Ever stuck with unstructured data in Excel sheets or spreadsheets and needed to push that messy data into a structured database? 🤯 Recently, I faced a similar challenge, a large spreadsheet filled with inconsistent, unstructured data that needed to be transformed into multiple clean tables. Doing it manually would’ve been time consuming and error prone. Here comes Python 🐍 Instead of struggling with manual cleanup, I built a small data pipeline using Python to automate the entire process from parsing and structuring the data to inserting it directly into a PostgreSQL Supabase database. What could’ve taken hours was reduced to minutes with better accuracy and scalability. As software engineers, knowing the right tool can turn a messy problem into an elegant solution. #Python #DataEngineering #Automation #PostgreSQL #Supabase #SoftwareEngineering

One Python expression, 22+ SQL dialects, zero rewrites 🐍 Running queries across multiple databases often means rewriting the same logic for each backend's SQL dialect. A query that works in DuckDB may require syntax changes for PostgreSQL, and another rewrite for BigQuery. Ibis removes that friction by compiling Python expressions into each backend's native SQL. Swap the connection, and the same code runs across 22+ databases. Key features: • Write once, run on DuckDB, PostgreSQL, BigQuery, Snowflake, and 18+ more • Lazy execution that builds and optimizes the query plan before sending it to the database • Intuitive chaining syntax similar to Polars 🚀 Article comparing Ibis with other libraries: https://bit.ly/3MnsHs7 #Python #DataScience #SQL

I was loading CSV files into SQL Server. It was slow. Then I switched to BULK INSERT. 💥 Everything changed. BULK INSERT is a native SQL method. It is built for speed. But the real power comes when you combine it with Python. 𝗪𝗵𝗮𝘁 𝗱𝗶𝗱 𝗜 𝗱𝗼? ✔️ Python to handle multiple CSV files ✔️ Python to clean and normalize data ✔️ BULK INSERT for fast loading into SQL Server This combination is simple. And very powerful. Python manages flexibility. SQL manages performance. 𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁: a faster ingestion process a cleaner pipeline a more reliable system 𝗗𝗮𝘁𝗮 𝗶𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻 𝗶𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗹𝗼𝗮𝗱𝗶𝗻𝗴. 𝗜𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝘀𝗽𝗲𝗲𝗱 𝗮𝗻𝗱 𝗰𝗼𝗻𝘁𝗿𝗼𝗹. Curious how it works? 🔗 GitHub repository: https://lnkd.in/dwjwP-bh P.S.  I know BULK sounds a lot like “HULK”… not very original, but I like it 😄

Working with data often means jumping between tools—but what if you could bring everything together seamlessly? Recently, I explored integrating SQL with Python, and it completely changed how I approach data analysis. Instead of manually exporting data, I connected directly to my SQL Server database using Python. With just a few lines of code, I was able to: 🔹 Establish a secure connection using pyodbc 🔹 Fetch data directly from SQL tables 🔹 Convert the data into a pandas DataFrame 🔹 Prepare it for further analysis and visualization Here’s a small part of the process I used: Defined connection parameters (server, database, driver) Created a connection string Connected to SQL Server using pyodbc Queried system tables to explore available data What I found most valuable is how this integration removes friction from the workflow. No more repetitive exports—just real-time access to structured data. This approach opens up powerful possibilities: ✔ Automating data pipelines ✔ Performing advanced analysis in Python ✔ Creating visualizations with libraries like matplotlib ✔ Building scalable data workflows For anyone working with data, combining SQL and Python is not just a technical skill—it’s a productivity booster. #DataAnalytics #Python #SQL #DataScience #Automation #LearningJourney

You won’t master SQL in a month ❌ You won’t master Python in a month ❌ You won’t master PySpark in a month ❌ But here’s what actually works 👇 --- 🔷 SQL 💻 → Solve 1 problem every day Resources → LeetCode, HackerRank, StrataScratch --- 🔷 Python 🐍 → Write scripts on weekends Resources → Codebasics, CodeWithHarry --- 🔷 PySpark ⚡ → Spend 30 mins daily understanding concepts Resources → Darshil Parmar, Deepak Goyal, Shubham Wadekar --- ▪️You don’t need to study like crazy ❌ ▪️You just need to improve a little every day 💡 --- Here’s the truth most people ignore 👇 ▪️1.00^365 = 1 ▪️1.01^365 = 37.7 🚀 --- Do nothing → you stay the same ❌ Improve 1% daily → massive growth 📈 --- 🔹Small steps 🔹Every day That’s your real advantage 🧠🔥 --- 🔸Save this 🔸Stay consistent 🔸Trust the process 🚀 --- #dataengineering #sql #python #pyspark #learningjourney #consistency #careergrowth

Building your first data pipeline with Python + SQL is easier than you think. You don’t need complex tools to get started. Just the right flow 👇 1️⃣ Start with the connection Use Python to connect to your database: → SQLAlchemy → pandas Define your source and target tables clearly 2️⃣ Extract & Transform in one flow → Write a clean SQL query to extract data → Load it into a pandas DataFrame → Apply transformations (cleaning, joins, calculations) 3️⃣ Load & schedule → Use df.to_sql() to load data back → Wrap everything in a single .py file → Schedule it using cron (or Airflow later) That’s it. You’ve built your first pipeline using Python + SQL. Start simple. Focus on understanding the flow. Tools can come later. But many people struggle at this stage. They focus too much on tools, ignore the fundamentals, and underestimate SQL. This often leads to random learning, no clear structure, no preparation strategy… And when you’re stuck in that loop, having the right mentor can make a huge difference. That’s why, if you want to go deeper into building real-world pipelines, I recommend checking out Bosscoder Academy’s Data Engineering program. They focus on fundamentals, projects, and system-level thinking. 🔗 Check their program here: bcalinks.com/39Hf27EV Every advanced pipeline starts with a simple one. #DataEngineering #Python #SQL

