You're reading from Hands-On Python for DevOps Leverage Python's native libraries to streamline your workflow and save time with automation

Product type Paperback

Published in Mar 2024

Publisher Packt

ISBN-13 9781835081167

Length 220 pages

Edition 1st Edition

Languages

Python

Tools

Combine

Concepts

DevOps

Author (1):

Ankur Roy

View More author details

Table of Contents (19) Chapters

Preface

1. Part 1: Introduction to DevOps and role of Python in DevOps

2. Chapter 1: Introducing DevOps Principles FREE CHAPTER

3. Chapter 2: Talking about Python

4. Chapter 3: The Simplest Ways to Start Using DevOps in Python Immediately

5. Chapter 4: Provisioning Resources

6. Part 2: Sample Implementations of Python in DevOps

7. Chapter 5: Manipulating Resources

8. Chapter 6: Security and DevSecOps with Python

9. Chapter 7: Automating Tasks

10. Chapter 8: Understanding Event-Driven Architecture

11. Chapter 9: Using Python for CI/CD Pipelines

12. Part 3: Let’s Go Further, Let’s Build Bigger

13. Chapter 10: Common DevOps Use Cases in Some of the Biggest Companies in the World

14. Chapter 11: MLOps and DataOps

15. Chapter 12: How Python Integrates with IaC Concepts

16. Chapter 13: The Tools to Take Your DevOps to the Next Level

17. Index

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Dealing with velocity, volume, and variety

When given any tutorial on how to process data, you are usually given a quick introduction to the three Vs (velocity, volume, and variety). These are the three ways in which the complexity of data can scale. Each of them presents a singularly unique problem when dealing with data, and a lot of data that you would have to deal with can be a combination of all three. Velocity is the speed of data coming in over a period of time, volume is the amount of data, and variety is the diversity of the data being presented.

So, this section will be divided according to the three Vs, and in each subsection, there will be a solution for a common problem that may arise with them. This way, you will get to see how Python can help in dealing with such massive amounts of data. Let’s start with volume as it is the simplest and probably the first thing that comes to people’s minds when it comes to big data.