Rust Replaces Python in Data Engineering Ecosystem

🚀 Python 3.14 — Back to the Interpreter. Back to the Basics. Today I went back to where everything starts: An Informal Introduction to Python. https://lnkd.in/d4NN7cmG # Launch Python 3.14 explicitly (Windows launcher) C:\Users\John> py -3.14 # This is a comment → ignored by Python # Remember. This is a comment. # This is NOT a comment because it's inside quotes text = "# This is not a comment." # Addition 7 + 4 # Subtraction 50 - 37 # Order of operations (multiplication first) (100 - 5 * 7) # True division → float 17 / 3 # Floor division → integer 17 // 3 # Modulo → remainder 17 % 3 # Exponentiation 2 ** 10 # Store resolution values width = 1920 height = 1080 # Calculate total pixels (Full HD) width * height 💥 Fail Fast # Access undefined variable size → NameError 🔁 REPL Superpower: _ # `_` holds the last result in interactive mode width - _ 🎯 My Take Deep systems aren’t built on complexity. They’re built on mastery of fundamentals. Whether you’re building: A Django backend A distributed system An AI-powered application It all starts here — with clean thinking. “If you want to fly high, take a deep dive.” #Python #Django #Backend #SoftwareDevelopment #DeepDive

oday I explored how backend works and understood how Python connects logic with real-world applications. ✔️ Revised core concepts ✔️ Learned about how servers handle requests ✔️ Strengthened my understanding of problem-solving Slowly moving from just writing code ➝ to understanding how systems actually work. Consistency + Curiosity = Growth 💡 #Python #BackendDevelopment #LearningInPublic #TechJourney 

Rust is much faster than Python or JS. It's also statically typed and less likely to fail at runtime. In the era of LLM assistants, what justifies writing any new (backend) software in Python or JS?

🚀 Python Multithreading in Action 🧵⚡ Today I practiced Multithreading in Python using the threading module. 🔹 Created two thread classes 🔹 Overrode the run() method 🔹 Executed tasks simultaneously using .start() 💡 What I Learned: ✅ Threads run concurrently ✅ start() internally calls run() ✅ sleep() controls execution timing ✅ Output may interleave because threads execute in parallel 🧠 Code Concept: Class A(Thread) → prints "Hello" Class B(Thread) → prints "Pune" Both threads run 5 times with a 2-second delay Output runs simultaneously ⚡ This is the power of concurrent execution in Python. 🔥 Why Multithreading? ✔ Improves performance ✔ Best for I/O-bound tasks ✔ Used in real-world apps like: Web servers 🌐 Background processing ⚙ APIs 🔄 💬 Small practice today… 🏆 Big step towards becoming an Advanced Python Developer. #Python #Multithreading #Threading #AdvancedPython #CodingJourney #WomenInTech #MCA #LearningDaily

Dear network, I recently studied “Architecture Patterns with Python – Enabling TDD, DDD, and Event‑Driven Microservices” and turned my reading into structured, code‑oriented notes. It completely refreshed my thinking about domain models, repositories, service layers, and the Unit of Work in real Python backends. If you’re a Python/Django/FastAPI developer who wants cleaner architecture and more reliable tests, you might find these notes useful. link to the notes: https://lnkd.in/dcHuB2ZG #python #django #fastapi #cleanarchitecture #ddd #tdd #pythondeveloper #backendengineering #microservices

Stop "Awaiting" Everything: The Hidden Cost of Async Python 🐍 Is your Python codebase turning "Red"? In the world of FastAPI and modern web frameworks, we’ve fallen into a trap: the belief that prefixing every function with async makes our code "faster." But if you’re using async for simple logic or CPU-heavy tasks, you might actually be: 1. Adding "Micro-Stalling": Forcing simple logic through the event loop's scheduling machinery actually slows it down. 2. Hogging the Loop: One CPU-bound "async" function can freeze your entire server. 3, Increasing Cognitive Load: When everything is awaitable, nothing stands out as a genuine I/O bottleneck. I just wrote a deep dive on why "Sync" is often the superior choice for internal logic, data science, and simple utility functions. Check out the full breakdown here:

🔹 Understanding Python Architecture 🐍 🔹 Ever wondered what happens behind the scenes when you run a Python program? Let’s break down Python architecture in a simple way 👇 🧩 1. Source Code (.py) This is the code we write as developers. ⚙️ 2. Compiler Python compiles the source code into bytecode (not machine code directly). 📦 3. Bytecode (.pyc) An intermediate, platform-independent code stored in the "__pycache__" folder. 🚀 4. Python Virtual Machine (PVM) The heart of Python! It reads bytecode and executes it line by line. 💾 5. Memory Management Python automatically handles memory using heap, stack, and garbage collection. 🔄 Execution Flow: Source Code → Bytecode → PVM → Output 💡 Key Takeaway: Python is both compiled and interpreted, making it portable and easy to use across platforms. #Python #Programming #SoftwareDevelopment #Coding #TechExplained #Learning

🚀 What’s happening in Django & Python? (Week 9, 2026) This week’s updates are actually pretty wild—especially if you're into AI pipelines or high-performance Python. Here’s the breakdown: 🎸 The Django Side Control Room is a lifesaver: You can finally manage Redis and cache panels directly from the Django admin. No more jumping between tools to see what’s happening under the hood. 🛠️ Django-guardian 3.3.0: It’s now ready for Python 3.14. Plus, they’ve cut down on DB queries for permissions, which is a huge win for performance. ✨ AI Pipelines are evolving: There’s a new workflow using Firecrawl and Pydantic AI agents. If you're building directory sites, this basically automates the headache of data extraction and validation. 🤖 GSoC 2026: Django is officially back as a mentoring org! If you’ve been wanting to contribute to open source, now is the time to prep. 🎓 🐍 The Python Side Starlette 1.0.0rc1: Can you believe it’s been 8 years? The first release candidate for 1.0.0 is finally out. Huge milestone for the ecosystem. 🥂 Free-threaded Python (Barq): This one is for the performance geeks. Barq is an experimental HTTP library skipping the usual async models to explore true parallelism. ⚡ Type Grammar (PEP 827): Python typing is getting even more flexible with conditional expressions and new annotations. 💻 City2Graph: If you work with ML and maps, check this out. It turns geospatial data into graph structures, making it way easier to process.🗺️ Personal Take: That AI Content Pipeline is probably the biggest productivity hack on this list if you’re doing any kind of web scraping or data curation right now. ⏳ #Python #Django #WebDev #OpenSource #AI #SoftwareEngineering #code2encoder

Midiscript.org | The core repo is a small Python DSL that turns files into… The core repo is a small Python DSL that turns `.ms` files into MIDI. Under the hood it’s a CLI + lexer + parser + MIDI generator.

🎙️ TALK: Digitising Historical Caving Data with Python and Django by Andrew Northall Let's explore a pipeline using Python, Django, and LLMs to extract, structure and publish 2,700 incidents, turning old typewritten documents into useful data. 🎟️ https://lnkd.in/dXN83nct #djangocon #django #djangoconeu2026