🚀 Day 14 – SQL Challenge “Find students who know C”… Simple? Not when your data looks like this: 👉 C, Python, C++ Now the real question: How do you match only ‘C’ and ignore C++ / C#? I explored multiple approaches 👇 🔹 1. LIKE-based logic (pattern matching) Works, but gets messy with multiple conditions. 🔹 2. Split-based approach using SUBSTRING_INDEX Simulates splitting and gives more control: 🔹 3. FIND_IN_SET (simple & effective) Sometimes the simplest solution wins: WHERE FIND_IN_SET('C', skills) 🔥 Key Insight: There’s no single “best” solution in SQL. It’s about choosing between readability, scalability, and simplicity based on the situation. Which approach would you pick? 👇 Thanks for the suggestion, Ratan Kumar jha! 🙌 Tried a split-based approach as well using SUBSTRING_INDEX to simulate splitting in MySQL. Really helped make the logic cleaner and more structured. #SQL #DataAnalytics #SQLChallenge #ProblemSolving #LearningInPublic

𝐃𝐚𝐲 12/30: 𝐅𝐢𝐥𝐥𝐢𝐧𝐠 𝐔𝐩 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐖𝐢𝐭𝐡 𝐑𝐞𝐚𝐥 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 🏢 𝘛𝘰𝘥𝘢𝘺 𝘐 𝘢𝘥𝘥𝘦𝘥 19 𝘤𝘰𝘮𝘱𝘢𝘯𝘪𝘦𝘴 𝘵𝘰 𝘮𝘺 𝘰𝘯𝘭𝘪𝘯𝘦 𝘥𝘢𝘵𝘢𝘣𝘢𝘴𝘦. 𝐖𝐡𝐚𝐭 𝐈 𝐝𝐢𝐝: Wrote a quick Python script that created company names, job listings, and passwords for each one. This gives me real-looking data to test with, instead of empty pages. 𝐓𝐡𝐞 𝐬𝐭𝐫𝐮𝐠𝐠𝐥𝐞: Took a few tries to get it right. My database structure is strict, so if anything was missing or in the wrong spot, it rejected the whole thing. 𝐋𝐞𝐬𝐬𝐨𝐧: Test data is a lifesaver. It shows you problems before real users do. But your data has to follow the rules exactly, or the database won’t accept it. #30DaysOfCode #BuildingInPublic #Database #Python #TechJourney #Typescript #react #supabase #fullstack

📊 Excel vs SQL vs Python (Pandas) — Which One Should You Use and When? One of the most common questions for anyone working with data: 👉 Excel? 👉 SQL? 👉 Python? The real answer: They each serve different purposes. 🔹 Excel — Ideal for quick analysis, small/medium datasets, and business users 🔹 SQL — Powerful for filtering, joining, and querying large databases 🔹 Python (Pandas) — Flexible for automation, data cleaning, and advanced analytics This visual compares how the same tasks are done across all three tools and clearly highlights the differences in approach. A great reference, especially for those starting a career in data. 💡 My approach: Small data & quick insights → Excel Databases & performance → SQL Automation & advanced analysis → Python Which one do you use the most? 👇 #DataAnalytics #Excel #SQL #Python #Pandas #DataScience #BusinessIntelligence #Analytics

Hey everyone, Today I would like to share with a programming project I have recently completed. This project takes a dataset of Q1-3 2025 job listings, provided by Workforce Opportunities & Residency Cayman in the Cayman Islands, and ingest it into a postgreSQL database to serve via a fastapi REST API. The API is then demoed in a jupyter notebook which requests data from the API via HTTP and then performs data analysis on it. The structure of the project follows the pyproject template and focuses on a clear separation of concerns and dependency injection through a decoupling of creation and usage of the various components. Logging is implemented throughout the program to provide clear visibility into runtime behavior, facilitating debugging and issue diagnosis. Database Layer Data normalized into a central fact table with dimension tables to reduce redundancy before being ingested into the database using a staging table with transactions/procedural statements to ensure data integrity before being committed. View created for serving fact table to abstract SQL logic from API layer, with indices to reduce join complexity and speed up query parameter filtered searches. API Layer Asynchronous API made using fastapi and psycopg to serve data from the postgres database. Asynchronous connection pool used during API lifespan to reduce startup costs associated with initiating new connections. The main route is the '/jobs' route which exposes the view created at the SQL layer. Query parameters can be passed on all routes for filtered searches. The routes have been integration tested using pytest and the fastapi TestClient to verify that data is correctly being served from the database and that errors are correctly caught. Data Analysis Layer Analysis initally performed directly in the database at the SQL layer using CTE's and Analytic functions. To demo how an end user could utilize the API, data analysis also performed in a jupyter notebook whereby data is fetched from API using requests for HTTP. Data is handled using pandas, modeled using scikit-learn, and visualized using matplotlib. *all sensitive data hashed using hashlib Please visit the github to view the code/documentation as well as the video demonstration I made below. If you have any feedback, or would like to chat to me directly in more detail regarding my project, please leave a comment or send me a message. Thanks! github: https://lnkd.in/ex8jnnur https://lnkd.in/e7D7ip-d