bIG dATA & hADOOP
The difference between big data & Hadoop is a distinct and fundamental one. The former is an asset, often a complex and ambiguous one, while the latter is a Open source software program that accomplishes a set of goals and objectives for dealing with that asset.
Big data is simply the large sets of data that Enterprises or businesses and other parties put together to serve specific goals & operations. Big data can include many different types or kinds of data in many different type or kinds of formats.
Hadoop is one of the tools that is designed to handle big data. Hadoop and other software products work to interpret or parse the results of big data searches through specific proprietary algorithms & methods. Hadoop is an open-source program under the Apache license that is maintained by a global community of users. It includes various main components, including a MapReduce set of functions and a Hadoop distributed file system (HDFS).
The Hadoop Distributed File System (HDFS) is a distributed file system that runs on standard or low-end hardware. Developed by Apache Hadoop, HDFS works like a standard distributed file system but provides better data throughput and access through the MapReduce algorithm, high fault tolerance and native support of large data sets. The idea behind MapReduce is that Hadoop can first map a large data set, and then perform a reduction on that content for specific results. A reduce function can be thought of as a kind of filter for raw data. The HDFS system then acts to distribute data across a network or migrate it as necessary.
Database administrators, developers and others can use the various features of Hadoop to deal with big data in any number of ways. For example, Hadoop can be used to pursue data strategies like clustering and targeting with non-uniform data, or data that doesn't fit neatly into a traditional table or respond well to simple queries.
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