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Data Analytics for It Networks de John Garrett

Data Analytics for It Networks

Autor John Garrett
en Limba Engleză Paperback – 24 oct 2018

Problema fundamentală pe care o întâmpină administratorii de rețea moderni nu este lipsa datelor, ci dificultatea de a extrage valoare acționabilă dintr-o infrastructură tot mai fragmentată. În Data Analytics for It Networks, remarcăm o abordare riguroasă a modului în care știința datelor poate fi aplicată direct pe fluxurile de trafic pentru a stimula inovația. Autorul nu se rezumă la teorie, ci ghidează cititorul prin procesul tehnic de construire a unei arhitecturi Big Data capabile să susțină analize complexe în timp real. Notăm cu interes structura progresivă a lucrării: volumul debutează cu fundamentarea metodologică, trecând rapid către specificații tehnice despre baze de date, stocare și arhitecturi de microservicii. Un aspect distinctiv este analiza comparativă a instrumentelor de lucru, precum R versus Python sau SAS, oferind cititorului argumentele necesare pentru alegerea stack-ului tehnologic optim. Dacă Network Science Models for Data Analytics Automation v-a oferit cadrul teoretic al modelării, această carte oferă instrumentele practice și exemplele de implementare necesare într-un mediu de producție. De asemenea, spre deosebire de Network Behavior Analysis, care se concentrează pe extragerea modelelor de comportament, lucrarea de față pune accent pe integrarea soluțiilor de analiză ca un „overlay” peste infrastructura existentă. Suntem de părere că expertiza lui John Garrett în înțelegerea provocărilor operaționale este evidentă în capitolele dedicate maturității analizelor și gândirii strategice. Deși autorul a explorat anterior teme diverse, de la performanța personală în What's Your "And"? până la cursuri tehnice de fotografie, acest volum reprezintă punctul culminant al capacității sale de a sintetiza procese complexe în ghiduri aplicabile. Cartea acoperă inclusiv detaliile specifice produselor Cisco, transformând volumul într-un manual tehnic esențial pentru inginerii care doresc să treacă de la simpla mentenanță la optimizarea predictivă a rețelei.

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Specificații

ISBN-13: 9781587145131
ISBN-10: 1587145138
Pagini: 496
Dimensiuni: 183 x 229 x 30 mm
Greutate: 0.78 kg
Editura: Pearson Education

De ce să citești această carte

Recomandăm această carte profesioniștilor IT și analiștilor de date care doresc să implementeze soluții de analytics direct în infrastructura de rețea. Veți câștiga o metodologie clară pentru dezvoltarea cazurilor de utilizare, de la selectarea algoritmilor potriviți până la configurarea arhitecturilor Big Data. Este un instrument practic pentru transformarea datelor brute în avantaje strategice pentru organizație.

Despre autor

John Garrett este un expert recunoscut în domeniul rețelelor, având o capacitate unică de a traduce provocările tehnice complexe în soluții de business scalabile. Cu o experiență vastă în înțelegerea arhitecturilor de date, Garrett a publicat lucrări diverse, de la ghiduri practice precum Collins Complete Photography Course la volume despre cultura organizațională și performanță, cum este What's Your "And"?. În Data Analytics for It Networks, el își folosește expertiza pentru a sprijini profesioniștii IT în adoptarea tehnologiilor emergente de analiză a datelor.

Descriere scurtă

One of the hottest trends in the networking industry is to apply analytics to improve performance, security, and value. Everyone's talking about integrating analytics in networks. But successfully doing so requires a deep understanding of both networking and data, and that's a rare combination. This book's author, John Garrett, is currently Analytics Technical Lead for Cisco Advanced Services, where he draws on his MS in Predictive Analytics to develop analytics use cases for many of Cisco's most sophisticated customers. In Computer Networking Data Analytics, Garrett illuminates the data of networking, revealing the power of the information that's already available in your environment. Next, he introduces powerful techniques for using that information to accelerate innovation, and build a better network. Garrett walks step-by-step through a wide spectrum of common networking analytics techniques and scenarios, illuminating the insights underlying them, and helping you adapt them for your own environment. He concludes with several end-to-end case studies demonstrating how his team has developed high-value use cases for some of Cisco's largest customers. These end-to-end use cases can serve as repeatable templates for developing your own solutions in areas ranging from advanced troubleshooting and self-healing to service assurance. They will help you drive more value from telemetry, event analytics, network infrastructure metadata, and all the information you have.

