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Engineering Science Reference Big Data Processing With Hadoop - Data Mining Reference

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Description

Big Data Processing With Hadoop is an essential reference source that addresses the growing challenge of analysing massive datasets in today's internet-driven world. As user numbers and multimedia applications continue to expand, organisations face unprecedented demands for processing speed and efficiency. This comprehensive guide discusses practical solutions for professionals managing diverse data applications who require fast turnaround responses whilst navigating complex processing obstacles. Whether you're working with enterprise databases, large-scale analytics, or distributed computing environments, this resource from Engineering Science Reference provides the knowledge needed to implement Hadoop-based solutions effectively. Part of the Advances in Data Mining and Database Management series, it bridges the gap between theory and real-world implementation for anyone seeking to optimise their data infrastructure and overcome scalability challenges.

Key Features

Big Data Processing With Hadoop covers essential information systems strategies for organisations managing millions of users and terabyte-scale datasets.

The Engineering Science Reference guide addresses common processing bottlenecks and turnaround time issues in large-scale data applications.

This reference source provides practical solutions for implementing Hadoop and distributed computing frameworks across diverse data mining scenarios.

Part of the Advances in Data Mining and Database Management series, offering comprehensive coverage of modern data storage and analysis techniques.

Designed for professionals, database administrators and data engineers who need to understand scalable approaches to information processing.

Covers solutions for multimedia-based applications and internet services requiring efficient handling of large user bases and high-volume data streams.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
16 November 2018
Listed Since
24 February 2020

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