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Topic- literature review on the same correction topic 5 pages excluding references.

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Topic provided previously on the below one only need literature review

Big Data

With the advancement in technology, a lot of information termed Big Data circulates and is important in our decisions. The more informed people are, the better decisions they make. This essay will involve a description of Big Data and highlight the areas that have been influenced by the large scale data.

The term big data can be defined as a large volume of information. This information or data set can either be structured or unstructured and is continually generated and relayed from various sources. Big Data may be acquired from websites for E-commerce, transactions done online, social media networks, medical records, indexes for internet searches, banking and financial services, scientific searches, website blogs, and document searches (Lee, Wei, Mukhiya2018). Big Data is characterized by a high volume of information, high-speed processing, and a wide variety.

Based on their size and compressibility, the traditional analysis software cannot analyze the data, and this prompted advancement in technology to deal with such enormous volumes of information. (Hussain, Roy2016). Companies mainly use this data to give a detailed view of their customer preferences and comments on their products. With the rising competition in business and the markets, managers in organizations often use data to decide how to do their business planning and maximize profits. Therefore a better definition of big data can be the realization of great business intelligence that stores large volumes of data, process it accurately, and analyze the processed information that was not done by traditional statistical software.

As the term itself entails, big data involves dealing with a lot of data. This comes with a new challenge because this data storage requires large volumes of space. To address the problems of analyzing these large volumes of data, various methods have been adopted to solve them. The first method is sampling, which includes picking a few bits or samples of data from a data set and use it to come up with conclusions and findings. Another method for this is data condensation. The software can reduce the data size to fit the storage space available. It also enables fast retrieval of such data when needed (Yu, Guo2016). Other methods, such as density-based approaches, grid-based approaches, incremental learning, and distributed computing, have been applied. With the above methods, it is easier to analyze the large volumes of data efficiently and within the required time.

Storage of large-scale data has become a priority, and economic tools have been provided to achieve this. Big data tools like cloud storage are cost-effective when storing big data (Hussain, Roy2016). Tools like Hadoop work at high speed when analyzing data promotes faster and effective decision making. Though the continuous development and technological improvement of computer systems and information technologies have influenced the improvement of computer hardware regarding Moore's law for the past few years, handling of large-scale data is a problem that is still existing as we get to the era of Big Data. That was part of why Kashyap made a statement that big data is the data that cannot be handled and processed by most current information systems or methods because data in the Big Data era will be too bulky to contain in few computer systems(Kashyap,2019).

A researcher needs to apply Big Data analytics for an online banking project in a scenario where one would acquire records for transactions from banks and securities exchange firms. The analysis of this data would help determine peak transaction hours, major causes of delay, among other factors that help improve service delivery. This application of Big Data analytics is important in making business decisions. The data can then be stored in the Cloud for easier retrieval once required.