Django Python PostgreSQL Evolution: Async Cloud-Native AI

Spontaneity is great. But sometimes a little structure goes a long way. 🗂️ That's the philosophy behind a tutorial I'd recommend to any Python developer this week: building a drag-and-drop Kanban task manager with Django and MongoDB Atlas. The project, called "Get Going," covers: → MongoDB Atlas Free Tier as the backend database → Django MongoDB Backend for data handling → HTMX for smooth, reactive UI without complex JavaScript Worth bookmarking! https://lnkd.in/eqUzwcaf

Spontaneity is great. But sometimes a little structure goes a long way. 🗂️ That's the philosophy behind a tutorial I'd recommend to any Python developer this week: building a drag-and-drop Kanban task manager with Django and MongoDB Atlas. The project, called "Get Going," covers: → MongoDB Atlas Free Tier as the backend database → Django MongoDB Backend for data handling → HTMX for smooth, reactive UI without complex JavaScript Worth bookmarking! https://lnkd.in/eK5UqSvN

Spontaneity is great. But sometimes a little structure goes a long way. 🗂️ That's the philosophy behind a tutorial I'd recommend to any Python developer this week: building a drag-and-drop Kanban task manager with Django and MongoDB Atlas. The project, called "Get Going," covers: → MongoDB Atlas Free Tier as the backend database → Django MongoDB Backend for data handling → HTMX for smooth, reactive UI without complex JavaScript Worth bookmarking! https://lnkd.in/ePTE5Jme

Spontaneity is great. But sometimes a little structure goes a long way. 🗂️ That's the philosophy behind a tutorial I'd recommend to any Python developer this week: building a drag-and-drop Kanban task manager with Django and MongoDB Atlas. The project, called "Get Going," covers: → MongoDB Atlas Free Tier as the backend database → Django MongoDB Backend for data handling → HTMX for smooth, reactive UI without complex JavaScript Worth bookmarking! https://lnkd.in/dx7MgurP

𝐖𝐡𝐚𝐭 𝐢𝐟 𝐲𝐨𝐮 𝐧𝐞𝐯𝐞𝐫 𝐡𝐚𝐝 𝐭𝐨 𝐰𝐫𝐢𝐭𝐞 𝐫𝐚𝐰 𝐒𝐐𝐋 𝐚𝐠𝐚𝐢𝐧? That's Django's ORM (Object-Relational Mapper) in a nutshell. An ORM lets you interact with your database using Python instead of SQL. You define your data as Python classes (called models), and Django handles the database queries behind the scenes. Here's a simple example: Instead of writing: SELECT * FROM blog_post WHERE author_id = 1; You write: Post.objects.filter(author_id=1) Same result. Pure Python. No SQL dialect to worry about. What makes the Django ORM powerful: → Works with PostgreSQL, MySQL, SQLite, Oracle. Same code, different DB → Migrations: Change your model, run a command, database updates automatically → Querysets that are chainable, lazy, and incredibly readable → Relationships like ForeignKey, ManyToMany, OneToOne, all handled elegantly I've worked with Oracle 19c and PostgreSQL in production. The ORM made switching between them painless which required just a config change. The caveat? For very complex queries, raw SQL is still available. The ORM doesn't replace SQL knowledge. It reduces how often you need it. If you're building data-heavy apps, learning the Django ORM deeply is one of the best investments you can make. Tomorrow: Django REST Framework, turning Django into an API powerhouse. #Django #ORM #Python #BackendDevelopment #Database

Just published a new article 🐳 "Django in Docker: Base Images, PostgreSQL & Migrations" If you've ever broken a Django setup because your teammate had a different Python version or no PostgreSQL installed — this one's for you. In this article, I cover: → Choosing the right Python base image (slim vs alpine vs full) → Pinning dependencies in requirements.txt the right way → Wiring Django + PostgreSQL using Docker Compose → Running migrations inside containers (3 approaches) → Fixing the "PostgreSQL not ready" race condition Built for students and developers just getting started with Docker + Django. Practical commands, common mistakes, and a hands-on challenge at the end. 🔗 https://lnkd.in/gJK_xi8F #Docker #Django #Python #DockerCompose #PostgreSQL #DevOps #CloudNative #DockerCaptain #DockerHOL

