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Software Architecture for Big Data and the Cloud 无水印pdf

Software

Architecture

Data

Cloud

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Table of Contents

1. Cover image

2. Title page

3. Table of Contents

4. Copyright

5. Contributors

6. About the Editors

7. Foreword by Mandy Chessell

1. Amnesia or Progress?

8. Foreword by Ian Gorton

9. Preface

1. Introduction

2. Why a New Book on Software Architecture for Big Data and the

Cloud?

3. Book Outline

4. Part I: Concepts and Models

5. Part II: Analyzing and Evaluating

6. Part III: Technologies

7. Part IV: Resource Management

8. Part V: Looking Ahead

10. Chapter 1: Introduction. Software Architecture for Cloud and Big Data:

An Open Quest for the Architecturally Significant Requirements

1. Abstract

2. 1.1. A Perspective into Software Architecture for Cloud and Big

Data

3. 1.2. Cloud Architecturally Significant Requirements and Their

Design Implications

4. 1.3. Big Data Management as Cloud Architecturally Significant

Requirement

5. References

11. Part 1: Concepts and Models

1. Chapter 2: Hyperscalability – The Changing Face of Software

Architecture

1. Abstract

2. 2.1. Introduction

3. 2.2. Hyperscalable Systems

4. 2.3. Principles of Hyperscalable Systems

5. 2.4. Related Work

6. 2.5. Conclusions

7. References

2. Chapter 3: Architecting to Deliver Value From a Big Data and

Hybrid Cloud Architecture

1. Abstract

2. 3.1. Introduction

3. 3.2. Supporting the Analytics Lifecycle

4. 3.3. The Role of Data Lakes

5. 3.4. Key Design Features That Make a Data Lake Successful

6. 3.5. Architecture Example – Context Management in the IoT

7. 3.6. Big Data Origins and Characteristics

8. 3.7. The Systems That Capture and Process Big Data

9. 3.8. Operating Across Organizational Silos

10. 3.9. Architecture Example – Local Processing of Big Data

11. 3.10. Architecture Example – Creating a Multichannel View

12. 3.11. Application Independent Data

13. 3.12. Metadata and Governance

14. 3.13. Conclusions

15. 3.14. Outlook and Future Directions

16. References

3. Chapter 4: Domain-Driven Design of Big Data Systems Based on a

Reference Architecture

1. Abstract

2. 4.1. Introduction

3. 4.2. Domain-Driven Design Approach

4. 4.3. Related Work

5. 4.4. Feature Model of Big Data Systems

6. 4.5. Deriving the Application Architectures and Example

7. 4.6. Conclusion

8. References

4. Chapter 5: An Architectural Model-Based Approach to Quality-

Aware DevOps in Cloud Applicationsc

1. Abstract

2. 5.1. Introduction

3. 5.2. A Cloud-Based Software Application

4. 5.3. Differences in Architectural Models Among Development

and Operations

5. 5.4. The iObserve Approach

6. 5.5. Addressing the Differences in Architectural Models

7. 5.6. Applying iObserve to CoCoME

8. 5.7. Limitations

9. 5.8. Related Work

10. 5.9. Conclusion

11. References

5. Chapter 6: Bridging Ecology and Cloud: Transposing Ecological

Perspective to Enable Better Cloud Autoscaling

1. Abstract

2. Acknowledgement

3. 6.1. Introduction

4. 6.2. Motivation

5. 6.3. Natural Ecosystem

6. 6.4. Transposing Ecological Principles, Theories and Models

to Cloud Ecosystem

7. 6.5. Ecology-Inspired Self-Aware Pattern

8. 6.6. Opportunities and Challenges

9. 6.7. Related Work

10. 6.8. Conclusion

11. References

12. Part 2: Analyzing and Evaluating

1. Chapter 7: Evaluating Web PKIs

1. Abstract

2. 7.1. Introduction

3. 7.2. An Overview of PKI

4. 7.3. Desired Features and Security Concerns

5. 7.4. Existing Proposals

6. 7.5. Observations

7. 7.6. Conclusion

8. References

2. Chapter 8: Performance Isolation in Cloud-Based Big Data

Architectures

1. Abstract

2. 8.1. Introduction

3. 8.2. Background

4. 8.3. Case Study and Problem Statement

5. 8.4. Performance Monitoring in Cloud-Based Systems

6. 8.5. Application Framework for Performance Isolation

7. 8.6. Evaluation of the Framework

8. 8.7. Discussion

9. 8.8. Related Work

10. 8.9. Conclusion

11. References

3. Chapter 9: From Legacy to Cloud: Risks and Benefits in Software

Cloud Migration

1. Abstract

2. 9.1. Introduction

3. 9.2. Research Method

4. 9.3. Results

5. 9.4. Discussion

6. 9.5. Conclusion

7. References

4. Chapter 10: Big Data: A Practitioners Perspective

1. Abstract

2. 10.1. Big Data Is a New Paradigm – Differences With

Traditional Data Warehouse, Pitfalls and Consideration

3. 10.2. Product Considerations for Big Data – Use of Open

Source Products for Big Data, Pitfalls and Considerations

4. 10.3. Use of Cloud for hosting Big Data – Why to Use Cloud,

Pitfalls and Consideration

5. 10.4. Big Data Implementation – Architecture Definition,

Processing Framework and Migration Pattern From Data

Warehouse to Big Data

6. 10.5. Conclusion

7. References

13. Part 3: Technologies

1. Chapter 11: A Taxonomy and Survey of Stream Processing

Systems

1. Abstract

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fowlersong

2018-03-13

一本700多页的书，排版看起来很不舒服，应该是转自其他版本。根本看不下去，不建议下载。
cutebear2012

2018-02-06

Thanks. I love it.

yinkaisheng-nj

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