Big Data Analytics Concepts, Technologies Challenges, and Opportunities

The rapid observed increase in using the Internet led to the presence of huge amounts of data. Traditional data technologies, techniques, and even applications cannot cope with the new data's volume, structure, and types of styles. Big data concepts come to assimilate this non-stop flooding. Big data analysis process used to jewel the useful data and exclude the other one which provides better results with minimum resource utilization, time, and cost. Feature selection principle is a traditional data dimension reduction technique, and big data analytics provided modern technologies and frameworks that feature selection can be integrated with them to provide better performance for the principle itself and help in preprocessing of big data on the other hand. The main objective of this paper is to survey the most recent research challenges for big data analysis and preprocessing processes. The analysis is carried out via acquiring data from resources, storing them, then filtered to pick up the useful ones and dismissing the unwanted ones then extracting information. Before analyzing data, it needs preparation to remove noise, fix incomplete data and put it in a suitable pattern. This is done in the preprocessing step by various models like data reduction, cleaning, normalization, preparation, integration, and transformation.