
Distributed Framework ◽  
Soft State ◽  
Different Types ◽  
Eventual Consistency

The data produced nowadays is the Big data which is unstructured, semi-structured or structured in nature. It is difficult for SQL to handle such large amount of data with varied forms so NoSQL was introduced which gives many advantages over SQL. It is a schema less database which allows horizontal scaling, scalability and distributed framework. SQL is based on ACID properties (Atomicity, Consistency, Isolation, Durability) whereas NoSQL is based on BASE (Basic Availability, Soft state, Eventual consistency). This paper introduces the concepts of NoSQL, advantages of NoSQL over SQL, different types of NoSQL databases with special reference to the document store database, MongoDB. It explains in detail about the MongoDB and then experimentally evaluates the performance of the queries executed in SQL and NoSQL (MongoDB). The experiment conducted shows that NoSQL queries are executed faster as compared to SQL queries and can also handle huge amount of unstructured data very effortlessly and easily.

scholarly journals Applications of Big Data in the Digital India: Opportunities and Challenges

2016 ◽  
Vol 3 (3) ◽  
Author(s):  
Vinay Kumar ◽  
Arpana Chaturvedi
Keyword(s):  
Big Data ◽  
Social Networking ◽  
Exponential Growth ◽  
Social Networking Sites ◽  
Unstructured Data ◽  
Threat Perception ◽  
Data Repository ◽  
Huge Amount ◽  
Real Challenge ◽  
Area Of Application

<div><p><em>With the advent of Social Networking Sites (SNS), volumes of data are generated daily. Most of these data are multimedia type and unstructured with exponential growth. This exponential growth of variety, volume and complexity of structured and unstructured data leads to the concept of big data. Managing big data and harnessing its benefits is a real challenge. With increase in access to big data repository for various applications, security and access control is another aspect that needs to be considered while managing big data. We have discussed area of application of big data, opportunities it provides and challenges that we face in the managing such huge amount of data for various applications. Issues related to security against different threat perception of big data are also discussed. </em></p></div>

scholarly journals Big Data and MapReduce Challenges, Opportunities and Trends

2016 ◽  
Vol 6 (6) ◽  
pp. 2911
Author(s):  
Sachin Arun Thanekar ◽  
K. Subrahmanyam ◽  
A. B. Bagwan
Keyword(s):  
Big Data ◽  
High Velocity ◽  
Structured Data ◽  
Unstructured Data ◽  
Map Reduce ◽  
Huge Amount ◽  
Commodity Hardware ◽  
Survey Paper ◽  
Recent Trends ◽  
Large Clusters

<p>Nowadays we all are surrounded by Big data. The term ‘Big Data’ itself indicates huge volume, high velocity, variety and veracity i.e. uncertainty of data which gave rise to new difficulties and challenges. Big data generated may be structured data, Semi Structured data or unstructured data. For existing database and systems lot of difficulties are there to process, analyze, store and manage such a Big Data.  The Big Data challenges are Protection, Curation, Capture, Analysis, Searching, Visualization, Storage, Transfer and sharing. Map Reduce is a framework using which we can write applications to process huge amount of data, in parallel, on large clusters of commodity hardware in a reliable manner. Lot of efforts have been put by different researchers to make it simple, easy, effective and efficient. In our survey paper we emphasized on the working of Map Reduce, challenges, opportunities and recent trends so that researchers can think on further improvement.</p>

Security Issues and Challenges Related to Big Data

2018 ◽  
pp. 86-101
Author(s):  
Jaimin N. Undavia ◽  
Atul Patel ◽  
Sheenal Patel
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Database Systems ◽  
Heterogeneous Data ◽  
Unstructured Data ◽  
Huge Amount ◽  
Current Time ◽  
Security Issues ◽  
Future Prediction ◽  
Data Term

Availability of huge amount of data has opened up a new area and challenge to analyze these data. Analysis of these data become essential for each organization and these analyses may yield some useful information for their future prospectus. To store, manage and analyze such huge amount of data traditional database systems are not adequate and not capable also, so new data term is introduced – “Big Data”. This term refers to huge amount of data which are used for analytical purpose and future prediction or forecasting. Big Data may consist of combination of structured, semi structured or unstructured data and managing such data is a big challenge in current time. Such heterogeneous data is required to maintained in very secured and specific way. In this chapter, we have tried to identify such challenges and issues and also tried to resolve it with specific tools.