Notă biografică

John Garrett is CCIE Emeritus (6204) and Splunk Certified. He earned an M.S. in predictive analytics from Northwestern University, and has a patent pending related to analysis of network devices with data science techniques. John has architected, designed, and implemented LAN, WAN, wireless, and data center solutions for some of the largest Cisco customers. As a secondary role, John has worked with teams in the Cisco Services organization to innovate on some of the most widely used tools and methodologies at Customer Experience over the past 12 years.
For the past 7 years, John's journey has moved through server virtualization, network virtualization, OpenStack and cloud, network functions virtualization (NFV), service assurance, and data science. The realization that analytics and data science play roles in all these brought John full circle back to developing innovative tools and techniques for Cisco Services. John's most recent role is as an Analytics Technical Lead, developing use cases to benefit Cisco Services customers as part of Business Critical Services for Cisco. John lives with his wife and children in Raleigh, North Carolina.

Cuprins

Foreword xvii Introduction: Your future is in your hands! xviii Chapter 1 Getting Started with Analytics 1 What This Chapter Covers 1 Data: You as the SME 2 Use-Case Development with Bias and Mental Models 2 Data Science: Algorithms and Their Purposes 3 What This Book Does Not Cover 4 Building a Big Data Architecture 4 Microservices Architectures and Open Source Software 5 R Versus Python Versus SAS Versus Stata 6 Databases and Data Storage 6 Cisco Products in Detail 6 Analytics and Literary Perspectives 7 Analytics Maturity 7 Knowledge Management 8 Gartner Analytics 8 Strategic Thinking 9 Striving for "Up and to the Right" 9 Moving Your Perspective 10 Hot Topics in the Literature 11 Summary 12 Chapter 2 Approaches for Analytics and Data Science 13 Model Building and Model Deployment 14 Analytics Methodology and Approach 15 Common Approach Walkthrough 16 Distinction Between the Use Case and the Solution 18 Logical Models for Data Science and Data 19 Analytics as an Overlay 20 Analytics Infrastructure Model 22 Summary 33 Chapter 3 Understanding Networking Data Sources 35 Planes of Operation on IT Networks 36 Review of the Planes 40 Data and the Planes of Operation 42 Planes Data Examples 44 A Wider Rabbit Hole 49 A Deeper Rabbit Hole 51 Summary 53 Chapter 4 Accessing Data from Network Components 55 Methods of Networking Data Access 55 Pull Data Availability 57 Push Data Availability 61 Control Plane Data 67 Data Plane Traffic Capture 68 Packet Data 70 Other Data Access Methods 74 Data Types and Measurement Considerations 76 Numbers and Text 77 Data Structure 82 Data Manipulation 84 Other Data Considerations 87 External Data for Context 89 Data Transport Methods 89 Transport Considerations for Network Data Sources 90 Summary 96 Chapter 5 Mental Models and Cognitive Bias 97 Changing How You Think 98 Domain Expertise, Mental Models, and Intuition 99 Mental Models 99 Daniel Kahneman's System 1 and System 2 102 Intuition 103 Opening Your Mind to Cognitive Bias 104 Changing Perspective, Using Bias for Good 105 Your Bias and Your Solutions 106 How You Think: Anchoring, Focalism, Narrative Fallacy, Framing, and Priming 107 How Others Think: Mirroring 110 What Just Happened? Availability, Recency, Correlation, Clustering, and Illusion of Truth 111 Enter the Boss: HIPPO and Authority Bias 113 What You Know: Confirmation, Expectation, Ambiguity, Context, and Frequency Illusion 114 What You Don't Know: Base Rates, Small Numbers, Group Attribution, and Survivorship 117 Your Skills and Expertise: Curse of Knowledge, Group Bias, and Dunning-Kruger 119 We Don't Need a New System: IKEA, Not Invented Here, Pro-Innovation, Endowment, Status Quo, Sunk Cost, Zero Price, and Empathy 121 I Knew It Would Happen: Hindsight, Halo Effect, and Outcome Bias 123 Summary 124 Chapter 6 Innovative Thinking Techniques 127 Acting Like an Innovator and Mindfulness 