The term “Big Data” has been used to describe data sets that are so large that typical and traditional means of data storage, management, search, analytics, and other processing has become a challenge.Big Data is characterized by the magnitude of digital information that can come from many sources and data formats (structured and unstructured), and data that can be processed and analyzed to find insights and patterns used to make informed decisions.
IBM states that 90 percent of the digital data in the world was created in the past two years alone. Organizations are collecting, producing, and storing this data, which can be a strategic resource.
Analyzing Big Data requires lots of storage and large computations that demand a great deal of processing power.
Hadoop For Big Data Challenge:Hadoop meets the challenges of Big Data by simplifying the implementation of data-intensive, highly parallel distributed applications.It allows analytical tasks to be divided into fragments of work and distributed over thousands of computers, providing fast analytics time and distributed storage of large amounts of data.
Hadoop provides a cost-effective way for storing huge quantities of data and hadoop is a scalable and reliable platform for processing large amounts of data over a cluster of commodity hardware. And it provides new and improved analysis techniques that enable sophisticated analytical processing of multi-structured data.