Django ORM Internals and Query Optimization — What Every Backend Developer Should Understand What is Django ORM Really Doing? The Django ORM is an abstraction layer that converts Python code into SQL queries. When you write: books = Book.objects.all() Django does not immediately hit the database. Instead, it creates a QuerySet — a lazy object that represents the SQL query. The actual database call happens only when the data is evaluated. Examples of evaluation: Iterating over QuerySet Converting to list Accessing elements This concept is called lazy loading. How QuerySets Work Internally A QuerySet goes through multiple steps: Query construction Django builds a SQL query internally using a query compiler Optimization It decides joins, filters, and conditions Execution The query is sent to the database Result caching Results are stored to avoid repeated queries This means: Reusing the same QuerySet can save queries Creating new QuerySets repeatedly can hurt performance The Real Problem: N+1 Queries One of the biggest mistakes developers make: books = Book.objects.all() for book in books: print(book.author.name) This creates: 1 query for books N queries for authors This is inefficient and slows down applications at scale. Optimization Techniques 1.select_related() Used for ForeignKey and OneToOne relationships. books = Book.objects.select_related('author') This performs a SQL JOIN and fetches related data in a single query. 2.prefetch_related() Used for ManyToMany or reverse relationships. authors = Author.objects.prefetch_related('books') This runs separate queries but combines results efficiently in Python. 3.only() and defer() Fetch only required fields: Book.objects.only('title') Reduces data transfer and speeds up queries. 4.values() and values_list() Return dictionaries or tuples instead of full model objects: Book.objects.values('title', 'price') Useful for APIs and data-heavy operations. Why This Matters Poor ORM usage leads to: Slow APIs High database load Bad user experience Optimized queries result in: Faster response times Better scalability Efficient resource usage #Python #Django #ORM #BackendFramework #BackendDevelopment #SoftwareDevelopment #QuerySets #SQL #Optimization #Scalable #Fast_API_Response

Django 6.0 introduces a built-in task framework that replaces Celery entirely for InboxToKindle (https://inboxtokindle.com). This new approach eliminates the need for Redis, RabbitMQ, or a separate worker process, as tasks are now managed within PostgreSQL alongside other data. I have detailed how this framework operates in production, covering the pipeline, the self-rescheduling technique for periodic tasks, and when it may still be appropriate to use Celery. In summary, most Django projects require background tasks that function effectively. You can read more about it here: https://lnkd.in/eGWyjQ_U What is your current setup? Are you still using Celery, or have you explored lighter alternatives? #django #python #celery #webdevelopment #backend

Building Scalable Web Applications with Python, Django, Fast API, PostgreSQL, and Amazon Web Services, In today’s digital landscape, building scalable and high-performance web applications requires a well-integrated technology stack. A powerful combination widely adopted by modern development teams includes Python as the core programming language, along with Django and Fast API for backend development, PostgreSQL for reliable data management, and Amazon Web Services for cloud deployment and scalability. This combination enables organisations to build applications that are not only efficient but also flexible and future-ready. Python plays a central role in this stack due to its simplicity, readability, and extensive ecosystem. It allows developers to write clean and maintainable code while accelerating development timelines. Its versatility makes it suitable for a wide range of applications, from web development to data processing and automation, making it a preferred choice for modern software development. For backend development, Django and Fast API serve complementary purposes. Django is a high-level framework designed for rapid development and comes with built-in features such as authentication, an admin panel, and an object-relational mapper, making it ideal for building structured and secure applications. Data management is handled efficiently by PostgreSQL, a powerful open-source relational database system known for its reliability and advanced capabilities. It supports complex queries, ensures data integrity through ACID compliance, and integrates seamlessly with both Django and Fast API. This makes it an excellent choice for applications that require consistent and scalable data handling. When combined, this technology stack creates a robust architecture where the frontend communicates with backend services powered by Django and Fast API, data is securely stored and managed in PostgreSQL, and the entire application is deployed and scaled using AWS. This integrated approach ensures high performance, scalability, and reliability across all layers of the application. In real-world scenarios, such as building an e-commerce platform, Django can handle user authentication and administrative operations, Fast API can manage real-time services like order processing and payment integration, PostgreSQL can store transactional data, and AWS can ensure the application remains available and scalable under varying loads. This synergy allows development teams to deliver high-quality applications that meet modern user expectations. Overall, combining Python with Django, Fast API, PostgreSQL, and Amazon Web Services provides a strong foundation for building scalable, secure, and high-performing applications, making it an ideal choice for modern full-stack development.

Most Django developers think migrations are simple… until production goes down at midnight. I’ve seen migrations: • Lock massive tables • Break rolling deployments • Corrupt production data So I wrote a complete production guide on Django migrations 👇 In this carousel, I break down: • Zero-downtime migration strategy • Expand-contract pattern • Data migration best practices • Real production mistakes to avoid If you're working with Django in production, this is something you must understand deeply. Full article: https://lnkd.in/dKUjjs-7 If you found this useful, share it with your team 👇 #Django #Python #BackendDevelopment #WebDevelopment #SoftwareEngineering #FullStackDeveloper #Programming #Database #PostgreSQL #DevOps #SystemDesign #Coding #TechLeadership #Developers #LearnToCode