⚡ Shipped: Full-Stack Milestone Tracker with Smart Analytics Just deployed a production-ready learning management dashboard with some interesting technical features: 🔧 Technical Highlights: • RESTful API with FastAPI (async/await patterns) • MongoDB aggregation pipeline for real-time metrics • JWT-based authentication system • Auto-backup scheduling with APScheduler • Docker containerization for consistent deployments • CI/CD via GitHub Actions → Render • Keep-alive workflow to maintain uptime on free tier 📊 Features: • Dynamic question-to-milestone mapping algorithm • 6-stage progress tracking (Ready → Complete) • Chart.js visualizations + Excel/PDF exports • Responsive UI with dark mode • Admin panel for CRUD operations 🌐 Live: https://lnkd.in/dxvsWFaA 📚 Stack: Python 3.11 | FastAPI | Motor (async MongoDB) | Vanilla JS | Chart.js Built initially for personal use at IITian Academy Milestone Tracker. Planning to generalize it for wider adoption! Open to feedback from the dev community! What would you add? 💬 #FastAPI #Python #MongoDB #FullStack #WebDevelopment #DevOps #GitHubActions #Docker #APIs #BackendDevelopment #iitian

🚀 Day 40 of #100DaysOfCode — Learning FastAPI (Python) Today I explored FastAPI, one of the fastest and most modern frameworks for building APIs in Python ⚡ 🔍 What I learnt today: ✅ How to create a simple FastAPI app ✅ Setting up GET and POST routes ✅ Automatic interactive documentation using Swagger UI ✅ How FastAPI handles data validation with Pydantic ✅ Why it’s much faster and developer-friendly compared to Flask & Django REST 💡 Key takeaway: FastAPI makes building APIs feel effortless — it’s super fast, type-safe, and easy to maintain. Perfect for modern backend development! 🧠 Next, I’ll explore CRUD operations and database integration using FastAPI + SQLAlchemy. #Python #FastAPI #BackendDevelopment #100DaysOfCode #LearningJourney #APIDevelopment

Built Objectron, a lightweight custom ORM written entirely from scratch in Python. >> Key Features: > Model-to-Table mapping using Python classes > Field Descriptors with schema constraints (primary key, nullable, unique) > Session Management via the Unit of Work pattern > Clean Connection Handling > Database Agnostic through Adapters (currently supports SQLiteDialect) > Dynamic Query Builder with chaining, filters, and sorting Objectron is an open-source ORM built to serve as a learning-friendly reference for understanding ORM internals. Building it from scratch also gave me a much deeper understanding of OOP concepts. Repo link: https://lnkd.in/dDdVg-cb

How to work with databases 𝐬𝐦𝐨𝐨𝐭𝐡𝐥𝐲 in Python? 🐍 Use 𝐒𝐐𝐋𝐀𝐥𝐜𝐡𝐞𝐦𝐲 — easily create tables, insert, update, or query data directly from your code 💡 Use 𝐀𝐥𝐞𝐦𝐛𝐢𝐜 — safely change your database structure over time. No risky manual edits, just clean migrations. It's kinda git for DB. Just necessary to add migrations step into your CI/CD ⚙️ Started using them heavily in my recent project at work. 💻 If you’re building backend services with FastAPI or any modern stack — these two are 𝐦𝐮𝐬𝐭-𝐡𝐚𝐯𝐞𝐬. 🚀 #database #in #python

🚀 The `json` Library: Working with JSON Data (Python) The `json` library allows you to encode and decode JSON (JavaScript Object Notation) data in Python. JSON is a common data format used for data interchange between applications. The `json` library provides functions for converting Python objects to JSON strings (serialization) and converting JSON strings to Python objects (deserialization). This library is essential for interacting with APIs and working with data in web applications. 💪 Stop scrolling. Start learning! 💡 Master tech faster — 10,000+ bite-sized concepts, 4,000+ in-depth articles, and 12,000+ practice questions await! 📲 Download the app: https://lnkd.in/gefySfsc 🔗 Check it out: https://techielearn.in #Python #PythonDev #DataScience #WebDev #professional #career #development

