Book Image

Python for Geeks

By : Muhammad Asif
Book Image

Python for Geeks

By: Muhammad Asif

Overview of this book

Python is a multipurpose language that can be used for multiple use cases. Python for Geeks will teach you how to advance in your career with the help of expert tips and tricks. You'll start by exploring the different ways of using Python optimally, both from the design and implementation point of view. Next, you'll understand the life cycle of a large-scale Python project. As you advance, you'll focus on different ways of creating an elegant design by modularizing a Python project and learn best practices and design patterns for using Python. You'll also discover how to scale out Python beyond a single thread and how to implement multiprocessing and multithreading in Python. In addition to this, you'll understand how you can not only use Python to deploy on a single machine but also use clusters in private as well as in public cloud computing environments. You'll then explore data processing techniques, focus on reusable, scalable data pipelines, and learn how to use these advanced techniques for network automation, serverless functions, and machine learning. Finally, you'll focus on strategizing web development design using the techniques and best practices covered in the book. By the end of this Python book, you'll be able to do some serious Python programming for large-scale complex projects.
Table of Contents (20 chapters)
1
Section 1: Python, beyond the Basics
5
Section 2: Advanced Programming Concepts
9
Section 3: Scaling beyond a Single Thread
13
Section 4: Using Python for Web, Cloud, and Network Use Cases

Summary

In this chapter, we discussed the role of Python for developing applications for cloud deployment in general, as well as the use of Apache Beam with Python for deploying data processing pipelines on Google Cloud Dataflow. We started this chapter by comparing three main public cloud providers in terms of what they offer for developing, building, and deploying different types of applications. We also compared the options that are available from each cloud provider for runtime environments. We learned that each cloud provider offers a variety of runtime engines based on the application or program. For example, we have separate runtime engines for classic web applications, container-based applications, and serverless functions. To explore the effectiveness of Python for cloud-native web applications, we built a sample application and learned how to deploy such an application on Google App Engine by using Cloud SDK. In the last section, we extended our discussion of the data process...