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Mastering Python Networking

Mastering Python Networking

By : Eric Chou
4.5 (88)
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Mastering Python Networking

Mastering Python Networking

4.5 (88)
By: Eric Chou

Overview of this book

Networks in your infrastructure set the foundation for how your application can be deployed, maintained, and serviced. Python is the ideal language for network engineers to explore tools that were previously available to systems engineers and application developers. In Mastering Python Networking, Fourth edition, you'll embark on a Python-based journey to transition from a traditional network engineer to a network developer ready for the next generation of networks. This new edition is completely revised and updated to work with the latest Python features and DevOps frameworks. In addition to new chapters on introducing Docker containers and Python 3 Async IO for network engineers, each chapter is updated with the latest libraries with working examples to ensure compatibility and understanding of the concepts. Starting with a basic overview of Python, the book teaches you how it can interact with both legacy and API-enabled network devices. You will learn to leverage high-level Python packages and frameworks to perform network automation tasks, monitoring, management, and enhanced network security, followed by AWS and Azure cloud networking. You will use Git for code management, GitLab for continuous integration, and Python-based testing tools to verify your network.
Table of Contents (19 chapters)
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17
Other Books You May Enjoy
18
Index

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In Chapter 7Network Monitoring with Python – Part 1, and Chapter 8Network Monitoring with Python Part – 2, we discussed the various ways to monitor a network. In the two chapters, we looked at two different approaches for network data collection: we can either retrieve data from network devices such as SNMP, or we can listen for the data sent by network devices using flow-based exports. After the data is collected, we will need to store the data in a database, then analyze the data to gain insights to decide what the data means. Most of the time, the analyzed results are displayed in a graph, whether a line graph, bar graph, or pie chart. We can use individual tools such as PySNMP, Matplotlib, and Pygal for each step, or we can leverage all-in-one tools such as Cacti or Ntop for monitoring. The tools introduced in those two chapters gave us basic monitoring and...

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