Day 22/30 SQL, Python, ETL, Data Modelling Challenge FREE Solutions 🚀
March 25th, 2025 CHALLENGE – unlock solutions + reasoning
👋 Hey Data Engineers!
Difficulty Level: Intermediate → Advanced
We’re officially 70% through the 30-Day Challenge! Let’s dig deeper into SQL aggregations, Python tricks, efficient ETL loads, and modeling techniques.
💡 Understand, Don’t Memorize: ✅ Real-world logic behind answers
✅ Optimization insights
✅ Interview-aligned learning
Today we’re working with subqueries, dictionary comprehensions, incremental ETL, and denormalization strategies. All 🔥 concepts that come up in real-world pipelines + interviews.
💡 Understand, Don’t Memorize:
✅ Concept clarity
✅ Interview-worthy tips
✅ Scalable thinking
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📌 SQL Challenge – Average Salary Filter
SELECT name FROM employees
WHERE salary > (SELECT AVG(salary)
FROM employees);❓ What will be the output?
🔘 A) Employees with the lowest salary
🔘 B) All employees
🔘 C) Employees earning above average salary
🔘 D) SQL Error
✅ Answer: C - Employees earning above average salary
Explanation:
The subquery (SELECT AVG(salary)) runs first. Then the main query filters employees whose salary exceeds that average.
Best Practices:
✔️ Use subqueries to calculate dynamic thresholds
✔️ Always alias when subqueries get complex
✔️ Use CTEs for better readability if reusable
🐍 Python Challenge – Dictionary Comprehensions
nums = [1, 2, 3, 4, 5] squares = {x: x*x for x in nums if x % 2 == 0} print(squares)❓ Output?
🔘 A) {1: 1, 2: 4, 3: 9, 4: 16, 5: 25}
🔘 B) {2: 4, 4: 16}
🔘 C) {1: 1, 3: 9, 5: 25}
🔘 D) Error
✅ Answer: B - {2: 4, 4: 16}
Explanation:
This comprehension includes only even numbers and creates a key-value pair x: x*x.
Best Practices:
✔️ Great for filtering + transforming in one step
✔️ Prefer dict comprehensions for clean logic
✔️ Avoid overcomplicating with nested conditions
⚡ ETL Challenge – Incremental Load Efficiency
❓ What is the biggest benefit of using incremental loading in ETL?
🔘 A) Reduces transformation logic
🔘 B) Minimizes data loss
🔘 C) Optimizes performance and reduces load time
🔘 D) Avoids schema changes
✅ Answer: C - Optimizes performance and reduces load time
Explanation:
Incremental loads process only new or updated records—saving time, resources, and cost in production pipelines.
Best Practices:
✔️ Use timestamps or last_modified columns
✔️ Add watermarks or checkpoints
✔️ Log every batch for traceability
🧱 Data Modeling Challenge – Denormalization in Warehouses
❓ In which scenario is denormalization most useful?
🔘 A) Reducing disk storage cost
🔘 B) Improving read performance for reporting
🔘 C) Ensuring data consistency across OLTP systems
🔘 D) Simplifying index management
✅ Answer: B - Improving read performance for reporting
Explanation:
Denormalization speeds up read-heavy workloads by reducing joins, which is ideal for BI tools and dashboards.
Best Practices:
✔️ Use for OLAP systems
✔️ Monitor redundancy to avoid inconsistencies
✔️ Document logic clearly for maintainability
🚀 Finish Strong – You’re Over 70% Done!
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