Artificial intelligence (AI), mobile devices, social and the Internet of Things (IoT) are driving data complexity, new forms and sources of data. Big Data analytics is the use of advanced analytical techniques on very large and diverse data sets that include structured, semi-structured and unstructured data, from different sources and in different sizes, from terabytes to zettabytes. Big Data is a term applied to data sets whose size or type exceeds the ability of traditional relational databases to capture, manage, and process data with low latency. And it has one or more of the following characteristics: high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, the web and social media, much of it generated in real time and at scale. Big data analytics allows analysts, researchers, and companies to enable users to make better, faster decisions using data that was previously inaccessible or unusable. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, companies can analyze previously untapped data sources independently or together with existing business data to gain new insights that lead to better, faster decisions. Quora and Facebook use big data tools to understand more about you and provide you with a feed that you should theoretically find interesting. The fact that the feed is uninteresting should demonstrate how difficult the problem is. Credit card companies analyze millions of transactions to find patterns of fraud. Maybe if I bought Pepsi on paper followed by a big ticket purchase, it could be a scammer? Big data describes a holistic information management strategy that includes and integrates many new types of data and data management alongside traditional data. Big data has also been defined by the four V:Volumes. The amount of data. While volume indicates more data, it is the granular nature of the data that is unique. Big Data requires processing high volumes of low-density, unstructured Hadoop data, that is, data of unknown value, such as Twitter data feeds, clickstreams on a web page and mobile app, network traffic, equipment equipped with sensors that acquire data at the speed of light and much more. It is big data's job to convert that Hadoop data into valuable information. For some organizations this could be tens of terabytes, for others hundreds of petabytes.Velocity. The fast rate at which data is received and perhaps used. Higher speed data is normally transmitted directly into memory rather than being written to disk. Some Internet of Things (IoT) applications have health and safety implications that require real-time assessments and actions. Other Internet-enabled smart products operate in real time or near real time. For example, consumer eCommerce applications try to combine mobile device location and personal preferences to create time-sensitive marketing offers. From an operational perspective, mobile application experiences feature large numbers of users, increased network traffic, and the expectation of immediate response. Variety. New unstructured data types. Unstructured and semi-structured data types, such as text, audio, and video, require additional processing to derive both meaning and insights..