ANALYSIS OF BIG DATA?

Big Data is data that either is too large, grows too fast, or does not fit into traditional architectures. Within such data can be valuable information that can be discovered through data analysis [1]. Big data is a collection of complex and large data sets that are difficult to process and mine for patterns and knowledge using traditional database management tools or data processing and mining systems. Big Data is data whose scale, diversity and complexity require new architecture, techniques, algorithms and analytics to manage it and extract value and hidden knowledge from it. Big Data includes structured, semi-structured and unstructured data such as call logs, mobile-banking transactions, online user-generated content such as blog posts and Tweets, online searches, satellite images, etc. As the size of data increases, the amount of irrelevant data usually increases as well and the process becomes impractical. Hence, in such cases, the analyst must be capable of focusing on the informational parts while ignoring the noise data. These kinds of difficulties complicate the analysis of multichannel data as compared to the analysis of single-channel data. In this paper, we examine the current trends and characteristics of Big Data, its analysis and how these are presenting challenges in data collection, storage and management [1], HACE theorem that characterizes the features of the Big Data revolution.