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Data Analytics for IT Networks: Developing Innovative Use Cases, First Edition

Book Description

Use data analytics to drive innovation and value throughout your network infrastructure

 

Network and IT professionals capture immense amounts of data from their networks. Buried in this data are multiple opportunities to solve and avoid problems, strengthen security, and improve network performance. To achieve these goals, IT networking experts need a solid understanding of data science, and data scientists need a firm grasp of modern networking concepts. Data Analytics for IT Networks fills these knowledge gaps, allowing both groups to drive unprecedented value from telemetry, event analytics, network infrastructure metadata, and other network data sources.

 

Drawing on his pioneering experience applying data science to large-scale Cisco networks, John Garrett introduces the specific data science methodologies and algorithms network and IT professionals need, and helps data scientists understand contemporary network technologies, applications, and data sources.

 

After establishing this shared understanding, Garrett shows how to uncover innovative use cases that integrate data science algorithms with network data. He concludes with several hands-on, Python-based case studies reflecting Cisco Customer Experience (CX) engineers’ supporting its largest customers. These are designed to serve as templates for developing custom solutions ranging from advanced troubleshooting to service assurance.

 

  • Understand the data analytics landscape and its opportunities in Networking
  • See how elements of an analytics solution come together in the practical use cases
  • Explore and access network data sources, and choose the right data for your problem
  • Innovate more successfully by understanding mental models and cognitive biases
  • Walk through common analytics use cases from many industries, and adapt them to your environment
  • Uncover new data science use cases for optimizing large networks
  • Master proven algorithms, models, and methodologies for solving network problems
  • Adapt use cases built with traditional statistical methods
  • Use data science to improve network infrastructure analysisAnalyze control and data planes with greater sophistication
  • Fully leverage your existing Cisco tools to collect, analyze, and visualize data

Table of Contents

  1. Cover Page
  2. Title Page
  3. Copyright Page
  4. About the Author
  5. About the Technical Reviewers
  6. Dedications
  7. Acknowledgments
  8. Contents at a Glance
  9. Contents
  10. Reader Services
  11. Icons Used in This Book
  12. Command Syntax Conventions
  13. Foreword
  14. Introduction: Your future is in your hands!
    1. My Story
    2. How This Book Is Organized
  15. Credits
    1. Figure Credits
  16. Chapter 1. Getting Started with Analytics
    1. What This Chapter Covers
    2. What This Book Does Not Cover
    3. Analytics and Literary Perspectives
    4. Summary
  17. Chapter 2. Approaches for Analytics and Data Science
    1. Model Building and Model Deployment
    2. Analytics Methodology and Approach
    3. Logical Models for Data Science and Data
    4. Summary
  18. Chapter 3. Understanding Networking Data Sources
    1. Planes of Operation on IT Networks
    2. Data and the Planes of Operation
    3. Summary
  19. Chapter 4. Accessing Data from Network Components
    1. Methods of Networking Data Access
    2. Data Types and Measurement Considerations
    3. Data Transport Methods
    4. Summary
  20. Chapter 5. Mental Models and Cognitive Bias
    1. Changing How You Think
    2. Domain Expertise, Mental Models, and Intuition
    3. Opening Your Mind to Cognitive Bias
    4. Summary
  21. Chapter 6. Innovative Thinking Techniques
    1. Acting Like an Innovator and Mindfulness
    2. Developing Analytics for Your Company
    3. Summary
  22. Chapter 7. Analytics Use Cases and the Intuition Behind Them
    1. Analytics Definitions
    2. How to Use the Information from This Chapter
    3. Popular Analytics Use Cases
    4. Summary
  23. Chapter 8. Analytics Algorithms and the Intuition Behind Them
    1. About the Algorithms
    2. Data and Statistics
    3. Unsupervised Learning
    4. Supervised Learning
    5. Text and Document Analysis
    6. Other Analytics Concepts
    7. Summary
  24. Chapter 9. Building Analytics Use Cases
    1. Designing Your Analytics Solutions
    2. Using the Analytics Infrastructure Model
    3. About the Upcoming Use Cases
    4. Operationalizing Solutions as Use Cases
    5. Tips for Setting Up an Environment to Do Your Own Analysis
    6. Summary
  25. Chapter 10. Developing Real Use Cases: The Power of Statistics
    1. Loading and Exploring Data
    2. Base Rate Statistics for Platform Crashes
    3. Base Rate Statistics for Software Crashes
    4. ANOVA
    5. Data Transformation
    6. Statistical Anomaly Detection
    7. Summary
  26. Chapter 11. Developing Real Use Cases: Network Infrastructure Analytics
    1. Human DNA and Fingerprinting
    2. Building Search Capability
    3. Other Uses of Encoded Data
    4. Dimensionality Reduction
    5. Data Visualization
    6. K-Means Clustering
    7. Machine Learning Guided Troubleshooting
    8. Summary
  27. Chapter 12. Developing Real Use Cases: Control Plane Analytics Using Syslog Telemetry
    1. Data for This Chapter
    2. OSPF Routing Protocols
    3. Non-Machine Learning Log Analysis Using pandas
    4. Machine Learning–Based Log Evaluation
    5. Task List
    6. Summary
  28. Chapter 13. Developing Real Use Cases: Data Plane Analytics
    1. The Data
    2. SME Analysis
    3. SME Port Clustering
    4. Machine Learning: Creating Full Port Profiles
    5. Machine Learning: Creating Source Port Profiles
    6. Asset Discovery
    7. Investigation Task List
    8. Summary
  29. Chapter 14. Cisco Analytics
    1. Architecture and Advisory Services for Analytics
    2. Stealthwatch
    3. Digital Network Architecture (DNA)
    4. AppDynamics
    5. Tetration
    6. Crosswork Automation
    7. IoT Analytics
    8. Analytics Platforms and Partnerships
    9. Cisco Open Source Platform
    10. Summary
  30. Chapter 15. Book Summary
    1. Analytics Introduction and Methodology
    2. All About Networking Data
    3. Using Bias and Innovation to Discover Solutions
    4. Analytics Use Cases and Algorithms
    5. Building Real Analytics Use Cases
    6. Cisco Services and Solutions
    7. In Closing
  31. Appendix A. Function for Parsing Packets from pcap Files
  32. Index