Software Architecture for Big Data and the Cloud

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Written by
Published by
Morgan Kaufmann
QR code for Software Architecture for Big Data and the Cloud
Jun, 2017

About Software Architecture for Big Data and the Cloud

Software Architecture for Big Data and the Cloud is designed to be a single resource that brings together research on how software architectures can solve the challenges imposed by building big data software systems. The challenges of big data on the software architecture can relate to scale, security, integrity, performance, concurrency, parallelism, and dependability, amongst others. Big data handling requires rethinking architectural solutions to meet functional and non-functional requirements related to volume, variety and velocity.
The book's editors have varied and complementary backgrounds in requirements and architecture, specifically in software architectures for cloud and big data, as well as expertise in software engineering for cloud and big data. This book brings together work across different disciplines in software engineering, including work expanded from conference tracks and workshops led by the editors.

  • Discusses systematic and disciplined approaches to building software architectures for cloud and big data with state-of-the-art methods and techniques

  • Presents case studies involving enterprise, business, and government service deployment of big data applications

  • Shares guidance on theory, frameworks, methodologies, and architecture for cloud and big data

Table of Contents

Chapter 1: Introduction. Software Architecture for Cloud and Big Data
Part 1: Concepts and Models
Chapter 2: Hyperscalability – The Changing Face of Software
Chapter 3: Architecting to Deliver Value From a Big Data and
Chapter 4: Domain-Driven Design of Big Data Systems Based on a
Chapter 5: An Architectural Model-Based Approach to Quality
Chapter 6: Bridging Ecology and Cloud: Transposing Ecological
Part 2: Analyzing and Evaluating
Chapter 7: Evaluating Web PKIs
Chapter 8: Performance Isolation in Cloud-Based Big Data
Chapter 9: From Legacy to Cloud: Risks and Benefits in Software
Chapter 10: Big Data: A Practitioners Perspective
Part 3: Technologies
Chapter 11: A Taxonomy and Survey of Stream Processing
Chapter 12: Architecting Cloud Services for the Digital Me in a
Chapter 13: Reengineering Data-Centric Information Systems for
Chapter 14: Exploring the Evolution of Big Data Technologies
Chapter 15: A Taxonomy and Survey of Fault-Tolerant Workflow
Part 4: Resource Management
Chapter 16: The HARNESS Platform: A Hardware- and Network
Chapter 17: Auditable Version Control Systems in Untrusted
Chapter 18: Scientific Workflow Management System for Clouds
Part 5: Looking Ahead
Chapter 19: Outlook and Future Directions

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