The Future of Big Data - Paper Example

Introduction

In modern society, we live in an age of information explosion. There are massive volumes of data which have become accessible for decision and opinion makers. Big data denotes merely to datasets whose cost is away from the ability and essential database tools which are used to analyze, manage, capture and store. It entails how large a dataset needs significant data classification. Big data are datasets that grow so huge and makes it difficult for experts to handle by implementing the use of traditional tools and techniques. There is need to do more research and come up with solutions that would help in handling and derive knowledge from these datasets.

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Madden (2012) says that through data analytics, people can extract essential and unknown patterns, relationships, and a lot of information. Much of these are collected from social media profiles, browsing history, smartphones and another whole bunch of resources. Previous data are by decision makers to gain competitive advantage. Data gathering is also a method that uses unsubstantiated education to discover unidentified arrangements. It is a process of aligning sets of entities together in classes based on similarities and behaviors of the set. For instance, people share over 350 million photos on Facebook on a daily basis. There is a possibility of connecting all profiles to make one volume of a database concerning an individual. In the future, people will be in a position to analyze the past behaviors, and this will help them to predict the future. Big data comes with a lot of benefits, and these may only be useful if used in a manner that people do not abuse who have information about others.

What is NoSQL?

It is a method to databases that symbolises a shift away from the out-dated relational database administration systems. Understanding NoSQL requires the understanding of SQL. SQL is a query language that the relational database management system use. These rely on tables, rows, columns, and others to organize and retrieve data. The aim of NoSQL is to offer scalability, performance and high convenience (Li & Manoharan, 2013). The NoSQL data management systems can be distributed into the key-value store, tabular and document-oriented databases.

This kind of database lacks no joins support, no complex support, and no constraints support. These have to be implemented at the database level. These limitations exist because the focus is to provide performance and scalability. NoSQL databases offer high performance and high functionality. RDBMS, on the other hand, offers low performance and low functionality compared to NoSQL. It should not be used when handling complex transactions.

What is Hadoop?

A large percentage of worldwide data has been collected over the last two years. It is possible because on managing big data, Facebook and Google are in a position to deal with significant data. Hadoop is an open basis software structure used for storage of data and operating applications on bunches of product hardware. It enables applications to process data in parallel rather than in serial. According to De Mauro, Greco and Grimaldi (2015), there are two ingredients that companies can implement in investigating Hadoop. The first one is big data, say, ten TB and statistical simulations. They help in processing data faster such as statistical analysis, ETL processing, and business intelligence.

As a result, Hadoop is essential in managing big data because it helps in storing and processing vast amounts of any data at a fast rate. Hadoop also increases computing power. It's computing prototypical procedures big data so fast. The more computing power one uses, the more dispensation power is obtained. The database is also flexible since you don't need to process data before storing it. Hadoop also enables scalability, and it requires little administration.

VMware NSXThis is the network virtualization arena for the software-defined data center. The system entrenches networking and safety functionality which is controlled in hardware openly into the hypervisor. The system virtualization alters the data center's network functioning model, and this helps customers to realize the full potential of SDDC. The network also helps in reproducing the whole networking atmosphere. It also provides a complete set of logical networking elements and services which includes rational swapping, capacity balancing, monitoring, VPN, firewalling, QoS and routing. VMware NSX moves networking to software and creates new levels of flexibility.

Network Virtualization

Network virtualization refers to a construct layer that decouples the physical hardware from operating system to deliver superior IT resource deployment and flexibility. It permits several virtual technologies with assorted operating systems such as Windows and Linux and mobile applications to run in separation, alongside each other on the identical physical machine. It also refers to the capacity to generate reasonable, virtual networks decoupled from the fundamental network hardware to safeguard the system can enhance with and support virtual atmospheres.

Spring for Apache HadoopSpring for Apache Hadoop offers sustenance for emerging applications founded on Apache Hadoop technologies. It is accomplished by leveraging the competencies of the spring network. It streamlines increasing Apache Hadoop by providing a combined alignment model and API's that are easy to use. The system also includes incorporation with other spring ecosystem projects such as Spring Integration and Spring Batch. These enable one to develop solutions for big data ingest and Hadoop workflow adaptation.

Reflection

The fact that data mining is just one portion of big data concept, these skills are relevant and remain in the current society. The primary reason behind this fact is that big data creates an opportunity for the economies of the world to not only enhance their security systems but also progress in areas ranging from marketing and credit risk analysis and urban planning. However, there is need to provide the balance between the privacy risks and big data rewards, and this is one of the most significant challenges that data mining and significant data encounter. Madden (2012) assert that these skills are necessary because they impact companies differently. Internet and other modes of modern communication are opening up ways that are vital for obtaining more information. Data scientists with higher data mining skills have higher project success and satisfaction.

Privacy with Data Mining.Privacy and legal issues are important aspects of data mining that may result in a growing conflict. Data mining is significant since it can be used to raise the problems regarding privacy. Consumers might not be aware of whether their information is being collected or not, and shared with third parties (Imtiyaj, 2015). Data mining can be used to extract information from the databases, and in most cases, this usually subject the consumer information and privacy at risk. There is a need for enacting legislation or acts that prevent the use of consumers' data for data mining without their acknowledgment.

Big Data Blues: The Dangers of Data Mining

The statement that online finger-wagging, lawsuits and discontented customers are the ill-fated by-products of whatever individuals may remark to be significant data manipulations may be right. Data mining is dangerous because poor quality and dirty data characterize it. If there is garbage in the database, there is no need for making decisions based on it. Similarly, companies can undertake a lot of expensive data mining work, but the information might not be significant.

References

De Mauro, A., Greco, M., & Grimaldi, M. (2015, February). What is big data? A consensual definition and a review of key research topics. In AIP conference proceedings (Vol. 1644, No. 1, pp. 97-104). AIP.

Imtiyaj, S. (2015). Privacy Preserving Data Mining. transactions, 2(2).

Li, Y., & Manoharan, S. (2013, August). A performance comparison of SQL and NoSQL databases. In Communications, computers and signal processing (PACRIM), 2013 IEEE pacific rim conference on (pp. 15-19). IEEE.

Madden, S. (2012). From databases to big data. IEEE Internet Computing, 16(3), 4-6.

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