Relational Database Management Systems ( Rdbms )

As a result of the appearance of big data in our world, conventional data warehousing and data analysis methods no longer have the process power needed. What is Big Data you may ask and why is it such a big deal. NIST defines big data as anywhere “[…] data volume, acquisition velocity, or data representation limits the ability to perform effective analysis using traditional relational approaches […]” (Mell & Cooper, n.d.).

Data, Data everywhere. It is a precious thing that will last longer than the systems. In this challenging world, there is a high demand to work efficiently without risk of losing any tiny information which might be very important in future. Hence there is need to create large volumes of data which needs to be stored and explored for future analysis. I am always fascinated to know how this large amount of data is handled, stored in databases and manipulated to extract useful information. A raw data is like an unpolished diamond, its value is known only after it is polished. Similarly, the value of data is understood only after a proper meaning is brought out of it, this is known as Data Mining.

In 1999,Steve Bryson, David Kenwright, Michael Cox, David Ellsworth, Robert Haimes published Visually exploring gigabyte data sets in real time, which is the first CACM article that uses the term “Big Data” [1]. Big Data defines the data sets with gazillion information that cannot be crawled, managed, or processed by traditional tools in a certain amount of time. It also represents the techniques to extract valuable information quickly from various information of large-amount data. Some common technologies that are applicable to big data are massively parallel processor (MPP) database, data mining, distributed file system, distributed database, cloud computing, network, and extensible memory system.

"Such ‘Data Explosions ' has led to one of the most challenging research issues of the current Information and Communication Technology (ICT) era: how to effectively and optimally manage such large amount of data and identify new ways to analyze large amounts of data for unlocking information. The issue is also known as the ‘Big Data ' problem, which is defined as the practice of collecting complex data sets so large that it becomes difficult to analyze and interpret manually or using on-hand data management applications. From the perspective of real-world applications, the Big Data problem has also become a common phenomenon in domain of science, medicine, engineering, and commerce"

ABSTRACT:-“ Instead of relying on expensive, proprietary hardware and different systems to store and process data, enables distributed parallel processing of huge amounts of data across inexpensive, industry-standard servers that both store and process the data, and can scale without limits. With no data is too big. And in today’s hyper-connected world where more and more data is being created every day, breakthrough advantages mean that businesses and organizations can now find value in data that was recently considered useless.

Big Data is termed extremely large datasets that their sizes are beyond the ability of capturing, managing, and

of data ,but with this problem of handling such a large amount of data also come . Companies

RDBMS are intolerable for large data volumes. NoSQL distributed databases, allow data to be spread across thousands of servers and can store large volume of data.

Tom Davenport, an author specializing in business intelligence, analytics and business process innovation, defines big data in his recently authored book “Big Data at Work: Dispelling the Myths, Uncovering the Opportunities” as “The broad range of new and massive data types that have appeared over the last decade or so.”

Big Data is a newer term that has been introduced to the technology world. By definition, the term “Big Data” refers to large amounts of complex sets of data, their relationships and their analysis. (Electronic Privacy Info Center). It can also be defined as a “collection of data from traditional and

The term big data came into the picture to refer the big volumes of information’s both the companies and governments are storing. The data may be where we live, where we go, what we buy and what we say etc. all will be recorded and stored forever. More than 90% of data is generated in the past 2 years only and this volume is increasing day by day and doubling for every two years. In this world, the organizations are using the data generated by us and no one knows what they are doing with the collected data. Big data is defined as a lot of structured and unstructured data from different sources, such as E-commerce websites, online transactions, social networks, medical records, internet search indexes, banking and financial services, scientific searches, weblogs, and document searches and so on. Big data also can be described by four V’s: Volume, Velocity, Variety and finally Value.

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

Apache Cassandra is an open source distributed database management framework intended to handle a lot of information crosswise over numerous product servers, giving high accessibility. The single point of failure is eliminated in Cassandra.

Data has always been analyzed within companies and used to help benefit the future of businesses. However, the evolution of how the data stored, combined, analyzed and used to predict the pattern and tendencies of consumers has evolved as technology has seen numerous advancements throughout the past century. In the 1900s databases began as “computer hard disks” and in 1965, after many other discoveries including voice recognition, “the US Government plans the world’s first data center to store 742 million tax returns and 175 million sets of fingerprints on magnetic tape.” The evolution of data and how it evolved into forming large databases continues in 1991 when the internet began to pop up and “digital storage became more cost effective than paper. And with the constant increase of the data supplied digitally, Hadoop was created in 2005 and from that point forward there was “14.7 Exabytes of new information are produced this year" and this number is rapidly increasing with a lot of mobile devices the people in our society have today (Marr). The evolution of the internet and then the expansion of the number of mobile devices society has access to today led data to evolve and companies now need large central Database management systems in order to run an efficient and a successful business.

The internet of Things (IOT) is an important part of new generation of information technology and known as an important stage of development “information” age. (Ashton, 2009). As the name suggest, IOT means the materials, objects, animals or people which are connected to the internet without human-computer