Big Data

2015 ◽  
Vol 6 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Ashok Kumar Wahi ◽  
Yajulu Medury ◽  
Rajnish Kumar Misra
Keyword(s):  
Big Data ◽  
Unstructured Data ◽  
Data Driven ◽  
Enterprise 2.0 ◽  
Huge Amount ◽  
The World ◽  
Data Driven Decisions ◽  
The Impact

Big data has taken the world by storm. Everyone from every industry is not only talking about the impact of big data but is looking for ways to effectively leverage the power of big data. This challenge has heightened with the huge amount of unstructured data flowing from every direction, bringing along with it the increasing pressure to make data driven decisions rather than the gut-driven decisions. This article sheds light on how big data can be an enabler for smart enterprises if the organization is able to address the challenges posed by big data. Enterprises need to equip themselves with relevant technology, desired skills and a supporting managerial attitude to swim through the challenges of big data. It also highlights the need for all enterprises making the journey from 1.0 stage to Enterprise 2.0 to master the art of Big Data if they have to make the transition successful.

scholarly journals A Review of Polyglot Persistence in the Big Data World

Information ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 141 ◽  
Author(s):  
Pwint Phyu Khine ◽  
Zhaoshun Wang
Keyword(s):  
Big Data ◽  
Distributed Systems ◽  
Query Language ◽  
Point Of View ◽  
Data Systems ◽  
Data Intensive ◽  
Soft State ◽  
Eventual Consistency ◽  
Big Data Systems ◽  
The Relationship

The inevitability of the relationship between big data and distributed systems is indicated by the fact that data characteristics cannot be easily handled by a standalone centric approach. Among the different concepts of distributed systems, the CAP theorem (Consistency, Availability, and Partition Tolerant) points out the prominent use of the eventual consistency property in distributed systems. This has prompted the need for other, different types of databases beyond SQL (Structured Query Language) that have properties of scalability and availability. NoSQL (Not-Only SQL) databases, mostly with the BASE (Basically Available, Soft State, and Eventual consistency), are gaining ground in the big data era, while SQL databases are left trying to keep up with this paradigm shift. However, none of these databases are perfect, as there is no model that fits all requirements of data-intensive systems. Polyglot persistence, i.e., using different databases as appropriate for the different components within a single system, is becoming prevalent in data-intensive big data systems, as they are distributed and parallel by nature. This paper reflects the characteristics of these databases from a conceptual point of view and describes a potential solution for a distributed system—the adoption of polyglot persistence in data-intensive systems in the big data era.

scholarly journals Text And Sentimental Analysis On Big Data

2021 ◽  
pp. 324-334
Author(s):  
Saifuzzafar Jaweed Ahmed
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Virtual Machines ◽  
Heterogeneous Data ◽  
Machine Learning Algorithms ◽  
Unstructured Data ◽  
Large Field ◽  
Huge Amount ◽  
Data Set ◽  
Big Data Processing

Big Data has become a very important part of all industries and organizations sectors nowadays. All sectors like energy, banking, retail, hardware, networking, etc all generate a huge amount of unstructured data which is processed and analyzed accurately in a structured form. Then the structured data can reveal very useful information for their business growth. Big Data helps in getting useful data from unstructured or heterogeneous data by analyzing them. Big data initially defined by the volume of a data set. Big data sets are generally huge, measuring tens of terabytes and sometimes crossing the sting of petabytes. Today, big data falls under three categories structured, unstructured, and semi-structured. The size of big data is improving in a fast phase from Terabytes to Exabytes Of data. Also, Big data requires techniques that help to integrate a huge amount of heterogeneous data and to process them. Data Analysis which is a big data process has its applications in various areas such as business processing, disease prevention, cybersecurity, and so on. Big data has three major issues such as data storage, data management, and information retrieval. Big data processing requires a particular setup of hardware and virtual machines to derive results. The processing is completed simultaneously to realize results as quickly as possible. These days big data processing techniques include Text mining and sentimental analysis. Text analytics is a very large field under which there are several techniques, models, methods for automatic and quantitative analysis of textual data. The purpose of this paper is to show how the text analysis and sentimental analysis process the unstructured data and how these techniques extract meaningful information and, thus make information available to the various data mining statistical and machine learning) algorithms.

