Assignment on Big Data Analytics

Introduction of Big Data Analytics

            Big data analytics is one of the most complex processes of the evaluation or analysis of a large number of data as well as it comprises a larger number of datasets to uncover important information such as the market trends, customer correlation, hidden patterns as well as customer correlations to provide help organization to make effective decisions. The main purpose of this study is to provide information about the recent developments of big data and how it is beneficial in businesses. The study is the state of the art of big data analytics which is providing very important information related to big data in two primary perspectives such as applications and technology. The technical review of big data analytics is providing information about the technical aspects as well as emerging technologies. furthermore, it is also showing some significant technological advancement and trends. The application review, it is also provided information about the application of big data in different sectors. In the application review part, the example and some explanation of big data usage in the education industry are provided and also show how big data analytics can help the students in education sectors. Some challenges, as well as a recommendation, are also discussed in the study (Russom, 2011).

Technical review of Big Data Analytics

            The section is providing the technical review of the big data analytics as well as showing the technical or technological aspects of big data analytics. Furthermore, some examples related the data management, computation as well as the general purpose of the big data analytics service platforms are providing the relevant information to analyze how the data is managed as well as processed. There are several emerging technologies identified in this study that showing relevant technological advancements, as well as some important emerging trends, are discussed below.

            In the surveying aspect of big data analytics, big data application development has now become increasingly more significant in the last few decades and previous years. From different business sectors, several organizations purely depend on the knowledge obtained from a large number of data. furthermore, the platforms, as well as the traditional data techniques, are less efficient in the big data context. The slow responsiveness, accuracy performance as well as the lack of stability are shown by them. Much more work has been carried out to face the complicated challenges of big data. It has developed technologies as well as many types of distribution as a result. The stud is showing recent technologies developed by big data. The main focus of this survey is to provide information that how the technology is used for big data as well as the big data analytics has the ability to provide assistance in the business of the organizations to analyze the extracted data. furthermore, this data will provide clear insight to make effective decisions (Hilbert, 2016).

Several organizations in the world have started to adopt the optimized methods for the optimum distribution of the resources. The best implementation method has been integrating the approaches of big data analytics. Several ways to extract useful information supporting uncover patterns as well as appropriate decision making. The big data analytics technology is the combination of several processing methods as well as approaches that make efficient for collective use by different organizations to attain appropriate results for the implementation as well as strategic management. There are many techniques in which small large organizational businesses are leveraging big data. The predictive analytics can help the business and some effective tools for the business for the prevention of risks in making a decision (Ekbia, et al., 2015). The processing of big data can utilize the predictive analytics solutions related to hardware and software for the identification, deployment as well as evaluation of the predictive scenarios (Zikopoulos & Eaton, 2011). The list of the new technologies which are used for big data analytics are as following;

·         Predictive Analytics

·         NoSQL Databases

·         Knowledge discovery tools

·         Stream analytics

·         In-memory data fabric

·         Distributed storage

·         Data integration

·         Data processing

·         Data quality

Application review of Big Data Analytics

            In the application review section, some important examples of the application of big data analytics in real-life use cases are provided which are providing information that how effective the big data analytics is and where it is being used to better analyze. In this section, it is tried to provide information about how beneficial big data is by its application.

            The education sector is one of the largest educational and business sectors in the world and it is saturated with a large amount of the data related to students such as courses, faculty as well as results. It is realized that the study and analysis of this data can provide perceptions and clear insights to improve the workability of the education sector. The big data analytics has transformed some below-mentioned areas of the education sector (Addo-Tenkorang & Helo, 2016).

Tools of Big Data Analytics

There are a number of big data analytics tools such as Apache Hadoop, OpenRefine, and RapidMiner etc. They have different objectives in this case, Hadoop will be explained.

