Day 28/30 SQL, Python, ETL, Data Modelling Challenge FREE Solutions

April 2nd CHALLENGE – unlock solutions + reasoning

👋 Hey Data Engineers!

Welcome to Day 28 of the 30-Day Data Engineering Challenge.
Today’s challenge hits four high-impact concepts you’ll see often in real-world pipelines and interviews:

• SQL Window Functions (RANK)

• Python Dictionary Comprehensions

• Incremental Load Logic in ETL

• Surrogate Keys in Data Warehousing

🧠 Don’t just memorize—understand. Every challenge solution includes:
✅ Clear explanation & reasoning
✅ Why this solution works
✅ Key optimizations & best practices

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SQL Challenge – Ranking Within Groups

Challenge Recap:
❓ What does this SQL query return?

SELECT name, department, RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS dept_rank FROM employees;

✅ Answer: B - Assigns rank within each department by salary

Explanation:
The RANK() window function assigns a rank to each employee within their department, ordered by salary in descending order. Employees with equal salaries get the same rank, and the next rank is skipped accordingly.

Best Practices:

• Use RANK() for handling ties in rankings

• Combine PARTITION BY with ORDER BY for grouped logic

• Prefer DENSE_RANK() if you don’t want gaps in rank numbers

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Python Challenge – Dictionary Comprehensions

Challenge Recap:

squares = {x: x**2 for x in range(3)} print(squares)

✅ Answer: B - {0: 0, 1: 1, 2: 4}

Explanation:
This is a dictionary comprehension that creates key-value pairs where the key is x and the value is x**2. The result is a compact dictionary of squares from 0 to 2.

Best Practices:

• Use dictionary comprehensions for quick mapping

• Avoid nesting too many expressions—offload to functions

• Great for lookups, configs, and lightweight transformations

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ETL Challenge – Incremental Load Efficiency

Challenge Recap:
❓ What’s the primary benefit of incremental loads?

✅ Answer: B - Loads only changed/new data since last run

Explanation:
Incremental loading processes only new or modified records. It reduces resource usage, speeds up pipelines, and prevents duplication. You typically rely on a last_updated field or change flag.

Best Practices:

• Track last_updated or ingestion_time

• Store pipeline state between runs

• Avoid full loads unless schema has changed

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Data Modeling Challenge – Surrogate Keys

Challenge Recap:
❓ Why are surrogate keys used in data warehouses?

✅ Answer: C - They reduce complexity and changes

Explanation:
Surrogate keys are internal IDs that uniquely identify rows without relying on change-prone business values. They support versioning, history tracking, and cleaner joins in star schemas.

Best Practices:

• Use surrogate keys in all dimension tables

• Keep natural keys for reference, not as primary keys

• Use them to enable Slowly Changing Dimensions (SCD Type 2)

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