Essay Example on Big Data analytics has been remained a Backbone








Big Data analytics has been remained a backbone and mainstay of many enterprises most especially Telecommunication and Information Technology enterprises Storage processing extraction as well as the utilization of the insight from this data has been of immense contribution to the enterprise operations In previous times when many Information technology systems existed as disparate systems in silos and not interconnected data analysis and consumption at just one system was rampant However with the advent of the internet there arose urgent need and requirement for data and the processed information to be shared across systems This necessitated the birth of the ETL Extract Transform and Load in Business Intelligence where data transformation and reloading as well as re use was made possible Telecommunication companies as a result made huge investments in this ETL technology spanning across data warehousing staff software and hardware A huge volume of data generation occurs daily with the advent of smart devices leveraging on digital technologies on the telecommunication operators network network probes click streams for browsing sessions and call detail records are generated daily on the network Growth in Internet of Things and advanced communication technologies and digital sensors also is seeing an increasing volume of data to be analyzed for valuable insights for the enterprise on the cellular operator s network There are challenges in utilizing the traditional mode of processing data to analyze this big data as it requires great parallel processing

There was need for technology that can readily store analyze and process petabyte sized data without depending on massive investments in enterprise hardware and storage for data warehousing as it currently obtains for telecommunication operators Respite for this challenge is being provided by the open source technologies driving big data analytics providing the capability for usage of thousands of commodity servers for parallel processing of huge data on the telecommunication operators network while being computation to the data The architecture of this technology which revolves around the Apache Hadoop framework and the architecture of how it can be properly deployed to optimally handle the big data analytics need of the telecommunications operator in comparison with the current conventional relational database management systems RDBMS and how this will contribute an edge to the telecommunication operators is researched in this report Chapter on of this thesis is the Introduction of the thesis It provides an introduction into big data analytics as well as background into the research It also clearly states what the problems statement for the thesis is and as well states the contribution of the thesis The chapter two details the review of literature on big data analytics techniques and also the sources and trends of big data telecommunication industry It also dives into the conventional existing and also the emerging new technologies in big data analytics as well as the applications of these new technologies 

Chapter three provides a description of the methodology and the method adopted for the purpose of this research study It also introduces the data set used the source and its validity It provides insight into the description and the relevance to this research study Chapter four provides details into the implementation of the experiment the use of the conventional RDBMS and emerging Hadoop technologies in analyzing the data sets using different metrics to measure the performance Chapter five introduces the results of the experiment using the recorded response time for the metrics used in the analysis these were recorded in tables and visual presentation in form of charts for comparison Chapter six is the discussion of the result and description of how it provides insight and answers to the research question It details the interpretation of the result of comparison between big data analytics using conventional RDBMS as well as Hadoop technologies and how this can be applied to big data analytics in the telecommunication industry it also provides a conclusion to the thesis based on the discussed result as well as recommendation for future work

With current advances of telecommunication technology and ever increasing mobile technology cellular networks have turned out to be each huge generator and host of big data Liu Liu and Ansari 2014 During the geolocation of mobile gadgets recording smartphone calls and recording activities of mobile applications a sizable volume of data is generated and sustained on the operators networks In time past this huge data on the mobile operators networks weren't given much attention and sometimes discarded With the accumulation of huge data consistently on the mobile operators network and consequent upon the quick evolution of big data analytics the outstanding benefit and value hidden in the huge data is now been revealed It s of huge benefits to utilize the insights from these data to enhance and optimize the overall performance of operators cellular networks as well as maximize operators revenue Bi et al 2015 

Conventional data analytic using RDBMS Relational Database Management Systems had revealed its inadequacy in handling big data on the cellular operators network Firstly conventional data analytic handles analysis and processing of well structured data only Ironically the massive amount of system application primarily based data output however is typically unstructured Secondly data analysis implementation is historically done within a department in the organization or an enterprise unit after which analytical conclusions are derived from very restricted angles or in silos instead of from a larger global perspectives Thirdly the analytics in particular narrows and focuses on transaction data mostly whereby less emphasis and effort is paid to operational data as a result of its inadequacy in making a real time selections

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