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Springer Development Methodologies for Big Data Analytics - Springer

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Description

This comprehensive academic text explores multiple development approaches for building big data analytics systems, comparing plan-driven, agile, hybrid, and lightweight methodologies. Published as part of Springer's Transactions on Computational Science and Computational Intelligence series, the book provides detailed analysis of how different project management frameworks apply to data-intensive computing environments. Researchers, engineers, and technology leaders seeking to understand the trade-offs between structured planning and flexible iteration in big data projects will find this resource invaluable for selecting and implementing the most suitable development strategy for their organisation's analytics infrastructure.

Key Features

Covers plan-driven, agile, hybrid, and lightweight development methodologies specifically tailored for big data analytics systems.

Published by Springer as part of the Transactions on Computational Science and Computational Intelligence series, ensuring academic rigour and peer review.

Provides comparative analysis of different approaches to help teams select the optimal development framework for their analytics projects.

Addresses both theoretical foundations and practical implementation considerations for complex data-intensive computing environments.

Suitable for researchers, engineers, architects, and project managers working with large-scale data systems and analytics platforms.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
04 November 2023
Listed Since
07 July 2023

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