128 Innovation Tips and Techniques 129 Developing Analytics for Your Company 140 Defocusing, Breaking Anchors, and Unpriming 140 Lean Thinking 142 Cognitive Trickery 143 Quick Innovation Wins 143 Summary 144 Chapter 7 Analytics Use Cases and the Intuition Behind Them 147 Analytics Definitions 150 How to Use the Information from This Chapter 151 Priming and Framing Effects 151 Analytics Rube Goldberg Machines 151 Popular Analytics Use Cases 152 Machine Learning and Statistics Use Cases 153 Common IT Analytics Use Cases 170 Broadly Applicable Use Cases 199 Some Final Notes on Use Cases 214 Summary 214 Chapter 8 Analytics Algorithms and the Intuition Behind Them 217 About the Algorithms 217 Algorithms and Assumptions 218 Additional Background 219 Data and Statistics 221 Statistics 221 Correlation 224 Longitudinal Data 225 ANOVA 227 Probability 228 Bayes' Theorem 228 Feature Selection 230 Data-Encoding Methods 232 Dimensionality Reduction 233 Unsupervised Learning 234 Clustering 234 Association Rules 240 Sequential Pattern Mining 243 Collaborative Filtering 244 Supervised Learning 246 Regression Analysis 246 Classification Algorithms 248 Decision Trees 249 Random Forest 250 Gradient Boosting Methods 251 Neural Networks 252 Support Vector Machines 258 Time Series Analysis 259 Text and Document Analysis 262 Natural Language Processing (NLP) 262 Information Retrieval 263 Topic Modeling 265 Sentiment Analysis 266 Other Analytics Concepts 267 Artificial Intelligence 267 Confusion Matrix and Contingency Tables 267 Cumulative Gains and Lift 269 Simulation 271 Summary 271 Chapter 9 Building Analytics Use Cases 273 Designing Your Analytics Solutions 274 Using the Analytics Infrastructure Model 275 About the Upcoming Use Cases 276 The Data 276 The Data Science 278 The Code 280 Operationalizing Solutions as Use Cases 281 Understanding and Designing Workflows 282 Tips for Setting Up an Environment to Do Your Own Analysis 282 Summary 284 Chapter 10 Developing Real Use Cases: The Power of Statistics 285 Loading and Exploring Data 286 Base Rate Statistics for Platform Crashes 288 Base Rate Statistics for Software Crashes 299 ANOVA 305 Data Transformation 310 Tests for Normality 311 Examining Variance 313 Statistical Anomaly Detection 318 Summary 321 Chapter 11 Developing Real Use Cases: Network Infrastructure Analytics 323 Human DNA and Fingerprinting 324 Building Search Capability 325 Loading Data and Setting Up the Environment 325 Encoding Data for Algorithmic Use 328 Search Challenges and Solutions 331 Other Uses of Encoded Data 336 Dimensionality Reduction 337 Data Visualization 340 K-Means Clustering 344 Machine Learning Guided Troubleshooting 350 Summary 353 Chapter 12 Developing Real Use Cases: Control Plane Analytics Using Syslog Telemetry 355 Data for This Chapter 356 OSPF Routing Protocols 357 Non-Machine Learning Log Analysis Using pandas 357 Noise Reduction 360 Finding the Hotspots 362 Machine Learning-Based Log Evaluation 366 Data Visualization 367 Cleaning and Encoding Data 369 Clustering 373 More Data Visualization 375 Transaction Analysis 379 Task List 386 Summary 387 Chapter 13 Developing Real Use Cases: Data Plane Analytics 389 The Data 390 SME Analysis 394 SME Port Clustering 407 Machine Learning: Creating Full Port Profiles 413 Machine Learning: Creating Source Port Profiles 419 Asset Discovery 422 Investigation Task List 423 Summary 424 Chapter 14 Cisco Analytics 425 Architecture and Advisory Services for Analytics 426 Stealthwatch 427 Digital Network Architecture (DNA) 428 AppDynamics 428 Tetration 430 Crosswork Automation 431 IoT Analytics 432 Analytics Platforms and Partnerships 433 Cisco Open Source Platform 433 Summary 434 Chapter 15 Book Summary 435 Analytics Introduction and Methodology 436 All About Networking Data 438 Using Bias and Innovation to Discover Solutions 439 Analytics Use Cases and Algorithms 439 Building Real Analytics Use Cases 440 Cisco Services and Solutions 442 In Closing 442 Appendix A Function for Parsing Packets from pcap Files 443 9781587145131, TOC, 9/19/18