Practical DevOps for Big Data

Contributors

• University of Pau — Franck Barbier (Preface)
• Netfective Technology — Youssef Ridene, Joas Yannick Kinouani, Laurie-Anne Parant
• Imperial College London — Giuliano Casale, Chen Li, Lulai Zhu, Tatiana Ustinova, Pooyan Jamshidi
• Politecnico di Milano — Danilo Ardagna, Marcello Bersani, Elisabetta Di Nitto, Eugenio Gianniti, Michele Guerriero, Matteo Rossi, Damian Andrew Tamburri, Safia Kalwar, Francesco Marconi
• IeAT — Gabriel Iuhasz, Dana Petcu, Ioan Dragan
• XLAB d.o.o. — Matej Artač, Tadej Borovšak
• flexiOPS — Craig Sheridan, David McGowran, Grant Olsson
• ATC SA — Vasilis Papanikolaou, George Giotis
• Prodevelop — Christophe Joubert, Ismael Torres, Marc Gil
• Universidad Zaragoza — Simona Bernardi, Abel Gómez, José Merseguer, Diego Pérez, José-Ignacio Requeno

How to Read This Book

This book is about a methodology for constructing big data applications. A methodology exists for the purpose of solving software development problems. It is made of development processes—workflows, ways of doing things—and tools to help concretise them. The ideal and guiding principle of a methodology is to facilitate the job and guarantee the satisfaction of stakeholders involved in a software project—end-users and maintainers included. Our methodology addresses the problem of reusing complex and not easily learned big data technologies to effectively and efficiently build big data systems of good quality. To do so, it takes inspiration from two other successful methodologies: DevOps and model-driven engineering. Regarding prerequisites, we assume the reader has a general understanding of software engineering, and, from a tool point of view, a familiarity with the Unified Modeling Language (UML) and the Eclipse IDE.

The book is composed of eight parts. Part I is an introduction (Chapter 1) followed by a state of the art (Chapter 2). Part II sets our methodology forth (Chapter 3) and reviews some UML diagrams convenient for modelling big data systems (Chapter 4). Part III shows how to adjust UML in order to make it support a stepwise refinement approach, where models become increasingly detailed and precise. Except Chapter 5 introduces the subject, and each of the following chapters (Chapters 6, 7, and 8) is dedicated to one of our three refinement steps. Part IV focuses on model analysis. Indeed, models enable designers to study carefully the system without needing an implementation thereof: a model checker (Chapter 9) may verify whether the system, as it is modelled, satisfies some quality of service requirements; a simulator (Chapter 10) may explore its possible behaviours; and an optimiser (Chapter 11) may find the best one. Part V explains how models serve to automatically install (Chapter 12), configure (Chapter 13), and test (Chapters 14 and 15) the modelled big data technologies. Part VI describes the collect of runtime performance data (Chapter 16) in order to detect anomalies (Chapter 17), violations of quality requirements (Chapter 18), and rethink models accordingly (Chapter 19). Part VII presents three case studies of this methodology (Chapters 20, 21, and 22). Finally, Part VIII concludes the book (Chapter 24) after mentioning future research directions (Chapter 23).

Contents

1. Introduction
1. Preface
2. Introduction
3. Related Work
2. DevOps and Big Data Modelling
3. Methodology
4. Review of UML Diagrams
3. Modelling Abstractions
5. Introduction to Modelling
6. Platform-Independent Modelling
7. Technology-Specific Modelling
8. Deployment-Specific Modelling
4. Formal Quality Analysis
9. Quality Verification
10. Quality Simulation
11. Quality Optimisation
5. From Models to Production
12. Delivery
13. Configuration Optimisation
14. Quality Testing
15. Fault Injection
6. From Production Back to Models
16. Monitoring
17. Anomaly Detection
18. Trace Checking
19. Iterative Enhancement
7. Case Studies
20. Fraud Detection
21. Maritime Operations
22. News and Media
8. Conclusion
23. Future Challenges
24. Closing Remarks
9. Appendices
1. Glossary
2. Index