Python Web Frameworks for 2026: Django, FastAPI, Flask

Today I learned about Models in Django and finally understood the concept of ORM. ORM stands for Object Relational Mapper. It means we can create and manage database tables using Python instead of writing SQL directly. In Django, we define tables inside the models.py file. Each model class represents a table, and each attribute inside the class represents a column. What I understood: We write everything in Python. Django automatically converts it into database queries. No need to manually create tables in SQL. 🔹 Some Common Fields I Learned: CharField() – used to store text (like name, title, etc.) TextField() – used for long text IntegerField() – used to store numbers FloatField() – used for decimal values BooleanField() – used for True/False DateField() – used for dates EmailField() – used to store email addresses After creating models, we run migrations so Django can create the tables in the database. Today’s learning helped me understand how Django connects Python code with the database smoothly. Slowly everything is connecting — Views, Templates, and now Models. #Django #Python #WebDevelopment #BackendDevelopment #LearningJourney

🚀 Backend Journey: Day 4 - Writing Python, Generating Tables! 📌 Topic: The Power of Django Models and the ORM Yesterday, I broke down the MVT architecture. Today, I zoomed in on the "M" — the Model. In the past, whenever I thought about interacting with databases, I pictured writing long, complex SQL queries to create tables, insert records, and fetch data. Django handles this completely differently through its ORM (Object-Relational Mapping). Here is what I learned today about how Django manages data: 🔹 Classes over Queries: Instead of writing raw SQL like CREATE TABLE users..., I simply define a Python class. Each attribute in the class (like name = models.CharField(...)) automatically translates into a database column. It feels incredibly intuitive. 🔹 Database Agnostic: Because I'm writing Python, Django translates that code into the correct SQL for any database. I can develop locally using lightweight SQLite and switch to a robust PostgreSQL database for production by changing just one line in my settings. No need to rewrite any queries! 🔹 Built-in Security: By using the ORM to interact with the database, Django automatically parameterizes queries behind the scenes. This provides massive built-in protection against SQL Injection attacks. 🧠 Key Insight: The ORM bridges the gap between object-oriented programming and relational databases. It allows me to interact with my data purely as Python objects, which speeds up development tremendously and keeps the codebase clean and secure. Next up: I'll be exploring Migrations—how Django keeps track of all these database changes like a version control system for your database! 👇 Question for the experienced devs: Do you prefer the speed and safety of an ORM, or do you still drop down to raw SQL when dealing with highly complex queries? #Python #Django #DatabaseDesign #ORM #BackendDeveloper #SQL #SoftwareEngineering #LearningInPublic #WebDevelopment

FastAPI with SQLAlchemy: Building a Robust API with Databases In the world of web development, building robust and scalable APIs is crucial. Databases are the backbone of most applications, storing and managing the critical data that drives them. FastAPI, a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints, has gained immense popularity for its speed, simplicity, and ease of use. SQLAlchemy, a powerful and versatile SQL toolkit and Object-Relational Mapper (ORM) for Python, provides a high-level abstraction for interacting with databases, allowing developers to work with Python objects instead of writing raw SQL queries....

🔥 90 Days of Python Full Stack – Day 50 Django ORM Queries & CRUD Operations Today, I explored how to interact with the database using Django’s powerful ORM (Object Relational Mapping) system. After defining models, the next step is performing real operations on data — and that’s where CRUD comes in. 🔹 Concepts Covered ✅ Creating records using Model.objects.create() ✅ Retrieving data using .all(), .get(), .filter() ✅ Updating records ✅ Deleting records ✅ Understanding QuerySets ✅ Ordering & filtering data ✅ Using Django ORM instead of raw SQL 💡 Why This Matters Django ORM allows us to: Write Python instead of SQL Keep code clean and readable Prevent SQL injection risks Build dynamic, data-driven applications Instead of writing: SQL SELECT * FROM products WHERE price > 500; We write: Python Product.objects.filter(price__gt=500) Cleaner. Safer. More Pythonic. 📌 Day 50 completed — making Django applications fully dynamic with real database operations. Now the backend is officially alive 🚀 👉 What do you prefer: Raw SQL control or ORM abstraction? #90DaysOfPython #Django #PythonFullStack #BackendDevelopment #WebDevelopment #LearningInPublic

