Django in_bulk() for Efficient Data Access

Django ORM does not need a full Django project to work. In a new article, I explore how to use Django in standalone mode as a lightweight data access layer for existing SQLite databases. Using a minimal setup and inspectdb, it’s possible to reverse engineer a schema and immediately start querying real data with QuerySets. This is the first step in a series on standalone ORM workflows and migration patterns. https://lnkd.in/dgbq--5x #Django #Python #ORM #Database

Data visualization using pyecharts #machinelearning #dataacience #datavisualization #pythonlibrary #pyecharts The Pyecharts library uses Echarts to generate charts. Pyecharts library provides the interface between Python and Echarts. The Pyecharts work usually like we use visualization library in Python or R in our Jupyter Notebook. Pyecharts have flexible configuration options, so you can easily match the desired chart you want to make. Detailed documentation and samples to help developers get started the faster project https://lnkd.in/gEspFCyY

When I first saw our production database hitting 99% utilization, I thought we needed a bigger server. I was wrong. The real problem wasn’t infrastructure. It was one unoptimized query quietly running inside a loop. Development was fast. Production exposed everything. I just published a deep dive on Django Query Optimization — covering N+1 problems, select_related vs prefetch_related, aggregations, indexing strategies, and real-world mistakes that slow apps down. here is my article link https://lnkd.in/gViDwwVd If you’re building APIs in Django, this might save you from a painful production incident. Would love to know: Have you ever faced a performance issue that wasn’t obvious at first? #Django #BackendDevelopment #Python #DatabaseOptimization #WebPerformance

🎙️ TALK: Advanced ORM kung-fu for on-demand filtering, sorting, and summing 40M financial transactions by Mathias Wedeken Fetching items from the database, grouping them and performing calculations on related data sounds easy in Python, but at scale, it can quickly become a bottleneck. If you're a Django developer working with large and growing datasets, or if you're curious how your app can benefit from the performance of Postgres without writing raw SQL, this talk is for you. 🔗 Grab your ticket: https://lnkd.in/dXN83nct #djangocon #djangoconeu2026

Developer Rewrites Collectible Deal Finder with Flask and MySQL for Multi-Niche Scalability 📌 A developer transformed a clunky collectible deal finder into a scalable, modular system using Flask and MySQL-letting new niches like YuGiOh or One Piece be added in under an hour. No more duplicated code; now one script handles any niche, making expansion fast and maintenance seamless. This rewrite proves Python’s flexibility for real-time, niche-driven data apps. 🔗 Read more: https://lnkd.in/dZRTCUAS #Flask #Mysql #Nichescalability #Collectibledeals #Scriptmodular

If you've heard of fastapi and want to find out more about it. Or you already use one of the other common, rival Python frameworks like Django and wonder if fastapi could be a better fit for your workload, you should check out my recent Towards Data Science article. In the tutorial, I walk through: - Path operations (GET/POST/PUT/DELETE) - Pydantic models for request/response validation - Dependency Injection for clean, reusable logic (DRY) - Automatic Swagger + ReDoc documentation (and how to use it to test endpoints fast) - A practical CRUD To-Do API example you can extend into a real service Read it for free. The link is in the first comment.

A small backend change recently reminded me how much performance hides in the details. We had an API that worked fine in testing - but slowed down noticeably with real data. The issue wasn’t infrastructure. It was query behavior. Fix involved: • Replacing repeated ORM calls with select_related / prefetch_related • Moving filtering logic closer to the database • Adding proper indexing for frequently queried fields • Returning only required fields instead of full objects No new tools. No architecture rewrite. Just understanding how Django ORM, database queries, and response size interact at scale. Result: faster response time, lower DB load, and cleaner query logic. Backend work often looks invisible when done right - but small decisions in Python/Django can have huge impact in production. Always learning to think a little deeper than “it works”. #Python #Django #BackendDevelopment #SoftwareEngineering #API #DatabaseOptimization #TechCareers

One observability lesson that took me years to fully understand: Slow queries rarely appear suddenly. They usually start small. A feature is added. A queryset grows. A join becomes heavier. Indexes stop being enough. And one day a request that used to take 40ms suddenly takes 900ms. What changed how I build Django systems was starting to observe database behavior continuously, not only when something breaks. A few practices that helped a lot: Tracking query count per request Monitoring slow query logs Measuring endpoint latency over time Profiling ORM-heavy views Making database metrics visible to the team Django’s ORM is powerful, but it can hide the real cost of queries if you don’t measure them. Performance problems don’t usually come from a single bad line of code. They come from systems evolving without visibility. Observability is what lets you see those changes before users feel them. Hashtags #Django #Observability #BackendEngineering #DatabasePerformance #Python #SoftwareEngineering

🔍 Why do we use id-1 when accessing data using iloc in Pandas? While building a small Django REST API to read data from a CSV file, I came across an interesting concept in Pandas indexing. In Pandas, the iloc function is used to access rows based on integer index positions. Example: row = df.iloc[id-1] 📌 Why id-1? Because Pandas indexing starts from 0, but in APIs users usually think in terms of 1, 2, 3... Example dataset: IndexYearIndustry02024Agriculture12024Manufacturing22023Construction API request: /api/1 User expectation → First row But in Pandas: df.iloc[0] → First row df.iloc[1] → Second row So we convert the user input to the correct index: row = df.iloc[id-1] This small adjustment ensures the API returns the correct row from the CSV file. 💡 Key takeaway: User input often starts from 1, but programming indexes start from 0. #Python #Django #Pandas #RESTAPI #DataEngineering #BackendDevelopment

🚀 Database Decoded: FastAPI vs Django ORM vs Raw SQL — When to Use What? As developers, we often focus on building features fast… but the real performance game starts with how we query our databases. I recently wrote a deep dive comparing FastAPI + SQLAlchemy, Django ORM, and Raw SQL — not just from a theoretical perspective, but from real-world experience building high-performance APIs and scalable systems. 💡 In this blog, I break down: ✔️ Why Django ORM feels “magical” ✔️ How SQLAlchemy gives you granular control ✔️ When Raw SQL is the real hero (and when it’s a trap) ✔️ Optimization tricks to avoid N+1 queries and slow APIs If you're building modern Python backends or transitioning between Django and FastAPI, this guide might change how you design your database layer. 👇 Full blog link in comments — would love to hear how you optimize your queries in production! https://lnkd.in/dyKPG2tv #FastAPI #Django #Python #BackendDevelopment #SQLAlchemy #WebDevelopment #SoftwareEngineering