Abstract-The increasing amount of data and a need to analyze the given data in a timely manner for multiple purposes has created a serious barrier in the big data analysis process. This article describes the challenges that big data creates at each step of the big data analysis process. These problems include typical analytical problems as well as the most uncommon challenges that are futuristic for the big data only. The article breaks down problems for each step of the big data analysis process and discusses these problems separately at each stage. It also offers some simplistic ways to solve these problems.

This work is dedicated to formation of data warehouse for processing of a large volume of registration data of domain names. Data cleaning is applied in order to increase the effectiveness of decision making support. Data cleaning is ap- plied in warehouses for detection and deletion of errors, discrepancy in data in order to improve their quality. For this purpose, fuzzy record comparison algorithms are for clearing of registration data of domain names reviewed in this work. Also, identification method of domain names registration data for data warehouse formation is proposed. Deci- sion making algorithms for identification of registration data are implemented in DRRacket and Python.

Abstract-The increasing amount of data and a need to analyze the given data in a timely manner for multiple purposes has created a serious barrier in the big data analysis process. This article describes the challenges that big data creates at each step of the big data analysis process. These problems include typical analytical problems as well as the most uncommon challenges that are futuristic for the big data only. The article breaks down problems for each step of the big data analysis process and discusses these problems separately at each stage. It also offers some simplistic ways to solve these problems.

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