Optimizing Python API Performance with Async I/O

Currently working through a Flask tutorial. At this stage, I’ve been learning and implementing: • Flask routing and request handling (GET vs POST) • User authentication and session management • Extending data models and handling migrations • Server-side forms with validation • Tracking user activity with UTC timestamps • Using Jinja templates and template inheritance It’s been helpful in understanding how backend logic, database state, and templates come together in a real Flask application. #Python #Flask #WebDevelopment #BackendDevelopment #LearningInPublic

A common pain point for growing local businesses: The "Slow Dashboard." I recently audited a legacy Python backend where a single report generation was taking 45 seconds. The fix wasn't a faster server or a complex cache. It was fixing a series of N+1 queries and adding a composite index on the database. The Result: 45 seconds down to 0.8 seconds. If your software is slowing down as your business grows, you usually don't need a total rewrite. You need a targeted performance audit. #Python #DatabaseOptimization #Performance #LocalBusinessTech

Most Python bugs don’t come from bad logic. They come from bad data. APIs send "N/A", CSVs send empty strings, users send creative inputs, and suddenly your clean code starts behaving unpredictably. Instead of scattering defensive checks across your codebase, you can normalize and validate data at the model level with Pydantic. What this gives you: - Cleaner downstream logic - Fewer edge-case bugs - One clear place where business rules live This is the kind of practical, production-ready pattern covered throughout Practical Pydantic, not theory, but real solutions to real data problems. If you work with APIs, data pipelines, or FastAPI apps, this book is a solid investment in code reliability. 📘 https://lnkd.in/eGiB7ZxU Because trusting input blindly is not a strategy, validation is.

If you're defining data models in Python, Go, TypeScript, and Java — you don't have one model. You have four. Four that will drift apart, silently, until something breaks in production on a Friday evening. I wrote a deep dive comparing Protobuf, Avro, and JSON Schema — the three dominant schema languages for solving this problem. Covers when to use each, real-world war stories, why Pydantic/Zod aren't enough on their own, and a speculative design for the ideal schema language that doesn't exist yet. Link in the comments! #DataEngineering #SoftwareArchitecture #SchemaDesign #Protobuf #Avro #JSONSchema

From data science and automation to Android and JVM development, Python and Kotlin serve different needs. This guide compares them so you can choose with confidence. https://lnkd.in/ghkqU3KK

build a legal tech data pipeline yesterday — no fancy orchestration tools, just pure Python! It takes court judgments (PDFs), extracts key info using a text-to-text model from Nebius (Qwen-30B-thinking, Qwen-80B-thinking ), outputs clean structured JSON, and merges back into the original PDF. first time doing something like this, so it was a long game Now the next level: I want to learn Apache Airflow properly instead of raw scripts. Any tips for beginners on: Migrating a chain of Python scripts → one clean DAG Handling file paths reliably (absolute vs relative, best practices)? If you've done this migration before, drop your go-to resources or one-liner advice — would mean a lot! #LegalTech #Python #Airflow #DataPipelines #AIinDev

Working with data and Python with VSCode? You should check the Data Wrangler extension 🚀 The Data Wrangler extension from Microsoft provides a native data viewer for VSCode and VSCode’s Jupyter notebooks. It enables viewing, filtering, cleaning, and analyzing data and provides column statistics insights and visualization. When applying filters and transformations on the UI, it can generate the Pandas 🐼 code. 📌 Extension documentation: https://lnkd.in/g7DdZqVE #data #python #vscode

🧠 Python Feature That Makes Multiple Dicts Feel Like One: collections.ChainMap 💻 No merging. 💻 No copying. Just smart lookup 👌 ❌ Common Way config = {} config.update(defaults) config.update(env) config.update(user) Messy and order-dependent 😬 ✅ Pythonic Way from collections import ChainMap config = ChainMap(user, env, defaults) Python searches left to right automatically ✨ 🧒 Simple Explanation Imagine checking for a toy 🧸 1️⃣ Check your bag 2️⃣ Check your cupboard 3️⃣ Check the store 💫 Stop as soon as you find it. 💫 That’s ChainMap. 💡 Why This Is Powerful ✔ No data copying ✔ Clean configuration handling ✔ Used in settings & overrides ✔ Interview-friendly concept ⚡ Real Use Case value = config["timeout"] # user → env → defaults 💻 Python doesn’t force you to merge data. 💻 It lets you layer it intelligently 💻 ChainMap is one of those tools you appreciate later. #Python #PythonTips #PythonTricks #AdvancedPython #CleanCode #LearnPython #Programming #DeveloperLife #DailyCoding #100DaysOfCode

I’ve been refining my Python fundamentals through the "30 Days of Python" challenge, and Day 6 focuses on a critical structure: Tuples. While many see Tuples as just "immutable lists," from a backend engineering perspective, they represent a design choice for data integrity. When building complex systems—like my project Alfie—using the right data structure is the first line of defense against bugs. Key takeaways from today’s implementation: Immutability as a Constraint: By using Tuples for fixed data sets (like API endpoints or geographic coordinates), I ensure that the data remains constant throughout the execution flow. Performance: Tuples are more memory-efficient than lists. In high-frequency data processing, these small optimizations scale. Unpacking & Manipulation: Leveraging tuple unpacking to streamline function returns and improve code readability. Consistency in the fundamentals is what allows for complexity in the architecture. Moving on to Sets and Dictionaries next. #Python #BackendEngineering #SoftwareDevelopment #AlfieAI #CodingChallenge #CleanCode