scholarly journals Access Control Models in NoSQL Databases: An Overview

2019 ◽  
Vol 8 (1) ◽  
pp. 1-9
Author(s):  
Ashwaq A. Alotaibi, Reem M. Alotaibi and Nermin Hamza Ashwaq A. Alotaibi, Reem M. Alotaibi and Nermin Hamza
Keyword(s):  
Access Control ◽  
Relational Databases ◽  
Unstructured Data ◽  
Huge Amount ◽  
Sensitive Data ◽  
Nosql Databases ◽  
Security Issues ◽  
Access Control Models ◽  
Control Models ◽  
Nosql Database

Recently non-relational databases known as NoSQL have become most popular for handling a huge amount of data. Many organizations move from relational databases towards NoSQL databases due to the growing popularity of cloud computing and big data. NoSQL database is designed to handle unstructured data like documents, e-mails, and social media efficiently. It uses distributed and cooperating devices to store and retrieve data. As a large number of people storing sensitive data in NoSQL databases, security issues become critical concerns. NoSQL has many advantages like scalability and availability, but it suffers from some security issues like weak authorization mechanisms. This paper reviews the different models of NoSQL databases and the security issues concerning these databases. In addition, we present the existing access control models in different NoSQL databases.

scholarly journals Tracking the New Demand for Justice in the Big Data Ecosystem

2018 ◽  
Vol 10 (1) ◽  
pp. 90
Author(s):  
Carmen Vargas Pérez ◽  
Juan Luis Peñaloza Figueroa
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Social Welfare ◽  
Law And Economics ◽  
Point Of View ◽  
Unstructured Data ◽  
New Paradigm ◽  
Huge Amount ◽  
Equity And Efficiency ◽  
Data Ecosystem

Many studies have focused on the possibilities that organizations have to mine and analyze, through computational analytics, the huge amount of structured and unstructured data that is now available, to determine correlations and thus reveal patterns, trends, and associations to predict human behaviour; and to transform this information into knowledge for companies and governments. That is, just from the point of view of the suppliers of good and services. In this paper we contribute to the Law and Economics literature by showing that the logic of Big Data, the access to the cloud, and the use of Artificial Intelligence are drastically changing the ordinary citizen's way of making decisions in the field of justice; and that this new paradigm in the Demand for Justice will mean improvements in terms of both equity and efficiency, and ultimately an improvement in social welfare.

A Study on MapReduce: Challenges and Trends

2016 ◽  
Vol 4 (1) ◽  
pp. 176
Author(s):  
Sachin Arun Thanekar ◽  
K. Subrahmanyam ◽  
A. B. Bagwan
Keyword(s):  
Big Data ◽  
High Velocity ◽  
Structured Data ◽  
Unstructured Data ◽  
Map Reduce ◽  
Huge Amount ◽  
Commodity Hardware ◽  
Survey Paper ◽  
Recent Trends ◽  
Large Clusters

<p>Nowadays we all are surrounded by Big data. The term ‘Big Data’ itself indicates huge volume, high velocity, variety and veracity i.e. uncertainty of data which gave rise to new difficulties and challenges. Big data generated may be structured data, Semi Structured data or unstructured data. For existing database and systems lot of difficulties are there to process, analyze, store and manage such a Big Data. The Big Data challenges are Protection, Curation, Capture, Analysis, Searching, Visualization, Storage, Transfer and sharing. Map Reduce is a framework using which we can write applications to process huge amount of data, in parallel, on large clusters of commodity hardware in a reliable manner. Lot of efforts have been put by different researchers to make it simple, easy, effective and efficient. In our survey paper we emphasized on the working of Map Reduce, challenges, opportunities and recent trends so that researchers can think on further improvement. </p>

3. Storing big data

2017 ◽  
pp. 26-43
Author(s):  
Dawn E. Holmes
Keyword(s):  
Big Data ◽  
Data Compression ◽  
File Systems ◽  
Lossless Compression ◽  
Unstructured Data ◽  
Distributed File Systems ◽  
Nosql Databases ◽  
The Difference ◽  
Relational Database Management ◽  
Relational Database Management Systems

The amount of data generated is approximately doubling every two years. How do we store and manage these colossal amounts of data? ‘Storing big data’ considers database storage and the idea of distributing tasks across clusters of computers. Relational database management systems are used to create, maintain, access, and manipulate structured data, whereas distributed file systems provide effective and reliable storage for unstructured data across many servers. NoSQL databases and their architecture are discussed along with the CAP Theorem and Cloud storage. The difference between lossless compression for text files and lossy data compression for sound and image files is also explained.

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