🔓 📢 Day 4/30 - CHALLENGE DEEP DIVE SOLUTIONS : SQL, PYTHON, ETL, DATA MODELLING
Solutions for Feb 27th, 2025 CHALLENGE – Unlock the Full Breakdown + Live Runnable Code!
🚀 Welcome to Day 4 – The Challenge Just Got Real!
You're here because you're serious about leveling up your Data Engineering skills—and trust me, today’s deep dive is going to change the way you think about SQL, Python, ETL, and Data Modeling.
❌ No more just memorizing answers.
✅ You’ll understand, analyze, and apply these concepts like a pro.
If you've ever struggled with:
🔹 Filtering aggregated results correctly in SQL
🔹 Understanding Python list comprehensions efficiently
🔹 Ensuring accurate data validation in ETL pipelines
🔹 Choosing the right schema for analytics performance
Then today’s breakdown is exactly what you need.
Unlock exclusive deep dives, real-world case studies, and hands-on runnable code—so you don’t just learn, but master SQL, Python, ETL, and Data Modeling.
🔥 Don’t Just Read—Upgrade & Experience It!
Every deep dive walks you through the concepts step by step—but more importantly, you get runnable code to test on onecompiler.com, so you truly grasp the concepts instead of just reading theory.
🚀 This isn’t just another tutorial—it’s your personal blueprint to mastering Data Engineering.
🚀 SQL Challenge - GROUP BY vs. HAVING (Deep Dive & Optimizations)
Understanding GROUP BY vs. HAVING
WHEREfilters raw data before aggregation.HAVINGfilters aggregated results afterGROUP BY.
🔹 Where it’s used in real-world applications?
✅ Sales & Revenue Reports: Filter products generating revenue above a threshold.
✅ Customer Analytics: Find high-spending customers after aggregating transactions.
✅ Fraud Detection: Identify unusual activity by filtering aggregated behavior.
Run & Test on onecompiler.com
1️⃣ Open onecompiler.com, select SQL (PostgreSQL or MySQL).
2️⃣ Copy and paste the SQL query:
