Day 20/30 SQL, Python, ETL, Data Modelling Challenge FREE Solutions 🚀
March 21st, 2025 CHALLENGE – unlock solutions + reasoning
👋 Hey Data Engineers!
Difficulty Level: Intermediate
Today, we're building on yesterday’s fundamentals and pushing a bit further! Let’s test your SQL, Python, ETL transformations, and Data Modeling concepts.
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✅ Clear explanations & reasoning
✅ Why this solution works
✅ Key optimizations & best practices
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📌 SQL Challenge - EXISTS vs. IN
Challenge Recap:
❓ Which query is generally more efficient for checking if a record exists in another table?
🔘 A) SELECT * FROM orders WHERE customer_id IN (SELECT id FROM customers);
🔘 B) SELECT * FROM orders WHERE EXISTS (SELECT 1 FROM customers WHERE customers.id = orders.customer_id);
🔘 C) SELECT * FROM orders WHERE customer_id = ANY (SELECT id FROM customers);
🔘 D) SELECT * FROM orders WHERE customer_id NOT IN (SELECT id FROM customers);
Answer: ✅ Option B - EXISTS (SELECT 1 FROM customers …)
Explanation:
EXISTS is generally more efficient than IN for large datasets because it short-circuits as soon as a match is found, whereas IN may scan all records in the subquery.
Best Practices:
✔️ Use EXISTS when checking boolean existence conditions.
✔️ Use IN for small, predefined lists (e.g., WHERE id IN (1,2,3)).
✔️ Always analyze performance with EXPLAIN ANALYZE for query tuning.
🐍 Python Challenge - Optimizing Loops in Python
Challenge Recap:
❓ Which approach is the most memory-efficient for iterating over a large dataset?
🔘 A) for item in list(range(10**7)):
🔘 B) for item in (x for x in range(10**7)):
🔘 C) for item in {x for x in range(10**7)}
🔘 D) for item in list(range(10**7)).copy()
Answer: ✅ Option B - (x for x in range(107))**
Explanation:
A generator expression ((x for x in range(n))) doesn’t store all elements in memory, instead generating them on demand, significantly reducing memory consumption.
Best Practices:
✔️ Use generators for large dataset iteration to minimize memory usage.
✔️ Prefer yield functions over returning large lists.
✔️ Avoid unnecessary .copy() calls on large lists.
⚡ ETL Challenge - Data Deduplication in ETL
Challenge Recap:
❓ Which method is best for removing duplicates in an ETL pipeline?
🔘 A) Using DISTINCT in SQL queries
🔘 B) Implementing primary key constraints
🔘 C) Using Merge (UPSERT) strategies
🔘 D) Truncating the table and reloading all records
Answer: ✅ Option C - Using Merge (UPSERT) strategies
Explanation:
MERGE (also known as UPSERT) ensures existing records are updated, and new records are inserted, preventing duplicates without full table reloads.
Best Practices:
✔️ Use UPSERT (MERGE INTO) to efficiently handle duplicates.
✔️ Implement hashing or composite keys to track unique records.
✔️ Avoid full table truncation, as it increases processing time.
📌 Data Modeling Challenge - Data Partitioning Strategies
Challenge Recap:
❓ Which partitioning method is best for optimizing queries on a time-series dataset?
🔘 A) Hash Partitioning
🔘 B) List Partitioning
🔘 C) Range Partitioning (e.g., by date)
🔘 D) Random Partitioning
Answer: ✅ Option C - Range Partitioning (e.g., by date)
Explanation:
For time-series data, range partitioning based on date/time improves query performance by allowing fast pruning of old data and enabling efficient range queries.
Best Practices:
✔️ Partition by date/time for time-based datasets (e.g., logs, financial data).
✔️ Use partition pruning for faster retrieval of relevant data.
✔️ Implement data retention policies to archive or delete old partitions.
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💬 Drop your answers in the comments! The best responses will get a shoutout in the next edition! 🔥