Check Personal Data in Leaked Databases with Python API

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

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

#Learnings File operations are really helpful especially for csv and json files. We perform various operations read, write in normal file manager too, but Python allows multiples diverse logics along with various data structures that make it an effective way to perform such operations. Here is a glimpse of a similar code shared below based on file operations: https://lnkd.in/gjFbX85v

🚀 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

🐍🔖 Python Database Tutorials — This section contains all of our tutorials that are related to working with databases in Python https://lnkd.in/gcDxzS6

Small Change, Big Performance Gain I recently tested a simple optimization in Python using ThreadPoolExecutor for an API-heavy workflow. First Case : 2 workers Total tasks: 52 ( external API calls) Time taken:  👉 436.489 seconds Second Case : 10 workers Total tasks: 52 ( external API calls) Time taken:  👉 90.856 seconds 💥 Simple takeaway If your workload involves: 1. API calls 2. database queries 3. external service calls then improving concurrency can* have a big impact.

Starting my journey into databases with Python 🐍 One of the first things I’m learning is how to connect Python to a database and begin interacting with data using SQL. To make this easier, I’m using SQLite a simple and lightweight database alongside SQLAlchemy, which helps Python communicate with different types of databases. Here’s what I’ve learned so far: Import create engine from SQLAlchemy Create a database engine by specifying the database type and name. Use the engine to connect and interact with the database. Explore the database by retrieving table names using engine.table_names() It’s a small step, but an important foundation for querying and analyzing data. Small steps, big growth 🚀 #Python #SQL #DataEngineering #LearningJourney #TechGrowth

📦 Python Basics: Variables Variables store data. Example: server_name = "prod-server-1" Real-world use: ✔ Store server names ✔ Store log data ✔ Store API responses Interview Tip: Explain use case, not just definition. #PythonBasics

If you're a Python developer and you're not using tools like ruff, black, pytest, mypy, py-spy and pre-commit, then you're probably behind the curve. All these tools are free to download and use in your projects, and WILL make your code better and less buggy. My latest article on Towards Data Science goes into more depth on all the above-mentioned tools and shows how to download and use each one. A link to the article is in the first comment.

If you want to refresh your Python fundamentals, I’ve put together a free repository. It’s a 1-week structured refresher designed for Data Engineers, covering the core Python concepts used in real-world workflows, from scripting and automation to databases and ETL pipelines. Each day builds on the previous one, moving from basics to practical data engineering patterns. If you find it useful, consider giving it a star so others can discover it too. Repository: https://lnkd.in/dXRpXABp #DataEngineer #Python #GitHub