Data Analytics for It Networks: Developing Innovative Use Cases, Paperback

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
  

About the author

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.