Big DataBig Data is a popular phrase used to describe a huge amount of structured and unstructured data. Big data is difficult to process with traditional database and software techniques due to the large amount of data. Volume, velocity, variability and variety are three characteristics of Big Data. • Volume: Big Data involves huge volumes of data. This data is generated by machines, networks and social media, and the volume of data to be analyzed is enormous. Volume refers to the amount of data to be handled. Many organizations produce large amounts of data internally or externally. • Velocity: Velocity refers to the speed of data generation, or how quickly data is generated and processed to achieve its objectives. The data flow is massive and continuous. • Variety: Organizations collect a variety of data in different ways. The data collected using internally or externally can be structured, semi-structured or unstructured. For example, social media sources, such as Facebook, blogs, and tweets, and sensor data can be semi-structured or unstructured. This variety of unstructured data creates problems for data storage and analysis.[5]Big data is important because it allows large quantities of raw data to be analyzed where it was not practical, for reasons of cost or technology. Big data is the term for a collection of data sets that are so large and complex that they become difficult to process using available database management tools or traditional data processing applications. Big Data differs from traditional transactional data in several ways. Whether it's volume or storage issues, Big Data is often not relational (some of the more structured data can easily be put into a relational format but unstructured... middle of paper... requires huge performance and scalability. But old platforms have poor scalability, data loading, responsiveness, processing capacity for analyzing and managing concurrent mixed workloads The storage and analytics approach and the analytics and storage approach can be identified as the two main techniques for big data analytics. Big data analytics helps explore hidden correlations, hidden patterns, and provide other valuable data insights. This analytics helps data scientists and other users evaluate large volumes of data, which may be left untapped by traditional business intelligence (BI) systems that can provide competitive benefits for organizations. It also helps achieve business benefits such as more effective marketing and increased revenue. The main goal of Big Data analytics is to help organizations make superior business decisions. [8]