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Data processing in big data implementing map reduce framework
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This framework aims to get superior result of minimize overall query load time and processing time of big data and increasing the scalability, robustness and takes better CPU Usage options in user and administrator issues into considerations. It is performed simulation process in real time environment for Big Data Applications.Today the sources and services to be had in the internet are growing rapidly day by day, this massive data.traditional datasets in rows and columns like DBMS tables, XML Data files and Unstructured Data like e-mail attachments, manuals, images, PDF documents, medical rec...
This framework aims to get superior result of minimize overall query load time and processing time of big data and increasing the scalability, robustness and takes better CPU Usage options in user and administrator issues into considerations. It is performed simulation process in real time environment for Big Data Applications.Today the sources and services to be had in the internet are growing rapidly day by day, this massive data.traditional datasets in rows and columns like DBMS tables, XML Data files and Unstructured Data like e-mail attachments, manuals, images, PDF documents, medical records such as x-rays, ECG (Electro Cardio Gram) and MRI (Magnetic Resonance Imaging) images, forms, rich media like graphics, video and audio, contacts, forms and documents. This data is classified as "huge information" due to its sheer extent, range, velocity and veracity. It is tough to discover a answer for the massive data storage and its get admission to and visualization trouble. There's a need to increase answers to manipulate big amounts of facts on a everyday foundation and extract new understanding from them. Big data storage methods are different from conventional garage strategies.
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