Object Relational Mapping is the main part of Django basically used for fetching the python objects....because Django follows Model view template architecture and the ORM is responsible for handling the Model part .ORM simplifies the Common database CRUD operations and to be honest the most fascinating feature of Django ORM is the in built database migrations that seriously helps to track the changes in our database schema so for Django ORM is >>>>❤️ But One crucial suggestion i wanna give everyone that whenever you are going to deploy your django application in any Cloud application whether it's Render or any cloud service try avoid SQLite db for long term purposes because sometimes it acts as a serverless in Productions . So it is always better to use PostgreSQL rather than SQLite3 and for that purpose you can use Supabase because you can easily get the Database URL link from there...The same thing is applicable for flask also because flask uses #Sqlalchemy for ORM and SQLite3 as database URL so you have to change that into a PostgreSQL #Django #ORM #Fullstackapplication #python #supabass #Backend #web #developement #Flask #Database #Data

In my latest tutorial, I explored the transition from TinyDB to SQLite when building Python web applications. While TinyDB is great for beginners because it stores data in simple JSON files and is easy to set up, it has limitations as applications grow. Querying large datasets, managing structured data, and handling multiple users can become challenging. That’s why I introduced SQLite as a practical alternative. SQLite provides a lightweight relational database with powerful SQL queries, better data integrity, and improved performance—all without requiring a separate database server. For developers building apps with Python and Flask, SQLite is often the next logical step when moving from simple prototypes to more structured applications. Here is the tutorial link: https://lnkd.in/g8UYe2pz #Python #Flask #SQLite #WebDevelopment #Programming #Database

I built a small tool that converts files to Markdown using Docker — no Python setup required. Drop a .docx, .pdf, or .xlsx file into a folder, run a single make command, and get clean Markdown back. That's it. It wraps Microsoft's `markitdown` in a container so your host stays clean. Deterministic paths, quiet output, and zero local dependencies beyond Docker and Make. Useful if your stack is Go, Node, PHP, Java, TypeScript, and you don't want to maintain a Python environment just for file conversion. #markdown #agents #llms #docker #agentic #coding https://lnkd.in/gcvx4Quu

Writing a Django query does not mean it hits the database! Writing a Django query and hitting the database are two different things. The ORM makes it easy to forget the difference between these. Here's what actually happens: 1. When a QuerySet is written, Django builds a query object internally. 2. This has no SQL. No database connection. Just a description of what data is needed. 3. This description can be chained, filtered, sliced, and passed around as required. Still no database hit. 4. The database is only contacted at the moment of evaluation, when Python actually needs the data. This is powerful when used intentionally. Build a complex query across multiple functions, evaluate once, pay the DB cost once! This can backfire too! A QuerySet evaluated inside a loop, once per iteration, is the N+1 problem. Takeaway: -> Efficiency: Chain and pass QuerySets, zero DB cost until evaluation -> Danger: Evaluation inside loops fires one query per row silently -> Control: Knowing evaluation points is the foundation of ORM performance I’m deep-diving into Django internals and performance. Do follow along and tell your experiences in comments. #Python #Django #DjangoInternals #SoftwareEngineering #BackendDevelopment

🔄 Day 6: What is an ORM? (And Why It Matters in Django) When you use Django models, you’re not writing SQL directly. You’re using an ORM — Object Relational Mapper. An ORM lets you interact with the database using Python code instead of raw SQL. Instead of: SELECT * FROM users WHERE id = 1; You write: User.objects.get(id=1) Sounds simple — but here’s what really matters: • ORM converts Python objects into SQL queries • It handles relationships automatically • It prevents common SQL mistakes • It improves development speed But… If you don’t understand what’s happening behind the scenes, you can accidentally write slow queries. Good backend developers: ✔ Use ORM smartly ✔ Understand generated SQL ✔ Optimize queries when needed ORM is powerful — but only when you know how it works internally. Have you ever debugged a slow ORM query? #BackendDevelopment #Django #ORM #Python #WebDevelopment

>> 🧠 Ever wondered how websites are scraped to collect useful data? → While working on data-related tasks, we often need information that exists on websites → Instead of manually copying data, we can automate the process using web scraping → One of the most beginner-friendly Python libraries for this is Beautiful Soup ✍ What is Beautiful Soup? → It is a Python library used to parse HTML and XML documents → It helps to navigate, search and modify the HTML content fetched from the website → It transforms the complex HTML document into a tree structure where each node corresponds to an element, such as tags, text and attributes and makes it easy to locate and extract specific information 💡 Why use Beautiful Soup? → Easy to learn and use → Flexible parsing → Handles broken HTML → Efficient navigation and search functions → Integration with other libraries → well-documented and active community 🔗 Check out the repository and notes below for hands-on practice and a deeper understanding 👇 https://lnkd.in/gHHqZQ4b #Python #WebScraping #BeautifulSoup #DataScience #MachineLearning #PythonProgramming