Streamlit offers a straightforward approach to converting Python scripts into interactive applications, eliminating the need for complex web frameworks. When paired with InfluxDB 3 Core, devs can build powerful real-time analytics dashboards right in Python. Why Streamlit + InfluxDB 3 Core? 🧐 ✅ 𝐓𝐢𝐦𝐞-𝐒𝐞𝐫𝐢𝐞𝐬 𝐅𝐨𝐜𝐮𝐬: InfluxDB 3 Core is optimized for handling high-ingest, time-stamped data. ✅ 𝐏𝐲𝐭𝐡𝐨𝐧 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧: The InfluxDB 3 Python client makes querying and analyzing data straightforward. ✅ 𝐑𝐚𝐩𝐢𝐝 𝐏𝐫𝐨𝐭𝐨𝐭𝐲𝐩𝐢𝐧𝐠: Streamlit allows you to create bespoke, interactive Python applications in minutes. ✅ 𝐅𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲: Developers can add custom logic, machine learning models, or specialized visualizations easily. https://bit.ly/4n6jVLd

Day 45 – Packages vs Modules (The Missing Piece) Heard of modules and packages but not sure what’s what? Let’s simplify 👇 📦 Module → A single .py file 📦 Package → A folder that contains multiple modules + an __init__.py file Example: my_package/ __init__.py math_ops.py string_ops.py You can use them like: from my_package import math_ops math_ops.add(5, 10) Packages = modularity + reusability. This is how Python frameworks like Django or Flask are structured internally.💡 👉 Small, modular codebases are easier to grow and maintain. #Python #SoftwareEngineering #CleanCode #100DaysOfCode

🚀 Reviving My 2020 Archives – Advanced Python Tutorials (Part 1) Back in 2020, I created a series of in-depth Python tutorials covering topics many developers overlook — but every engineer should master. I’m bringing them back, polished for 2025 🧩 Today’s Topic — Decorators in Python They may look simple, but decorators form the foundation of some of the most powerful frameworks we use daily — FastAPI, Flask, Django, and Airflow. 💻 Here’s one of the examples from my GitHub repo: 👉 https://lnkd.in/gbw7Txec ``` def be_polite(fn): def wrapper(): print("What a pleasure to meet you") fn() print("Have a great day") return wrapper def greet(): print("My name is Aman") Output : What a pleasure to meet you! My name is Aman Have a great day! ``` 🎯 This is where Python’s elegance truly shines — functions enhancing other functions seamlessly. 🧠 Why it matters: Decorators let you wrap shared logic (like logging, retries, validation) around multiple functions — without repeating code. 🧠 In real-world data systems, decorators enable logging, authentication, retries, caching, and orchestration — powering tools like Airflow DAGs and FastAPI routes. 🔗 Full tutorial with explanations and multiple examples here: 👉 https://lnkd.in/gbw7Txec #Python #DataEngineering #AmanpreetSingh #AdvancedPython #FastAPI #Flask #Django #Airflow #CleanCode #DataSolutions #LinkedInLearning #100DaysOfCode #SoftwareEngineering #BackendDevelopment #ProgrammingTips #LearningSeries #DeveloperCommunity #GitHubProjects

This week has been about Python in Node.js, modern CSS colour functions, graph queries in DuckDB and an article on LLMs explicitly ignoring ethical constraints. https://lnkd.in/eKmetcJQ

🚀 Day 3 of learning Django Rest Framework (DRF) Today, I explored one of the most important topics in DRF — Validations and ModelSerializer 🔥 Here’s what I learned today 👇 ✅ Field-level validation – Used to validate a single field. ✅ Object-level validation – Used when you need to check multiple fields together. ✅ Custom validators – You can even create your own validation functions to handle specific logic. ✅ ModelSerializer – This makes life easier! Instead of writing every field manually, ModelSerializer automatically generates fields from the model. It also handles validation automatically, saving a lot of time ⏱️ Overall, I learned how validation keeps APIs clean, reliable, and secure, and how ModelSerializer helps write less code with more power 💪 #Django #DRF #Python #BackendDevelopment #LearningJourney #ModelSerializer #APIDevelopment #WebDevelopment