Getting Architecture Just Right: Detailed Practical Guidance for Architecting Any Real-World IT Project To build effective architectures, software architects must tread a fine line between precision and ambiguity (a.k.a. big animal pictures). This is difficult but crucial: Failure to achieve this balance often leads directly to poor systems design and implementation. Now, pioneering IBM Distinguished Engineer and Chief Technology Officer Tilak Mitra offers the first complete guide to developing end-to-end solution architectures that are “just enough”--identifying and capturing the most important artifacts, without over-engineering or excessive documentation, and providing a practical approach to consistent and repeated success in defining software architectures. Practical Software Architecture provides detailed prescriptive and pragmatic guidance for architecting any real-world IT project, regardless of system, methodology, or environment. Mitra specifically identifies the artifacts that require emphasis and shows how to communicate evolving solutions with stakeholders, bridging the gap between architecture and implementation. Through a real-world case study, Mitra guides you iteratively through building an end-to-end solution architecture. Step by step, he addresses key topics in developing system contexts, architecture overviews, architecture decisions, functional models, operational models, integration patterns for systems design, infrastructure matters, and more. Throughout, you’ll find clear, crisp, and accurate guidance for crafting architectures that can be confidently delivered on time and budget. Coverage Includes Using architecture to promote communication, enable planning, and support functional as well as nonfunctional capabilities (such as scalability, performance, maintainability, and security) Focusing on the architectural problem at hand and avoiding overreliance on theory and generalization An architectural introduction to analytics Capturing “just enough” about system context, architecture overview, architecture decisions, functional models, and operational models Integration patterns and their usage in architecture design Infrastructure matters in any software architecture A real-world case study and its software architecture

Table of Contents:
Foreword   xv Preface   xvi Chapter 1: Case Study   1 The Business Problem   1 Summary   5 Chapter 2: Software Architecture: The What and Why   7 Some Background   7 The What   8 The Why   10 Architecture Views and Viewpoints   14 Summary   17 References   18 Chapter 3: Capturing Just Enough   19 Architecture Aspects in Focus   19 Summary   21 Chapter 4: The System Context   23 The Business Context Versus System Context Conundrum   23 Capturing the System Context   25 Case Study: System Context for Elixir   30 Summary   36 References   37 Chapter 5: The Architecture Overview   39 What It Is   39 Why We Need It   41 The Enterprise View   42 The Layered View   47 The IT System View   52 Case Study: Architecture Overview of Elixir   57 Summary   63 References   63 Chapter 6: Architecture Decisions   65 Why We Need It   65 How to Get Started   66 Creating an Architecture Decision   67 Case Study: Architecture Decisions for Elixir   72 Summary   75 Chapter 7: The Functional Model   77 Why We Need It   77 A Few Words on Traceability   79 Developing the Functional Model   81 Case Study: Functional Model for Elixir   99 Summary   107 References   108 Chapter 8: The Operational Model   109 Why We Need It   110 On Traceability and Service Levels   111 Developing the Operational Model   113 Case Study: Operational Model for Elixir   141 Summary   149 References   150 Chapter 9: Integration: Approaches and Patterns   151 Why We Need It   151 Approaches to Integration   152 Integration Patterns   161 Case Study: Integration View of Elixir   166 Summary   169 References   170 Chapter 10: Infrastructure Matters   171 Why We Need It   172 Some Considerations   172 Case Study: Infrastructure Considerations for Elixir   192 Summary   194 So Where Do We Stand?    195 References   196 Chapter 11: Analytics: An Architecture Introduction   199 Why We Need It   200 Dimensions of Analytics   201 Analytics Architecture: Foundation   205 Architecture Building Blocks   216 Summary   228 References   230 Chapter 12: Sage Musings   231 Agility Gotta Be an Amalgamate   231 Traditional Requirements-Gathering Techniques Are Passe   233 The MVP Paradigm Is Worth Considering   234 Do Not Be a Prisoner of Events   235 Predictive Analytics Is Not the Only Entry Point into Analytics   235 Leadership Can Be an Acquired Trait   236 Technology-Driven Architecture Is a Bad Idea   237 Open Source Is Cool but to a Point   238 Write Them Up However Trivial They May Seem   239 Baseline Your Architecture on Core Strengths of Technology Products   240 Summary   241 References   241 Appendix A: 25 Topic Goodies   243 What Is the Difference Between Architecture and Design?    243 What Is the Difference Between Architectural Patterns, Design Patterns, and a Framework?    243 How Can We Compare a Top-Down Functional Decomposition Technique and an Object-Oriented Analysis and Design (OOAD) Technique?    244 What Is the Difference Between Conceptual, Specified, and Physical Models?    245 How Do Architecture Principles Provide Both Flexibility and Resilience to Systems Architecture?    245 Why Could the Development of the Physical Operational Model (POM) Be Broken into Iterations?    246 What Is a Service-Oriented Architecture?    246 What Is an Event-Driven Architecture?    246 What Is a Process Architecture?    247 What Is a Technology Architecture?    248 What Is an Adapter?    248 What Is a Service Registry?    249 What Is a Network Switch Block?    249 What Are Operational Data Warehouses?    249 What Is the Difference Between Complex Event Processing (CEP) and Stream Computing?    250 What Is the Difference Between Schema at Read and Schema at Write Techniques?    251 What Is a Triple Store?    251 What Is a Massively Parallel Processing (MPP) System?    252 IBM Watson Is Built on DeepQA Architecture. What Is DeepQA?    252 What Is the Difference Between Supervised and Unsupervised Learning Techniques?    253 What Is the Difference Between Taxonomy and Ontology?    253 What Is Spark and How Does It Work?    254 What Are Some of the Advantages and Challenges of the Cloud Computing Platform and Paradigm?    256 What Are the Different Cloud Deployment Models?    257 What Is Docker Technology?    258 Summary   259 References   259 Appendix B: Elixir Functional Model (Continued)    261 Logical Level   261 Specified Level   264 Physical Level   267 Index   269