Relational Databases For An Efficient Data Management And Retrieval Of Data

Relational database systems came into existence in the 1970’s, and revolutionized the way data is maintained in computers. Like every technology that is subjected to test of time, relational database systems are under criticism for not being scalable enough to meet growing users and exponentially increasing data management needs. Today, a new technology called NoSQL is being pioneered by leading companies like Google, Amazon, and Facebook etc. to meet the shortcomings of the 40-year-old relational database technology. Modern web applications are making a transition from traditional relational databases to NoSQL databases to meet the demand of steadily growing concurrent users and big data.

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).

This paper will discuss the main difference between the relational database optimized for on line transactions and a data warehouse optimized for processing and summarizing large amounts of data. Next this author will outline the difference database requirements for operational data for decision support data. Next this paper will describe three example in which databases could be used to support decision making in a large organizational environment. Lastly this author will describe three other examples in which data warehoused and data mining could be used to support data processing and trend analysis in a large organizational environment.

With the development of the Internet and cloud computing, there need databases to be able to store and process big data effectively, demand for high-performance when reading and writing, so the traditional relational database is facing many new challenges. Especially in large scale and high-concurrency applications, such as search engines and SNS, using the relational database to store and query dynamic user data has appeared to be inadequate. In this case, NoSQL database created.

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

For example, Facebook which is the most popular social networking website recently announced their adoption of a NoSQL based graph data store for efficient storage of user data. In other words, NoSQL has already made its way into the enterprise. However, just like every other widely accepted technology, NoSQL has its own set of advantages and disadvantages. It is important for an enterprise to quantify the pros and cons of a particularly new database technology against the already existing solutions based on their custom requirements. For example, legacy enterprise applications may require extensive community support from their database vendors. Moreover, traditional relational database vendors such as Oracle have already established themselves for providing excellent support. On the other hand, NoSQL has been rapidly growing since the past few years and is consistently evolving in terms of big data handling, data warehousing and lesser complexity. Hence, there is a need to study the current market of data stores based on the most popular NoSQL data stores and how well they fair against the widely accepted traditional database systems. This requires a study of the commonly used NoSQL data stores.

NoSQL databases are a significant departure from the relational model that has dominated the business world for the past few decades. Standing for “Not Only SQL,” these products are all some variation of a non-relational, key-value pair database, and they are becoming very popular with companies that use Big Data and prioritize speed or availability over consistency of data.

STRUCTURE OF DATA: The data structure of a relational database comprises of table structure. Every table is identified by a unique name or label. The data tables are described as the collection of rows and columns. Each row of the table is known as the record and each column is known as the field of the specific data table. All the data sets are well organized and logical linked to each other through definite and unique relationships. A table, therefore can also be defined as the “structured collection of relationships”. The fundamental aim of developing No SQL database systems is to easily and effectively handle vast quantities of data or information in advanced web-scale applications. In order to achieve this purpose, the No SQL systems are designed as the schema-free database systems. There are different modes to define the No SQL databases that typically depend on the requirements of the data that has to be managed. The data model for key-value store No SQL database is

NoSQL databases are databases designed to run on clusters of computers/servers, built for the ever-increasing data storage needs for websites. Devised as a way of scaling databases horizontally which is a challenge with traditional relational databases. Scaling horizontally is the ability to add more computers/servers as nodes to a database. These “clusters” work well with write-heavy systems and allow increase storage and processing power limited only by the number of connections you can have on the network. Defined as No-Schema, No-SQL data structures mean they are not limited to the original data structure. Objects and fields etc can be implemented at

application. Specifically, this report investigates the use of relational database design versus the no-SQL model as the preferred basis of the new application.

For the challenges we are facing be it technical or functional we find a NoSql data base as a best fit. We found out that NoSql incorporates a wide mixed bag of various database technologies and were produced in response to the rising data needs. Also when in comparison to the RDBMS present in the market NoSql provides an enriched performance and better scalability solutions. So in search of the best fit as our solution we searched out various types of NoSql database types and found out about Document databases, Graph databases, Key value stores and other similar types. Let’s explore various market players in each of the type and find the best one.

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.

In addition to its flexibility, these databases provides horizontal scalability and distributed computing that led to adoption of NoSQL databases in the firms. The SQL databases uses Structured Query Language whereas NOSQL databases use Unstructured Query Language which varies from database to database.

Present day most of the clients are using the traditional databases like Oracle, DB2 etc and are experiencing problems in storage and performance. A large number of changes are required so that they can overcome all the drawback of the traditional database and researches are carrying out which is resulting in the database which differ from the normal database characteristics. Various number of clients are changing to NoSQL to overcome the drawbacks they are facing in traditional Database. So NoSQL have increasing demand because of following properties:

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,