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Django in Production

Django in Production

By : Arghya Saha
4.7 (9)
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Django in Production

Django in Production

4.7 (9)
By: Arghya Saha

Overview of this book

You may have got your first Django developer job after a six-week bootcamp or online course, and that’s great, but what’s next? In small companies, mentorship can be hard to come by and gaining the traits of a senior developer without that can take a long time. This is precisely where Django in Production comes into play. This book will first delve into the true meaning of "good practice" and help you understand the rationale behind industry professionals building websites in specific ways to develop a solid foundation for your Django projects. Next, you will uncover hidden Django secrets through hands-on exploration, leveraging the power of Docker and version control to your advantage. You will gain insights into mastering Git hooks for efficient code maintenance, establishing a robust CI pipeline, and harnessing the capabilities of AWS Beanstalk. These tools will empower you to develop highly scalable products—an essential skill set for aspiring developers transitioning from junior to senior roles. Later, you will understand the significance of monitoring and be introduced to industry-standard tools utilized by professionals for effective monitoring practices. By the end of this book, you will have set yourself apart from the crowd, equipped with the knowledge and expertise to thrive as a seasoned Django developer.
Table of Contents (21 chapters)
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1
Part 1 – Using Django and DRF to Build Modern Web Application
7
Part 2 – Using the Advanced Concepts of Django
13
Part 3 – Dockerizing and Setting Up a CI Pipeline for Django Application
16
Part 4 – Deploying and Monitoring Django Applications in Production

Using models and Django ORM

For any organization, data is one of the most important foundational elements in Django, and the models.py file is used to structure how data is stored in the database. A well-architected system would have a strong Django model foundation, where the developer has spent enough time and done proper research before creating the database schema.

One of the most common questions developers have while designing the schema is whether to perform normalization or denormalization. The answer is always “it depends.” It depends on multiple factors, such as the data access pattern and the performance bottleneck we are trying to solve. So, as a rule of thumb, it is always preferred to start with a normalized database schema design and then, as we find different use cases, try to solve the problem by introducing caching or indexes, and then finally perform denormalization.

Important note

We won’t focus on the basics of how to work with Django...

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