Relational Databases For An Efficient Data Management And Retrieval Of Data

The ever-widening realm of big data has created an expanding frontier of exploration for the creation of new methods of data analysis in order to produce actionable knowledge for the benefit of organizations everywhere. Companies amass enormous troves of data every day. Keeping this data housed in a fashion that maximizes storage efficiency and in a format optimized for query and analysis is paramount for effective data warehousing. Many database structures exist for the storage, arrangement, and accessing of data, but large databases and online analytical processing (OLAP) benefit from specific qualities. In these databases, compression and rapid querying are the main enabling qualities sought for analytical data stores and data warehouses. Columnar (or column-oriented) relational databases (RDBMS) offer these and other benefits, which is why it is a popular database scheme for analytical systems. Specifically, the vertical arrangement of records is optimal for selecting the sum, average, or a count of total record attributes because one horizontal read yields all values of an attribute. Otherwise, a physical disk must seek over and past unwanted attributes of the records to provide the same

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

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.

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.

In this paper, we explore a popular example of each type of database and examine what kinds of problems these products are best suited to solve.

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.

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

In the initial stages of evolution of databases, relational databases systems was designed as a solution to the problems of flat file databases. A relational database stores data in multiple table. This technique helped to overcome the issues like data duplication, data noise and inconsistency which ensured that the data is entered and stored only once. Later as the data grew in size, it became a challenging task to handle such a significantly large amount of data. Key features like high data velocity, data variety, data volume and data complexity are few important reasons which the traditional database systems failed to handle successfully. As a result NoSQL came into

With the appearance of Big Data, there was clearly a need for more flexible databases. In this paper, we will review one of the graph database (Neo4j), and compared it with one of the traditional relational databases (MySQL) based on the features like ACID, replication, and the language that is used for both of them. MySQL is being another name for Relational Databases and it has been used for a long time period until now. And Neo4j which is a graph database and it is a part of the emerging technology that is called NoSQL is now trying to prove that there is a need for NoSQL usage.

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

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.

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

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.

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:

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.