Building End-to-End Data Pipeline with Python & SQL Server

New project unlocked🔓 I just finished building a 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗟𝗶𝗳𝗲𝘁𝗶𝗺𝗲 𝗩𝗮𝗹𝘂𝗲 (𝗖𝗟𝗩) 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 𝗦𝘆𝘀𝘁𝗲𝗺. The starting question: 𝘩𝘰𝘸 𝘮𝘶𝘤𝘩 𝘳𝘦𝘷𝘦𝘯𝘶𝘦 𝘸𝘪𝘭𝘭 𝘦𝘢𝘤𝘩 𝘤𝘶𝘴𝘵𝘰𝘮𝘦𝘳 𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘦 𝘰𝘷𝘦𝘳 𝘵𝘩𝘦𝘪𝘳 𝘭𝘪𝘧𝘦𝘵𝘪𝘮𝘦 𝘪𝘯 𝘰𝘶𝘳 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴? Using the PostgreSQL DVD Rental dataset, I built an end-to-end pipeline: - Designed an ETL pipeline that processes ~14,000 transactions from 9 tables into a customer-level OLAP star schema - Engineered RFM-based features (Recency, Frequency, Monetary) for CLV modeling - Trained and compared multiple ML models (Linear Regression, Random Forest, Gradient Boosting) using chronological split and TimeSeriesSplit to avoid data leakage - Deployed everything into an interactive Django web app with a prediction form and business recommendations - The final model (Gradient Boosting) achieved strong performance, with R² close to 0.99 and low prediction error. One insight that came out of the analysis: customers who rent frequently, even at lower spend per transaction, often generate more lifetime value than occasional high spenders. Frequency matters more than monetary average! One limitation is that the dataset is static (historical DVD rental data), so the model reflects past behavior patterns rather than real-time customer activity. Additionally, some features like recency and tenure showed very low importance, likely due to the limited time range of the dataset, but they were still kept to ensures the model remains interpretable, aligned with business logic, and more generalizable to real-world scenarios beyond this dataset. This project helped me understand how data engineering, machine learning, and business thinking come together in a real system, not just a model. 🖇️GitHub → https://lnkd.in/g4k7iQuy Would love any feedback or thoughts!🖖🏻 #DataAnalytics #MachineLearning #Django #Python #PostgreSQL #PortfolioProject

🚀 Built an End-to-End Data Pipeline using API, Python & SQL Server! Excited to share a hands-on project where I implemented a complete data pipeline across two systems 💻 🔹 Project Overview: ✔ Extracted data from PostgreSQL (Laptop 1) ✔ Exposed data via Django API (JSON format) ✔ Accessed API from another machine (Laptop 2) ✔ Converted JSON → CSV using Python (pandas) ✔ Dynamically created table (no manual schema!) ✔ Loaded data into SQL Server using pyodbc 🔹 Architecture: PostgreSQL → Django API → JSON → Python → CSV → SQL Server 🔹 Key Learnings: 💡 API as a bridge between systems   💡 Handling JSON data in real-world scenarios   💡 Automating schema creation   💡 Cross-machine data transfer   💡 Building end-to-end ETL pipelines  This project gave me practical exposure to how modern data pipelines work in real-world data engineering 🚀 Looking forward to building more scalable and production-ready pipelines! #DataEngineering #Python #SQLServer #FastAPI #Django #ETL #DataPipeline #APIs #LearningInPublic #100DaysOfCode

I recently worked on an ETL pipeline built around UK regional carbon intensity data. The pipeline extracts 24-hour regional data from the Carbon Intensity API, transforms the nested JSON response into a structured tabular format, aggregates the 30-minute interval readings into daily regional summaries, and loads the output into PostgreSQL for analysis. On the transformation side, the workflow flattens both the carbon intensity values and the generation mix data across fuel sources, then uses Pandas to produce daily region-level metrics. On the database side, the final output is stored in PostgreSQL tables designed for reporting, with date-based partitioning applied to the fact tables to support cleaner storage management and better scalability as the data grows. The result is a query-ready pipeline that turns raw API data into structured daily carbon intensity and generation mix data that can be used for downstream analysis and reporting. Tech used: Python, Pandas, PostgreSQL, SQLAlchemy, YAML #DataEngineering #ETL #Python #PostgreSQL #SQL #DataPipeline #DatabaseDesign #AnalyticsEngineering

I recently built a data pipeline that automatically tracks and visualizes real-time weather data. The project follows an ELT (Extract, Load, Transform) workflow to keep data moving quickly and accurately from the source to the final dashboard. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: • 𝗗𝗮𝘁𝗮 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻: A Python script pulls live weather data from an API every 5 minutes. • 𝗦𝘁𝗼𝗿𝗮𝗴𝗲: The raw data is immediately loaded into a PostgreSQL database. • 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗦𝗼𝗿𝘁𝗶𝗻𝗴: I use dbt to transform raw data into structured tables for analysis: • 𝘀𝘁𝗴_𝘄𝗲𝗮𝘁𝗵𝗲𝗿_𝗱𝗮𝘁𝗮: The staging table where raw API data is cleaned, validated, and prepared for further processing. • 𝘄𝗲𝗮𝘁𝗵𝗲𝗿_𝗿𝗲𝗽𝗼𝗿𝘁: A refined table designed for real-time monitoring with clear, analysis-ready weather insights. • 𝗱𝗮𝗶𝗹𝘆_𝗮𝘃𝗲𝗿𝗮𝗴𝗲: An aggregated table that summarizes daily weather metrics to track trends over time. • 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: Apache Airflow orchestrates the entire process. • 𝗟𝗶𝘃𝗲 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱: Apache Superset displays results with a 5-minute auto-refresh. • 𝗦𝗲𝘁𝘂𝗽: Fully containerized using Docker for easy deployment. 𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: • 𝗡𝗲𝗮𝗿-𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲: Data updates every 5 minutes. • 𝗥𝗲𝗹𝗶𝗮𝗯𝗹𝗲: Prevents duplicates and ensures high-quality data. • 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁: ELT enables scalable transformations inside the database. This project helped me build a complete, automated data system from scratch. #DataEngineering #ELT #Python #SQL #Airflow #Docker #DataPipeline #WeatherUpdate

