Investigation Of Nosql Database Management

Abstract- This research documents a comprehensive evaluation of the emerging graph databases along with a benchmark study to compare it to the existing relational model. With the ease of the graphical representation brought in with Neo4j, we saw the opportunity to attempt getting details about the various attributes in the dataset and analyze this data to present a statistical view along with its popular counterpart, MySQL. The ultimate goal of this study is to determine whether a traditional relational database system like MySQL, can be replaced completely in production, by a graph database, such as Neo4j.

In order to overcome these limitations, a new database model known as Not Only SQL (NoSQL) database emerged with a set of new features. The main objective of NoSQL is not to discard SQL, but to be used as an alternative database data model for new features [1] [2] [3]. NoSQL database increases the performance of relational databases by a set of new characteristics and advantages. In contrast to relational databases, NoSQL databases introduced an additional feature that provides flexible and horizontal scalability and taking advantage of new clusters. The rise of NoSQL provides cost-effective management of data in modern web applications. With its new features, NoSQL can be used with applications that have a large transaction, and require low-latency access to huge datasets, service availability while

Graph database: Strength: designed for data whose relations are well represented as a graph and has elements which are interconnected. Graph databases are well-suited to irregular and complex structures. Weakness: Relationships are stored at the individual record level and uses more

Relational databases play a major role in making many apps and programs work. They provide an easy way to store large amounts of data in a consistent, non duplicating, and maintainable way to be used by developers for analytical or software use ("Advantages of a relational database", n.d.). However, more and more applications and companies with a tremendous amount of data such as search engines, social networks, and e-commerce sites have been requiring a level of speed and scalability that relational databases can not provide ("Why NoSQL?", n.d.). NoSQL is a name given to a quickly growing type of database known as non-relational databases, which are being used to store and manage huge amounts of structured, semi-structured, and non-structured data known as "Big Data" ("Why NoSQL?" n.d.). With the advent of social networks and apps with millions of users, the rate of growth of non-structured and semi-structured data is exponential, and the value in being able to quickly traverse it, analyze it, and use it for development is also growing quickly (McGuire, Manyika, & Chui, 2012).

In order to understand NoSQL databases, chapter two will describe the most significant features of NoSQL databases for solving the above mentioned requirements. Since the relational data model is not suitable for some use cases, chapter three will explain structure and flexibility of different data models offered by NoSQL databases. Chapter four will compare two of the widely used NoSQL databases which are MongoDB and Cassandra.

The term “No SQL” is considered in a much wider vision which means “Not Only SQL”. This can be elaborated in the sense that the concept of No SQL does not consider the complete elimination of SQL language, rather it focuses on supporting other SQL like queries. The No SQL Database basically follows a model-free approach. The leading advantage of implementing the No SQL database is eliminating all the restrictions of the rigorously followed structured model in the relational database system. In No SQL approach, there are many flexibilities of choosing eligible data structure according to the information or data that has to be handled. Some of the widely followed data models of the No SQL database are key value stores, column family stores, document database, graph database, etc. The fundamental concept behind the development of the key-value store data model is to create a data model that

A graph database represents data and relationships between this data using concepts from graph data structures like nodes, edges and properties. Nodes represents the data entities, properties represent information about the nodes and edges which connect two nodes or a node and a property represent the relationship between the connected elements. [1]

NoSQL databases had made for unraveling the Big Data issue by utilizing a distributed system to bring out excellent performance in data storage and retrieval at very large-scale. At this scale, pieces of the system often fail and NoSQL is created to handle these failures (Chow, 2013) (Ron, Shulman-Peleg, & Bronshtein, 2015). Various companies have espouse different sorts of non-relational databases, ordinarily alluded to as

The paper provides background and related literature on the Big Data, studies the concept from Relational Database to current NoSQL database which have been fueled by the growth Big Data and importance of managing it. And surveys the Big Data challenges from the perspective of its characteristics Volume, Variety and Velocity and attempts to study how those challenges can be addressed.

NoSQL is able to address the massive traffic loads experienced by database servers at corporations that specialize in data processing like Google, Facebook and Amazon. NoSQL technologies can provide near constant availability, massive user concurrency and lightning fast responses. There are four primary NoSQL database implementation types being used today: document based, wide column (or columnar), key-value and graph. The different properties of SQL and NoSQL databases will be examined and an overview of each NoSQL implementation type along with an example will be given.

The modern RDBMS advancements are not capable of supporting unstructured information with ideal space necessity. The plan winds up plainly mind-boggling and is henceforth troublesome for designers. The requirement for unstructured information administration is so annoying with conventional RDBMS arrangements (Big data in financial services industry: Market trends, challenges, and prospects 2013 - 2018). Moreover, RDBMS turns out to be an exorbitant answer for creating light-footed web applications with direct information investigation necessities. NoSQL is developing as a proficient possibility in this situation, which connects the issues related with RDBMS innovation. The market development can credit to creative dispatches of NoSQL arrangements, and collective endeavors by NoSQL sellers and clients. The endeavors of organizations, to enhance their market offerings, are creating the request of NoSQL, as a back-end bolster (Big data in financial services industry: Market trends, challenges, and prospects 2013 - 2018). The emergence of agile software development is creating the demand for NoSQL (Big data in financial services industry: Market trends, challenges, and prospects 2013 - 2018). They offer users much more avenues to accept data in many different forms. NoSQL is adaptable as SQL but offers many more uses that can apply to many organizations.

NoSQL Databases are being used in the social media applications and big data processing based portals in which huge, heterogeneous and unstructured data formats are handled. NoSQL Databases are used for faster access of records from the big dataset at back-end. The AADHAAR Card implementation in India was done using NoSQL Databases as huge amount of information is associated including Text Data, Images, Thumb Impressions and Iris Detection. Any classical database system cannot handle the dataset of different types (Image, Text, Video, Audio, Video, Thumb Impressions for Pattern Recognition, Iris Sample) simultaneously.

In this report I am going to discuss about No SQL databases and the criterion that are to be taken into consideration when choosing a database that best supports your product. NoSQL databases enclose a variety of database technologies and was developed in response to rise in volume of data unlike relational databases which were built to handle structured data [1].

Currently, there are two major of database management systems which are used to deal with data, the first one called Relational Database Management System (RDBMS) which is the traditional relational databases, it deals with structured data and have been popular since decades from 1970, while the second one called Not only Structure Query Language databases (NoSQL), they have been dealing with semi-structured and unstructured data; the NoSQL term was introduced for the first time in 1998 by Carlo Strozzi and Eric Evans reintroduced the term NoSQL in early 2009, and now the NoSQL types are gaining their popularity with the development of the internet and the social media. NoSQL are intending to override the cons of RDBMS, such as fixed schemas, JOIN operations and handling the scalability problems. With the appearance of Big Data,

Currently, there are two major of database management systems which are used to deal with data, the first one called Relational Database Management System (RDBMS) which is the traditional relational databases, it deals with structured data and have been popular since decades from 1970, while the second one called Not only Structure Query Language databases (NoSQL), they have been dealing with semi-structured and unstructured data; the NoSQL term was introduced for the first time in 1998 by Carlo Strozzi and Eric Evans reintroduced the term NoSQL in early 2009, and now the NoSQL types are gaining their popularity with the development of the internet and the social media. NoSQL are intending to override the cons of RDBMS, such as fixed schemas, JOIN operations and handling the scalability problems. With the appearance of Big Data,