JSON Data Serialization for Cross-Language Communication

🎯 Tech Learning Journey - Day 09: SQLite Databases - Your Personal Data Vault! SQLite is like having a mini database built right into Python. It's a lightweight database that stores data in tables you can search, filter, and update - perfect for apps that need to remember information between runs. import sqlite3 # Create database and table conn = sqlite3.connect\('mydata.db'\) cursor = conn.cursor\(\) cursor.execute\('CREATE TABLE IF NOT EXISTS users \(id INTEGER PRIMARY KEY, name TEXT\)'\) # Insert and query data cursor.execute\("INSERT INTO users \(name\) VALUES \('Dhanush'\)"\) cursor.execute\("SELECT \* FROM users"\) print\(cursor.fetchall\(\)\) # \[\(1, 'Dhanush'\)\] conn.close\(\) Where I use this: Building apps that need persistent storage, logging user activity, and managing application data locally. #Python #Coding #Programming #Database #SQLite

I’m pleased to share my recent project: a Distributed Caching System in Python, designed to improve application performance by reducing database dependency and network latency. The system leverages core data structures (LRU using HashMap and Doubly Linked List) along with object-oriented design principles to deliver efficient in-memory data retrieval. It also includes multi-node distribution, TTL-based expiration, REST API endpoints, and a Python SDK for seamless integration. Additional features such as rate limiting, logging, and a SQL-backed demo were implemented to simulate real-world backend scenarios. Through benchmarking, the system demonstrates a clear improvement in response time, with repeated requests being served from cache instead of querying the database. https://lnkd.in/d2-EGs7v This project was a valuable exercise in backend system design, performance optimization, and building production-oriented solutions. #Python #BackendEngineering #SystemDesign #Caching #APIs #SoftwareDevelopment #DistributedSystems

I’ve been developing prototype Python Pandas codes to transform observational data housed on an OMOP schema into a SDTM schema containing the tables, DM, and MH, in particular. My in-house setup is with MySQL on a bare metal Ubuntu Server. I realize that there are already R solutions to do this and I personally worked on a SAS solution, but it’s my my belief Python may be the best programming approach. Also I don’t have SAS, thus my interest in developing a set of codes using free tools, but it may have greater potential, overall, and it’s definitely worth the development time.

🚀 I just published my first Python package on PyPI — sqliter-lochan! The problem: Every time you use Python's built-in sqlite3, you write the same boring boilerplate. Connect. Cursor. Execute. Commit. Close. Every. Single. Time. so I made sqlite a little lighter → sqliter 👀 ❌ The Old Way (standard sqlite3): ────────────────────────── conn = sqlite3.connect('app.db') cursor = conn.cursor() cursor.execute("INSERT INTO users (name) VALUES (?)", ("Elone Musk",)) conn.commit() conn.close() ✅ The sqliter Way: ────────────────────────── from sqliter import Database db = Database("app.db") db.query("INSERT INTO users (name) VALUES (?)", ("Elone Musk",)) That's it. Clean. Simple. Done. ⚡ What makes it different: → Zero external dependencies — pure Python, nothing to install extra → One db.query() method handles INSERT, SELECT, UPDATE, DELETE — everything → Results come back as dict-like rows — no more index gymnastics → Built-in error handling — database failures never go unnoticed 📦 Install it right now: pip install sqliter-lochan 🔗 GitHub (contributions welcome!): https://lnkd.in/gr5x35m7 #Python #OpenSource #PyPI #SQLite #Developer #PythonPackage

📘 Python Learning – Day 13 Highlights 🐍💾 Today’s class was about SQLite & Python Integration — working with real databases! 🔹 SQLite Basics: A lightweight, serverless database stored in a single file 🔹 Python Integration: Used sqlite3 to connect and manage databases 🔹 Core Operations (CRUD): ✔ Create table ✔ Insert data ✔ Read data (SELECT) ✔ Update & Delete records 🔹 Important Concepts: Using cursor, commit(), and closing connections properly 🔹 Bonus: Handled database errors using try-except 💡 Example: sqlite3.connect("students.db") → creates/connects database From simple coding to managing real data like a developer 🚀 #Python #SQLite #Database #Programming #LearningJourney #Beginner #TechSkills

🚀 Day 15 – Python Database Connectivity (PDBC) Today I learned how Python connects with databases like MySQL. 🔹 Used DB-API (PEP 249 standard) 🔹 Performed database operations using cursor 🔹 Learned how to insert and fetch data 💡 Key Learning: Python becomes truly powerful when it interacts with databases — this is where real-world applications begin. 📌 Example: cursor.execute("SELECT * FROM employee") Ajay Miryala 10000 Coders #Python #Database #BackendDevelopment #CodingJourney #100DaysOfCode

🎯 Tech Learning Journey - Day 03: Web APIs & Requests - Talk to the Internet! Web APIs are how your Python code talks to websites and online services. You send a request, just like asking for information, and the API sends back data that you can use in your application. import requests # Get data from a public API response = requests.get\('https://lnkd.in/gUpgfHqa) data = response.json\(\) print\(f"User: \{data\['login'\]\}"\) print\(f"Repos: \{data\['public\_repos'\]\}"\) Where I use this: Fetching weather updates, getting user data from databases, and integrating third-party services into my apps. #Python #Coding #Programming #WebAPI

Day 04 #StudyClub SQL, we learned and implemented indexing strategies. The purpose of this indexing is to reduce the time it takes for the database to process our queries, due to the large volume of data. With 72 million records, processing a single query without an index would take an extremely long time. Mentor: Hilmi ‎. Team: Monika Hermiani Yolanda Simamora Irpan Pilihan Rambe nadhira M. Aprida Sapitri Br Saragih Dyah Ayu Goldy Cahyastuti Medina Uli Alba Somala Siti Komala Hafsaninda MR Rintaldi Ghazian Hindami Reihan Nanda R. #SQLDay #MySQL #EntityRelationshipDiagram #ManifoldDatafolk #DatabaseDesignPrinciple #StudyClubDay4 #Python #ETLProcess #IndexingStrategies

Modernizing legacy doesn’t mean replacing it. It means making it work smarter. This white paper explores how Python transforms Sybase ASE operations, reducing manual effort, improving visibility, and enabling scalable, resilient database management. From repetitive tasks to intelligent workflows, the shift is real. Read the full white paper and rethink how you run ASE: https://lnkd.in/dszBkh34 Authors: Vertika Anand, Chetan Rao   #Sybase #Python #DatabaseAutomation #DevOps #DataEngineering #ITModernization

I published my first open-source library — uow-lib A minimalist Unit of Work implementation for Python 3.12+ that works with any async driver (asyncpg, aiosqlite) and doesn't force an ORM on you. What it does: – Tracks entity mutations automatically via instrumentation – Guarantees one in-memory object per identity (Identity Map) – Orders operations by dependency: INSERTs parents before children, DELETEs children before parents – Auto-registers child entities when added to a collection What you get: – Models are plain classes — domain stays clean from infrastructure – Full control over SQL in your DataMappers – Works with query builders, raw SQL, or any Connection that implements commit/rollback – Applicable to NoSQL if your store supports transactions – No need to manually track changes in your models First release, so rough edges are expected. If you try it and something breaks or feels off — open an issue. That's the fastest way to make it better. 📦 https://lnkd.in/eJbmXitV 🔗 https://lnkd.in/eV2n-JAx #python #opensource #unitofwork #architecture #asyncio