What is Big Data Analytics?
Big data analytics refers to the strategy of analyzing large amounts of data, or big data. This big data comes from a variety of sources including social networks, videos, digital images, sensors, and sales transaction records. The goal in analyzing all of this data is to uncover patterns and connections that would otherwise be invisible and that could provide valuable insights about the users who created them. With this insight, companies can gain an edge over their competitors and make better business decisions.
Big data analytics enables data scientists and various other users to evaluate large amounts of transactional data and other data sources that traditional business systems could not handle. Traditional systems can fail because they cannot analyze that many data sources.
Sophisticated software programs are used for big data analysis, but the unstructured data used in big data analysis may not lend itself well to traditional data warehouses. The high processing requirements of big data can also make conventional data warehousing solutions a poor fit. As a result, newer, larger data analysis environments and technologies have emerged, including Hadoop, MapReduce, and NoSQL databases. These technologies form an open source software framework with which large amounts of data are processed via cluster systems.