🚀 Transform the Way You Work with SQL! If you deal with multiple SQL dialects, you know the pain… syntax differences, compatibility issues, and endless debugging 😩 Meet SQLGlot : a powerful Python library that makes SQL translation, parsing, and optimization effortless 🔥 💡 Why it stands out: ✨ Translates between 20+ SQL dialects (BigQuery, Snowflake, Spark, and more) ✨ Parses SQL into clean, structured syntax trees ✨ Optimizes queries automatically ✨ Lightweight, fast, and easy to integrate into your data workflows Whether you're building data pipelines, working across platforms, or just want cleaner SQL, SQLGlot is a game changer 💪 👉 Explore the GitHub repo: https://lnkd.in/e2YCntJe #DataEngineering #SQL #Python #Analytics #BigData #DataTools 

🚀 Day 10/10 — Optimization Series End-to-End Mini Data Pipeline 👉 Basics are done. 👉 Now we move from working code → optimized code. So far, you learned: SQL optimization Python best practices Configs & environments Now… 👉 Let’s connect everything into a real pipeline 🔹 What is an End-to-End Pipeline? 👉 A complete flow: Ingest → Transform → Store → Automate 🔹 Example Flow import requests import pandas as pd import json # Load config with open("config.json") as f: config = json.load(f) # Step 1: Ingest (API) data = requests.get(config["api_url"]).json() # Step 2: Transform df = pd.DataFrame(data) df = df.dropna() # Step 3: Store df.to_csv(config["output_path"], index=False) 🔹 Pipeline Architecture 👉 API → Python → Data Cleaning → Storage 🔹 Where Optimization Applies SQL → fast queries Python → clean structure Config → flexibility Env → security 🔹 Why This Matters Real-world data engineering Production-ready systems Scalable pipelines 🔹 Real-World Use 👉 ETL pipelines 👉 Data ingestion systems 👉 Analytics workflows 💡 Quick Summary Pipeline = everything working together 💡 Something to remember Individual skills are good… Connected systems are powerful. #SQL #Python #DataEngineering #LearningInPublic #TechLearning

I like using parquet files for storing and moving data around. It's great seeing lots of examples with it. Most data lake performance issues don't come from bad queries or slow engines. They come from how the data is physically laid out on disk. The article below walks through seven practical Parquet partition designs and when each one actually makes sense for real Python ETL workloads. It moves from simple date partitioning through multi-tenant layouts, hash bucketing, hot/cold splits, and versioned snapshots, each with clear folder structures, code samples, and honest trade-offs. It includes lots of good patterns matched to specific problems. Modexa's article is great for anyone building or maintaining data lake pipelines. If your team has ever battled tiny file storms or runaway scan times, this is a worthwhile read. https://lnkd.in/eeBWJcBd

A senior developer looked at my SQL query and said: ‘It works. But it will fail in production.’ I had written what I thought was a solid query. It passed every test. It returned the correct output. It even handled edge cases. Then came the review. “This will scan the entire table. On real data, it won’t finish.” That moment changed how I think about SQL. I had been optimizing for correctness. Production systems require optimization for scale and efficiency. What I learned the hard way: → A correlated subquery inside a WHERE clause can destroy performance → NOT IN behaves unpredictably with NULLs → Indexes become useless when columns are wrapped in functions → EXPLAIN PLAN is not optional—it's the starting point Most SQL problems are not logic problems. They are execution problems. That’s the gap between writing queries… and writing queries that survive production. If you’ve worked with large datasets, you’ve seen this happen. #SQL #DataEngineering #DataAnalytics #Learning #Python

An Implementation Guide to Building a DuckDB-Python Analytics Pipeline with SQL, DataFrames, Parquet, UDFs, and Performance Profiling - MarkTechPost https://lnkd.in/eJuvwc_a

From Database to Dashboard: Mastered Data Exporting! 📤📊 Day 72/100 Data is only useful if the right people can read it. For Day 72, I tackled Data Portability. While SQL is perfect for storage, sometimes you need to get that data into the hands of someone who doesn't speak code. I built a Python utility that queries a relational database and exports the entire result set into a professional CSV (Comma Separated Values) report. Technical Highlights: 📤 Automated Extraction: Using Python's csv module to bridge the gap between SQLite and Excel-friendly formats. 📋 Dynamic Metadata: Programmatically retrieving column headers using cursor.description to ensure the report is perfectly labeled. 💾 Streamlined Writing: Using writerows() for efficient, bulk-data transfer from memory to disk. 🛡️ Data Governance: Creating a 'Snapshot' system to backup records before performing destructive operations. The Professional Edge: As an engineer, building the database is only half the job. The other half is ensuring that the data is accessible, portable, and ready for analysis in tools like Excel or Tableau. Do check my GitHub repository here : https://lnkd.in/d9Yi9ZsC #SQL #DataAnalysis #100DaysOfCode #BTech #IILM #Python #SoftwareEngineering #DataEngineering #Excel #LearningInPublic #WomenInTech