Nevenka Lukic’s Post

a python api for checking if your personal data appears in leaked databases. you bring your own data source, it just gives you the query layer and the principle: you have a right to know. https://lnkd.in/eXq9XAgf

Just finished building an Automated Data Firewall in Python! The Problem: Manually checking incoming CSVs for errors is slow and prone to human error. The Solution: I built a system using Python and Watchdog that monitors folders in real-time. It runs parallel quality checks (null-value detection & outlier analysis) before promoting clean data to a MySQL warehouse via SQLAlchemy. Key Features: ✅ Real-time file monitoring ✅ Fail-safe reporting with ReportLab (PDFs) ✅ Concurrent processing with ThreadPoolExecutor 📂 Check out the repo here:https://lnkd.in/e8p7Aefq #Python #DataEngineering #Automation #Github #SoftwareDevelopment

🚀 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

Bruin Python SDK: query your database in one line. Most data teams have some internal Python helper for this, usually copied across projects and patched whenever something changes. Bruin Python SDK gives you that out of the box: 𝐝𝐟 = 𝐪𝐮𝐞𝐫𝐲("𝐒𝐄𝐋𝐄𝐂𝐓 * 𝐅𝐑𝐎𝐌 𝐮𝐬𝐞𝐫𝐬") Works across BigQuery, Snowflake, Postgres, MySQL, DuckDB and more. So you have fewer dependencies to manage and more time for actual analysis. Tutorial: https://lnkd.in/dqss6bry

🚀 Available for small Python tasks today I can help with: - Fixing Python errors - Simple automation scripts - Data cleaning (Excel / CSV) - Basic data analysis Fast delivery, simple solutions. If you need help, feel free to comment or DM me.

20+ years of software engineering in a nutshell: Data types will trick you. In Python, dictionary keys are a silent trap if you don't understand hashing. Look at this code: 1, 1.0, and True. To a human, they look like three different keys. To Python, they are mathematically equal and share the same hash value. Instead of creating three separate entries, Python simply overwrites the first key twice, leaving you with "c". 🤯 Senior developers catch this immediately; juniors spend hours debugging why their JSON or dictionary data is "missing." Stop playing checkers with your data architecture. ♟️ Don't just write code—understand the "game beneath the game." Insights by: Gaurav Savita #PythonDeveloper #CleanCode #SoftwareArchitecture #ProgrammingTips #CareerGrowth

DuckDB Python Integration Powers High-Performance Analytics with Zero-Copy Pipelines 📌 DuckDB’s Python integration unlocks zero-copy analytics, letting you query Pandas, Polars, and Arrow data directly-no serialization, no overhead. This breakthrough merges SQL power with Python speed, enabling massive datasets to be sliced, analyzed, and exported in real time. DevOps and data engineers now prototype and scale analytics in one seamless Python environment. 🔗 Read more: https://lnkd.in/dwpsxBBV #Duckdb #Python #Zerocopy #Sql #Vectorized

🚀 New Open-Source Project: Python + Kafka Integration I’m excited to share my latest project: 👉 https://lnkd.in/dv-SrNnm This repository is a practical guide to working with Apache Kafka using Python — focused on real-world usage and simplicity. 🔹 What you’ll find: • Producer & Consumer examples • Stream processing basics • Clean, easy-to-understand Python implementations • Ready-to-run examples for learning and testing Whether you're getting started with Kafka or building data-driven systems, this repo can help accelerate your workflow. Let’s build better real-time systems together. #Python #Kafka #DataEngineering #OpenSource #Backend #Streaming

20+ years of software engineering in a nutshell: Basic math will trick you. In Python, round(2.5) is 2, but round(3.5) is 4. 🤯 To a human, we are taught to always round .5 up. But if you do this in Python, you are playing checkers with your data structures. Python uses "Banker's Rounding" (rounding half to even) to prevent statistical bias when adding up massive arrays of numbers. Senior developers catch this immediately; juniors spend hours debugging why their financial dashboards are a few cents off. Stop playing checkers with your data architecture. ♟️ Don't just write code—understand the "game beneath the game." #PythonDeveloper, #DataScience, #SoftwareArchitecture, #ProgrammingTips, #CareerGrowth