Day 21/30 SQL, Python, ETL, Data Modelling Challenge FREE Solutions 🚀
March 24th, 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
Want runnable code + deep dive breakdowns? Upgrade to the Annual Plan and supercharge your prep.
📌 SQL Challenge – GROUPING SETS, ROLLUP, and CUBE
❓ Which SQL clause allows custom combinations of GROUP BY columns for reporting purposes?
🔘 A) GROUP BY ROLLUP
🔘 B) GROUP BY CUBE
🔘 C) GROUPING SETS
🔘 D) All of the above
✅ Answer: D - All of the above
Explanation:
All options extend GROUP BY with richer aggregations:
ROLLUP: Hierarchical totalsCUBE: All possible combinationsGROUPING SETS: Explicit control of multiple groupings
Best Practices:
✔️ Use GROUPING SETS for custom dashboards
✔️ Use ROLLUP for drill-down summaries
✔️ Analyze aggregation plans using EXPLAIN
🐍 Python Challenge – Running Totals with Itertools
❓ Which itertools function returns the running totals of values in an iterable?
🔘 A) chain()
🔘 B) accumulate()
🔘 C) groupby()
🔘 D) permutations()
✅ Answer: B - accumulate()
Explanation:accumulate() provides cumulative sums without manual loops.
E.g., accumulate([1, 2, 3]) → [1, 3, 6].
Best Practices:
✔️ Use for streaming calculations
✔️ Combine with operator.add or custom functions
✔️ Avoid unnecessary stateful loops
⚡ ETL Challenge – Change Data Capture (CDC)
❓ Which of the following is a widely used method for real-time CDC?
🔘 A) Full table scans
🔘 B) Hash comparison
🔘 C) Log-based CDC
🔘 D) Duplicate audit tables
✅ Answer: C - Log-based CDC
Explanation:
Log-based CDC reads transaction logs instead of querying full tables, enabling near real-time ETL pipelines.
Best Practices:
✔️ Use Debezium or Fivetran for log-based CDC
✔️ Avoid table scans for high-velocity systems
✔️ Maintain low-latency ingestion
🧱 Data Modeling Challenge – Surrogate Keys vs Natural Keys
❓ Why are surrogate keys preferred in dimensional models?
🔘 A) Improve readability
🔘 B) Avoid update issues
🔘 C) Enforce constraints
🔘 D) Reduce joins
✅ Answer: B - Avoid update issues
Explanation:
Surrogate keys don’t rely on changing business data (like email/SSN), ensuring stability and referential integrity.
Best Practices:
✔️ Use auto-incremented surrogate keys
✔️ Avoid natural keys that might change
✔️ Ensure consistency in joins across fact/dim tables
🚀 Ready to Level Up?
You're almost at the finish line! Upgrade for full access to:
✅ Deep Dives + Live SQL/Python
✅ Real-world DE interview prep
✅ Exclusive hands-on project guides
👉 Join now : zero2dataengineer.substack.com
💬 Drop your answers in the comments – top responses get a shoutout! 🔥