Apache Hadoop is actually a collection of open-source utilities which seem to facilitate using a network of various interconnected computers for solving problems with the use of significant amount of data computation. A software framework is provided by it for the processing of big data and distributed storage with the use of MapReduce Programming Model. A number of applications are run by Hadoop on distributed systems and it has thousands of nodes which involve pentabytes of information. In addition to it, it has a HDFS or Hadoop Distributed File System which allows and gives the capability of quick data transfer among nodes. It gives a storage layer for Hadoop which is suitable and adequate for distributed processing and storage. As the data is being stored, first, it is distributed and then it seems to proceed (Nandimath, et al., 2013).

A command line interface is provided by HDFS which enables interaction with Hadoop. Streaming access is provided by it to the file system data. Therefore, it includes authentication and file permission. Hue is an open-source interface for the analysis of data using Hadoop. Some operations which can be executed with it include uploading and browsing data, querying a table in Impala and Hive, running pig jobs and Spark, and using workflow search data. When it comes to OpenRefine, it is recognized as a data cleaning software because it helps in cleaning data for analysis. It has a number of uses such as parsing data from different websites, data transformation, and cleaning of messy data etc.

Example of Big Data Analytics

            More than 38000 students are currently studying at the University of Alabama and the university has a very large number of students and faculty data. The university can also be named as the ocean of data. In the past or previous decades, there had no kind of actual solution to analyze the data of students, may students from them seemed useless. But now the administrators of the university have the ability to utilize the analytics as well as to draw the patterns of the students, the data visualization for student’s data that is revolutionizing the retention efforts, recruitment as well as the operations of the university.

Challenges in processing and analyses of Big Data Analytics

            Some significant challenges of big data analytics processing are provided in this document below.

·         The gap in big data talent

·         Uncertainty of data management landscape

·         Collecting data within the big data platform

·         Synchronization required for the sources of data

·         Getting significant insights by using the analytics of big data

Recommendations of Big Data Analytics

            The developers and organizations should have to improve the workability as well as has to reduce the gap in the big data talent. It is a very challenging thing that collecting the uncertainty landscape of data management. The big data application should have the ability to synchronize data in the clouds and servers to make this data useful for the companies (LaValle, Lesser, Shockley, Hopkins, & Kruschwitz, 2011).

Conclusion of Big Data Analytics

It is concluded that there are several emerging technologies identified in this study that showing relevant technological advancements, as well as some important emerging trends are discussed in this document. The main focus of this survey is to provide information that how the technology is used for big data as well as the big data analytics has the ability to provide assistance. The big data analytics technology is the combination of several processing methods as well as approaches that make efficient for collective use by different organizations to attain appropriate results for the implementation as well as strategic management. The education sector is one of the largest educational and business sectors in the world and it is saturated with a large amount of the data related to students such as courses, faculty as well as results. The data which is collected from the history of the students can be very beneficial for the student's future learning as well as for the better result of the institutes. The most recent example of this benefit is the contribution of the e-learning program in the institutes.

References of Big Data Analytics

Addo-Tenkorang, R., & Helo, P. T. (2016). Big data applications in operations/supply-chain management: A literature review. Computers & Industrial Engineering, 528-543.

Ekbia, H., Mattioli, M., Kouper, I., Arave, G., Ghazinejad, A., Bowman, T., & Sugimoto, C. R. (2015). Big data, bigger dilemmas: A critical review. Journal of the Association for Information Science and Technology, 1523-1545.

Hilbert, M. (2016). Big data for development: A review of promises and challenges. Development Policy Review, 135-174.

LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT sloan management review, 52(2), 21-32.

Nandimath, J., Banerjee, E., Patil, A., Kakade, P., Vaidya, S., & Chaturvedi, D. (2013). Big data analysis using Apache Hadoop. 2013 IEEE 14th International Conference on Information Reuse & Integration (IRI), 700-703.

Russom, P. (2011). Big data analytics. TDWI best practices report, fourth quarter, 19(4), 1-34.

Zikopoulos, P., & Eaton, C. (2011). Understanding big data: Analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media.