🚀 Django's ORM (Object-Relational Mapper) (Python) Django's ORM provides an abstraction layer for interacting with databases. It allows you to perform database operations using Python code instead of writing SQL. The ORM translates Python code into SQL queries, making it easier to work with different database backends (like PostgreSQL, MySQL, and SQLite). Using the ORM improves code readability, reduces the risk of SQL injection vulnerabilities, and simplifies database management. #Python #PythonDev #DataScience #WebDev #professional #career #development

After working for 4+ years as a Python & Django developer and recently diving into FastAPI for AI/ML projects, I had an interesting realization… Django is incredibly powerful. You don’t fully appreciate it until you step outside it. The Django R&D team has already solved so many hard problems for developers: * Authentication & Authorization * Security Middleware * ORM & Database Management * Admin Panel * Protection against common vulnerabilities As developers, we often just plug and play — and everything works smoothly. But when I started working with FastAPI, things changed. FastAPI gives you flexibility and performance ⚡ But it also gives you responsibility. You have to think about: • Is my authentication secure? • Am I handling tokens correctly? • What about middleware, validation, and database safety? • Did I miss any security edge cases? At first, it feels difficult — especially when you’re used to Django handling everything for you. But over time, something interesting happens… 👉 You start understanding systems more deeply 👉 You gain confidence in designing architectures 👉 And honestly… the satisfaction of building things from scratch hits differently Both frameworks are powerful — just in different ways: 🔹 Django = Stability, speed of development, batteries-included 🔹 FastAPI = Control, performance, flexibility Now I enjoy working with both — depending on the problem. 💬 Curious to know: Do you prefer “batteries-included” frameworks like Django, or do you enjoy building things from scratch with FastAPI? #Python #Django #FastAPI #BackendDevelopment #SoftwareEngineering #WebDevelopment #AI #MachineLearning #Developers #CodingLife #TechThoughts Codebasics AtliQ Technologies Rahul Chauhan

💡Python – Simple to Learn, Powerful to Build Python is one of the most beginner-friendly and powerful programming languages. Its clean syntax makes coding easy to read, write, and maintain, while its vast ecosystem allows developers to build anything from automation scripts to scalable web applications. To build strong Python skills for backend development with Django, Flask, and FastAPI, mastering key modules is essential. 🔹 Core Modules: os, sys, datetime, json, re, collections📐 🔹 Backend Utilities: logging, pathlib, functools, argparse 🔹 Web/API Modules: requests, hashlib, uuid, secrets🌐 🔹 Async Programming (FastAPI): asyncio, concurrent.futures🎯 🔹 Database Modules: sqlite3, sqlalchemy, psycopg2♟️🧩 With a solid understanding of these modules, developers can easily build REST APIs, automate tasks, manage databases, and develop scalable backend systems.🖥️🖲️ #Python #Django #Flask #FastAPI #BackendDevelopment #PythonDeveloper #APIDevelopment #SoftwareEngineering

I recently built a high-performance backend project focused on concurrency and optimization. The project is a URL Metadata Fetcher that processes multiple URLs in parallel and extracts key information like page titles and status codes. The main goal was to understand how real-world systems improve speed and efficiency. Instead of handling requests one by one, I implemented asynchronous processing using aiohttp, which significantly reduced response time when working with multiple URLs. To further optimize performance: 1. Added caching to avoid repeated network calls 2. Integrated SQLite for data persistence 3. Measured execution time to compare performance improvements 4. Structured the project using a clean modular architecture 5. Wrote basic test cases using pytest This project helped me understand: 1. How concurrency actually improves performance in backend systems 2. The importance of caching in real-world applications 3. Writing cleaner, maintainable code with proper structure Tech stack used: Python, Flask, aiohttp, BeautifulSoup, SQLite, pytest GitHub Repository: https://lnkd.in/d-qvb9AJ #Python #BackendDevelopment #OpenToWork

