The Python web framework Django is an excellent choice for building performant web apps. Disqus, Pinterest, and Instagram are all built on a Django foundation. Django offers simplicity, flexibility, reliability, scalability, and all the tools you need to build a web app from admin to authentication to ORM. But this “batteries included” approach has disadvantages. It can hide a lot of the true complexity of an app from developers. Django ORM is a great example.

Amazon's Elastic Beanstalk service makes deploying and managing high-availability web applications on AWS easier. In this article, learn how to deploy a Python/Django application to Elastic Beanstalk.

Do you need complete control over your production environment? If so, you might want to skip the Platform as a Service (PaaS) offerings and deploy to your own server instead. This article describes deploying a Django application to an Ubuntu server at Linode.

If you've ever wondered if there is a better way to build and consume APIs, you should check out GraphQL—it's better than REST in many cases. This article discusses how to work with GraphQL in Django.

Docker offers many benefits, but it can be tricky to get started—especially when you need to deploy several components. In this article, Muhammed Ali shows how to use Docker and Docker Compose to containerize an application built with Django and React.

Job queues are a powerful tool for processing background work in your Django applications. This article teaches about job queues and workers—and how to implement them with Django Q and Celery.

Building stateful web applications can be tricky, unless you use a framework, of course—Django to the rescue! In this article, learn how to build a realtime chat app using Django Channels and WebSockets.

It doesn’t matter if you’re just starting to learn code or if you’ve been doing it for a few years – sometimes you feel like you have no idea what you’re doing. This feeling can be made even worse by the wealth of information out there, making the right decision can get stressful. Life is full of those tough choices: Coke vs. Pepsi; Yankees vs. Red Sox; Python vs. Flask. You know, regular questions about soda, baseball, and Python frameworks that almost everyone faces.

Python is insanely popular among machine learning enthusiasts these days. Hence, anyone developing a machine learning model normally turns to Python. The real challenge arises at the deployment stage because you can use many frameworks. Figuring out which Python framework to use may add to the confusion. This post discusses two popular machine learning frameworks, Flask and Django. We’ll also compare them side by side, so that you can make the right choice.

Automating your test suite helps get your code to production faster. In this tutorial, you'll learn how to write unit tests for Django and run them with GitHub Actions.