Big Data Management From Relational Database Management

Every day, we produce 2.5 quintillion bytes of data. 90% of all data in the world was produced in the past two years. Data has been around forever; we have always gathered information. Paleolithic cavemen recorded their activities by carving them in stone or notching them in sticks. Egyptians used hieroglyphics to record significant events in history. The Library of Alexandria was home to half-a-million scrolls of the ancient world. Less than hundred years ago, we used punch cards to record and store information. As technology continues to evolve, the amount of data we store continues to grow. We’ve come a long way since stone tablets, scrolls, and punch cards. It’s important to understand the concept of big data and the impact is has created. This paper will define the classifications of data, explain the challenges of big data, and describe how big data analytics is being used in today’s data driven world.

The author points out that although there are existing algorithms and tools available to handle Big Data, they are not sufficient as the volume of data is exponentially increasing every day. To show the usefulness of Big Data mining, the author highlighted the work done by United Nations. In order to further enhance the reader’s perspective, the author provided research work of various professionals to educate its readers about the most recent updates in Big Data mining field. The author further describes the controversies surrounding Big Data. The author has first provided the context and exigence by elaborating on why we need new algorithm and tools to explore the Big Data. The author used the strategy of highlighting the logos by mentioning the research work of different industry professionals, workshops conducted on Big Data and was able to appeal to connect to the reader’s ethos. The author also used pathos by urging the budding Big Data researchers to further dig deep into the topic and explore this area

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

The amount of data in our world has been rapidly increasing and analyzing these large data sets, or big data, has become crucial for businesses in increasing their success. Many businesses use big data to model their business structures, control processes, and run the business. The availability of this data leads to a more accurate analysis of the target market. More accurate analyses lead to more confident decision making and better decisions means greater operational efficiencies, cost reductions and reduced risk. There are many ways in which big data can be successfully implemented in an organization. Big data allows businesses to segment their target market, creating more precisely tailored products and services. Big data is also used to conduct controlled experiments to make better management decisions. Finally, big data can unlock value by making the captured information transparent and usable at much higher frequency (Manyika, “Big data: The next frontier for innovation, competition, and productivity”).

Abstract - Considered as another subversive technology revolution in IT other than Internet of Things and cloud computing, Big Data has the most valuable property. Its value hides in the great storage that needs to be analyzed while cloud computing merely serves as a method or a step to save and store the messages. As rapidly increasing attention is drawn on in recent years, Big Data has wider and wider influences in many fields. With the explosive demands on Big Data analytics, Big Data broadened its definition from an IT term of extremely large data sets to a set of new technologies.

“Why Big-Data Is a Big Deal”. Big-data is a logo used to describe a massive volume of both structured (is information already managed by the organization in relational databases ) and unstructured data (is information that is unorganized and does not fall into a pre-determined model) that is so large it is difficult to process using traditional database and software techniques. In most companies, the volume of data is too big or it moves too fast or it exceeds current processing capacity. Despite these problems, big-data has the potential to help companies improve operations and make faster, more intelligent decisions.

Big data is an extensive collection of structured and unstructured data. It is a modern day technology which is applied to store, manage and analyze data that are not possible to manage, store and analyze by using the commonly used software or tools. Since all of our daily tasks are overtaken by the modern technologies and all the businesses and organizations are using internet system to operate, the production of data has increased significantly in past

Big data is not a hype, but it is the future. The big data industry continues to advance, and big data service providers are making it easier for companies to work with big data in driving their businesses. Progressively, greater volumes and varieties of data will be incorporated with more business processes to support better decision making and greater insight. Moreover,

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

Data itself is useless, until it is mined and transformed into a valuable source of knowledge discovery. Due to its conversion into useful information, data mining has become the leading source being used in many fields worldwide. “Data mining is based on complex algorithms that allow for the segmentation of data to identify patterns and trends, detect anomalies, and predict the probability of various situational outcomes.”[1] Many organizations from healthcare to multimedia and more are relaying on data and getting developed through the use of it. Regardless of how, data warehouse changed its rhythm and dimension in terms of measurements such as: variety, volume and velocity. Today, one can see the current trends of data mining in different fields such as social networks, healthcare and businesses. As data mining is giving the opportunity for those fields to get advanced, "Big Data" is also opening up new doors within itself as the new trends emerge.

Data is a powerful weapon as well as a resource. Having data does not make you powerful but what you do with it makes all the difference. Companies like Amazon, eBay and Netflix are already using data to predict user behavior and utilizing that to increase their revenue. But processing data in real time is not an easy task. The data today has great volume, is veracious in nature and is increasing at an enormous rate and hence has been given the term Big Data. There is a constant research going on to find a solution to process such huge amount of data in real time.

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.

Abstract-The amount of data being generated nowadays has increased tremendously. With these large volumes of data, there is a need for efficient storage and processing of data. The data generated from a variety of sources like Social networking sites, Emails, audio and video files, text files and other various files is unstructured. The traditional database cannot handle this complex and high volumes of data efficiently. The Hadoop framework provides an effective solution for storing large volumes of data and processing the data for analysis. This paper deals about the Traditional Databases, Big data, Data analysis and how effectively Hadoop stores these large volumes of data and processes data for analysis. Furthermore, this paper also focuses on the various technologies used in Hadoop for the purpose of storing the data and for processing the data.

In this paper, we will review one of the graph database (Neo4j), which the graph database is part of the emerging technology that is called NoSQL and compared it with one of the traditional relational databases (MySQL). MySQL, it is being another name for Relational Databases and it has been used for a long time period until now. However, with the emergence of Big Data there was clearly a need for more flexible databases. Facebook 's Graph Search use Neo4j, a graph database, is an application which clearly displays how relationships need to be modeled in a more efficient and sophisticated manner than using conventional relational models. In this paper, we will make a comparison between MySQL and Neo4j based on the features like ACID, replication, availability and the language that is used in both of them.

Management of big data is useful with how it is used. Ways to use the stored information include, but not limited to, reduction of costs, time reductions, and making smart decisions based on data results. [1]