A Literary Review on Big Data & Hadoop

This report file is prepared on the topic Big Data Analytics and Hadoop; it has been tried to elucidate all the relevant details to the topic to be included in the report. In the beginning this report gives an overall view about this topic. 'Big Data' is the data but with a huge size. 'Big Data' is used to explain the collection of data which is huge in size and still growing exponentially with respect to time. Basically, this data is so large and complex as none of the traditional data management tools can be store it or process it efficiently and perfectly. Big data analytics gives permission to data scientists and various other users to calculate large volumes of transaction data and other sources of data that traditional business systems are unable to handle. Modern software programs that are used for big data analytics, while the unstructured data used in big data analytics may not be applicable to conventional data warehouses. Requirements which are high in processing associated with Big data may also make traditional data warehousing a poor fit. As a result, newer, bigger data analytics environments and technologies have emerged, including Hadoop, MapReduce and NoSQL databases. These technologies make up an open-source software framework that's used to process huge data sets over clustered systems. Hadoop is an open source distributed processing framework that manages data processing and storage for big data applications running in clustered systems, formerly known as Apache Hadoop. The technology is developed as part of an open source project within the Apache Software Foundation (ASF). Big Data is nothing but a concept which describes how to handle large amount of datasets. Hadoop is just a single framework out of dozens of tools. Initially Hadoop is used for batch processing technology. The difference between big data and the open source software Hadoop is a distinct and fundamental one.