Bridging the gap: Connecting a PHP App to Python AI Microservices. There are some situation that you need your PHP system to talk to an external LLM running on a Python Microservices. For the latest update to Profitaa, I built a specialized middleware layer that: ✅ Categorizes text/voice prompts (e.g Transactions/Inventory/e.t.c). ✅ Routes them to dedicated Ollama endpoints. ✅ Translates natural language into secure SQL queries. The "magic" isn't in the prompt—it's in the routing. By treating the LLM as a microservice rather than a monolith, we keep the core application fast and the AI responses precise. These were tokenized too to make the customer pay for each call. **wink #PHP #Python #Microservices #SoftwareArchitecture #LLM

Easily merge ODS files with Python! With the GroupDocs.Merger Cloud SDK for Python, you can seamlessly combine multiple ODS documents using a simple REST API. This powerful tool saves you time and effort, allowing you to focus on more critical tasks. Unlock the potential of your document management process and streamline your workflow today. https://lnkd.in/djKUiCgd

Python & Django is not just a stack — it’s an engineering advantage. In a world of constantly evolving frameworks, one combination continues to prove its strength in production systems: 👉 Python + Django Not because it is trendy — but because it is reliable, scalable, and battle-tested. Django provides what most backend systems struggle to achieve: 🔹 Clear architecture by design A structured framework that enforces best practices instead of relying on discipline. 🔹 Rapid API development without compromising quality With tools like Django REST Framework, building secure and scalable APIs becomes systematic — not improvised. 🔹 Built-in security at the core Protection against common vulnerabilities (CSRF, XSS, SQL injection) is not an add-on — it is part of the framework. 🔹 Scalability through simplicity Clean models, ORM efficiency, and modular design make systems easier to evolve and maintain. In real-world systems, the strength of a backend is not measured by complexity — but by how well it handles growth, change, and integration. 💡 The real power of Django is not just in building APIs — but in building systems that last. Post credit goes to : Mahmoud Abu Haniya #Python #Django #BackendDevelopment #APIs #SoftwareEngineering #SystemDesign #TechForImpact

My Python loop processed 5 reports in 2.5 seconds. After adding one decorator: 0.54 seconds. I changed zero call sites. Here's how it works. A function that's slow because it does real work — database queries, aggregations — gets decorated with @app.direct_task. That's it. The caller doesn't change. Exception handling doesn't change. The return type doesn't change. In development: set one environment variable and the decorator is invisible - tests pass, the function runs inline as it always did. In production: start a worker. The same call now routes to a distributed runner. The caller still blocks and gets the value back directly. No .result. No futures. No refactoring. For parallelism without touching the call site at all: add parallel_func to the decorator with a small helper that describes how to split the input. Pynenc dispatches one task per chunk, collects results, and returns the same type the caller expected. Full write-up + runnable demo (no Docker, no Redis, runs in ~30 seconds): https://lnkd.in/eySD7h_H What's the slowest loop in your codebase right now? #python #backend #distributedsystems #opensource

🐍 Day 25 of My 30-Day Python Learning Challenge 🚀 Project Completed: Log File Analyzer (Python + Streamlit) Over the past few days, I built a mini project that analyzes text files and provides meaningful insights. 📌 Key Features: ✅ Upload any text file ✅ Clean data (remove punctuation) ✅ Remove stopwords (like "the", "is") ✅ Count word frequency ✅ Display top frequent words ✅ Simple web interface using Streamlit --- 📌 Tech Stack: • Python • Streamlit • Basic Data Processing --- 📊 How It Works: 1. Upload a file 2. Data gets cleaned 3. Words are analyzed 4. Results are displayed instantly --- 💡 What I Learned: • Real-world data is messy and needs cleaning • Small improvements build real projects • Converting scripts into apps makes them impactful --- 📂 Next Step: Uploading this project to GitHub and improving UI. (Will share the link soon) --- 🔥 This is my first step toward building real-world applications. #Python #Streamlit #MiniProject #ProjectShowcase #LearningInPublic #SoftwareDeveloper #OpenToWork