Big Data is a collection of technologies developed to store, analyze and manage this data. It is a macro tool. Today it is used in fields as diverse as medicine, agriculture, gambling and environmental protection. Machine learning, forecasting Companies use big data to streamline their marketing campaigns and techniques. Modeling and other advanced analytics applications enable big data organizations to generate valuable insights. Companies use it in machine learning. Programs cannot balance large data in any particular database. When datasets are large in size, velocity, and volume, the following three distinct dimensions constitute "big data." Examples of big data analytics include stock markets, social media platforms, and jet engines. But it is growing exponentially with time. Traditional data management tools cannot store or process it efficiently. It is a large scale technology developed by Story, Analysis and Manoj, Macro-Tool. Find patterns of exploding confusion in information about smart design, however, implying that various big data structures are structured, whether this includes the amount of information, the speed with which it is generated and collected, or the variety or scope of related data points. In the past, data was collected only from spreadsheets and databases of large, disparate information that grew at an ever-increasing rate. Very large, complex data refers to large amounts of data for sets that are impossible to analyze with traditional data processing applications. Is software configuration used to handle the problem? Apache is an advanced application for storing and processing large, complex data sets and analyzing big data. Analytical techniques against very large, heterogeneous databases containing varying amounts of data, big data analytics help businesses gain insights from today's massive data sources. Defined as software tools for processing and extraction. People, companies and more and more machines are now developing technologies to analyze more complex and large databases that cannot be handled by traditional management tools. It is designed to discover patterns of chaos that explode in information in order to design smart solutions.

A Regular busy server crashes due to negative customer traffic, and holiday interruption is being considered. If the orbit empties at the end of a positive customer service, the server worked Going on vacation. A working vacation (WV) server at a low service rate works. If there are clients on the computer at the end of each holiday, the server the probability that a new visitor is inactive and on vacation is p (single WV) or with probability q (multiple WVs). Substantial variable technique, constant state probability for the system and its orbit we found the generating function. System performance measures, reliability measures and random decay law are discussed. Finally, Some numerical examples and cost optimization analysis provided. Alternative: Single-Server Review G- Sequence, Incredible Review G-Series, Volume Visit Review G-Series. Evaluation Preference: Working vacation, Bernoulli feedback, Random vacations, single vacation. Unreliable retrial G-queue, Batch arrival Retrial G-queue, single server iteration is taken as a G-sequence alternative and working vacations, random vacations, single vacation, and Bernoulli vacation is taken evaluation parameters. In this from analysis Fuzzy ARAS method the best solution determines the solution with the shortest distance and the longest distance from the negative-best solution, but comparison of these distances is not considered significant. As a result it seems unreliable retrial G-queue got the first rank where as is the Batch arrival Retrial G-queue is having the lowest rank.