Data Infrastructure Management

Insights and Strategies

Greg Schulz

• Verlag: Auerbach Publications
• Jahr: 2019
• ISBN:
  9780429559976
• Auflage: 1
• Thema: Business
• Thema: Operazioni

This book looks at various application and data demand drivers, along with data infrastructure options from legacy on premise, public cloud, hybrid, software-defined data center (SDDC), software data infrastructure (SDI), container as well as serverless along with infrastructure as a Service (IaaS), IT as a Service (ITaaS) along with related technology, trends, tools, techniques and strategies. Filled with example scenarios, tips and strategy considerations, the book covers frequently asked questions and answers to aid strategy as well as decision-making.

Contents

Preface

Who Should Read This Book

How This Book Is Organized

Acknowledgments

About the Author

Part One
Applications, Data, and IT Data Infrastructures

Chapter 1 IT Data Infrastructure Fundamentals

1.1 Getting Started

1.2 What’s the Buzz in and around IT Data Infrastructures?

1.2.1 Data Infrastructures—How Server Storage I/O Resources Are Used

1.2.2 Why Data Infrastructures Are Important (Demand Drivers)

1.2.3 Data Value

1.3 Data Infrastructures Past, Present, and Future

1.3.1 Where Are We Today? (Balancing Legacy with Emerging)

1.3.2 Where Are We Going? (Planning, Lessons Learned)

1.4 Data Infrastructure Management Tradecraft

1.5 Data Infrastructure Terminology (Context Matters)

1.6 Common Questions and Tips

1.7 Strategies

1.8 Chapter Summary

Chapter 2 Application and IT Environments

2.1 Getting Started

2.1.1 Context for the Chapter

2.2 Everything Is Not the Same with Data Infrastructures

2.2.1 Various Types of Environments (Big and Small)

2.2.2 Gaining Data and Application Insight

2.2.3 Various Types of Applications

2.2.4 Various Types of Data

2.3 Common Applications Characteristics

2.3.1 Performance and Activity (How Resources Get Used)

2.3.2 Availability (Accessibility, Durability, Consistency)

2.3.3 Capacity and Space (What Gets Consumed and Occupied)

2.3.4 Economics (People, Budgets, Energy, and Other Constraints)

2.4 Where Applications and Data Get Processed and Reside

2.5 Application Data and Data Infrastructure Strategies

2.6 Common Questions and Tips

2.7 Chapter Summary

Part Two
Data Infrastructure Management

Chapter 3 Data Infrastructure Management

3.1 Getting Started

3.2 Data Infrastructure Management and Tools

3.3 Data Infrastructure Habitats and Facilities

3.4 Data Infrastructure Management

3.5 Troubleshooting, Problem Solving, Remediation, and Repairs

3.6 Common Questions and Tips

3.7 Chapter Summary

Chapter 4 Data Infrastructure Availability, Data Protection, Security, and Strategy

4.1 Getting Started

4.2 Data Protection Fundamentals

4.3 Availability, Data Protection, and Security

4.4 Availability (Resiliency and Data Protection) Services

4.5 Revisiting 4 3 2 1—The Golden Rule of Data Protection

4.6 Availability, FTT, and FTM Fundamentals

4.7 Common Availability Characteristics and Functionalities

4.8 Enabling Availability, Resiliency, Accessibility, and RTO

4.9 Reliability, Availability, and Serviceability (RAS)

4.10 Enabling RPO (Archive, Backup, CDP, Snapshots, Versions)

4.11 Point-in-Time Protection for Different Points of Interest

4.12 Snapshots, Consistency, and Checkpoints

4.13 Data Infrastructure Security (Logical and Physical)

4.13.1 Data Infrastructure Security Implementation

4.13.2 Physical Security and Facilities

4.13.3 Logical and Software-Defined Security

4.13.4 Encryption Codes and Key Management

4.13.5 Identity Access Management and Control

4.13.6 General Data Infrastructure Security–Related Topics

4.14 Common Questions and Tips

4.15 Chapter Summary

Chapter 5 Data Infrastructure Metrics and Management

5.1 Getting Started

5.2 Avoid Flying Blind—Having Situational Awareness

5.2.1 Metrics That Matter (and Where to Get Them)

5.3 Data Infrastructure Decision Making

5.3.1 Comparing Data Infrastructure Components and Services

5.3.2 Analysis, Benchmark, Comparison, Simulation, and Tests

5.4 Data Infrastructure, Strategy, and Design Considerations

5.5 Common Questions and Tips

5.6 Chapter Summary

Part Three
Enabling Data Infrastructures

Chapter 6 Data Infrastructure Deployment Considerations: Part I

6.1 Getting Started

6.2 Applications, Tips, and Learning Experiences

6.3 Software-Defined, Virtual, Containers, and Clouds

6.3.1 Clouds: Public, Private, and Hybrid

6.3.2 Public Cloud Services

6.3.3 Private and Hybrid Cloud Solutions

6.4 Docker, Containers, and Microservices

6.5 Workspace and Virtual Desktop Infrastructure

6.6 Data Infrastructure Migration

6.6 Common Questions and Tips

6.7 Chapter Summary

Chapter 7 Data Infrastructure Deployment Considerations: Part II

7.1 Getting Started

7.2 Microsoft Azure, Hyper-V, Windows, and Other Tools

7.3 VMware vSphere, vSAN, NSX, and Cloud Foundation

7.4 Data Databases: Little Data SQL and NoSQL

7.5 Big Data, Data Ponds, Pools, and Bulk-Content Data Stores

7.6 Legacy vs. Converged vs. Hyper-Converged vs. Cloud and Containers

7.7 Common Questions and Tips

7.8 Chapter Summary

Chapter 8 Data Infrastructure Futures, Wrap-up, and Summary

8.1 Getting Started on the Wrap-up

8.2 People, Process, and Best Practices

8.2.1 Skills Development

8.3 Emerging Topics, Trends, and Predictions

8.4 Chapter and Book Summary

Appendix A: Companion Websites and Where to Learn More

Glossary

Index

Preface

This book follows from my previous books, Software-Defined Data Infrastructure Essentials: Cloud, Converged, and Virtual Fundamental Server Storage I/O Tradecraft * (aka “The Blue Book”), Resilient Storage Networks: Designing Flexible Scalable Data Infrastructures †(aka “The Red Book”), The Green and Virtual Data Center ‡ (aka “The Green Book”), and Cloud and Virtual Data Storage Networking § (aka “The Yellow, or Gold, Book”).

Data Infrastructure Management: Insights and Strategies provides fundamental coverage of physical, cloud, converged, and virtual server storage I/O networking technologies, trends, tools, techniques, and tradecraft skills. Software-defined data centers (SDDC), software data infrastructures (SDI), software-defined data infrastructures (SDDI, and traditional data infra- structures support business applications including components such as a server, storage, I/O networking, hardware, software, services, and best practices, among other management tools. Spanning cloud, virtual, container, converged (and hyper-converged), as well as legacy and hybrid systems, data infrastructures exist to protect, preserve, and serve data and information. Although there are plenty of new things, sometimes those new things get used in old ways, and sometimes old things can get used in...