Day 20/30 - DEEP DIVE SOLUTIONS: SQL, PYTHON, ETL, DATA MODELING

Solutions for March 21st, 2025 CHALLENGE – Unlock the Full Breakdown + Live Runnable Code!

Hello Data Engineers,

Today, we’re diving into SQL Subqueries Optimization, Python Iteration Efficiency, ETL Deduplication, and Time-Series Partitioning Strategies. If you've upgraded to Deep Dive, you're already ahead, mastering these concepts with real-world performance optimizations and runnable code.

If you haven’t upgraded yet, this is where we go beyond just knowing the answers—giving you expert breakdowns, query tuning techniques, and best practices used in production systems.

Upgrade now and stay ahead of the competition!

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📌 SQL Deep Dive: EXISTS vs. IN Optimization

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 WHERE customers.id = orders.customer_id);

Why This Happens:

• EXISTS short-circuits as soon as a match is found, making it more efficient for large datasets.

• IN evaluates the entire subquery result set, which can be slower if the subquery returns many values.

• NOT IN can cause performance issues when dealing with NULL values.

Where It's Used in Real-World Applications:

• Filtering active users in large customer databases

• Ensuring transaction records exist before processing payments

• Checking foreign key references efficiently in OLTP systems

Run & Test on OneCompiler:

1️⃣ Open OneCompiler and select PostgreSQL.
2️⃣ Create sample tables and test query performance:

CREATE TABLE customers (
    id SERIAL PRIMARY KEY,
    name VARCHAR(50)
);

CREATE TABLE orders (
    order_id SERIAL PRIMARY KEY,
    customer_id INT
);

INSERT INTO customers (name) VALUES ('Alice'), ('Bob'), ('Charlie');
INSERT INTO orders (customer_id) VALUES (1), (2), (3), (4); -- 4 does not exist in customers

-- Testing EXISTS vs IN performance
EXPLAIN ANALYZE  
SELECT * FROM orders WHERE EXISTS (SELECT 1 FROM customers WHERE customers.id = orders.customer_id);

EXPLAIN ANALYZE  
SELECT * FROM orders WHERE customer_id IN (SELECT id FROM customers);

3️⃣ Click Run and analyze the execution plan.

What’s Happening in the Output?

• The EXISTS query executes faster by stopping at the first match.

• The IN query processes the entire subquery result set before filtering.

Best Practices:

✔️ Use EXISTS for large datasets where an early match is expected.
✔️ Avoid NOT IN unless NULLs are properly handled.
✔️ Consider JOIN for improving performance when filtering smaller datasets.

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🐍 Python Deep Dive: Optimizing Large Dataset Iteration

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 - Using a Generator (x for x in range(10**7))

Why This Happens:

• Generators do not store data in memory, they generate values on demand, making them significantly more memory-efficient than lists or sets.

• Lists (list(range(10**7))) store all values in memory, consuming huge memory resources.

Where It's Used in Real-World Applications:

• Processing large log files without loading them entirely into memory

• Streaming big datasets efficiently in ML pipelines

• Handling batch processing in ETL without memory overhead

Run & Test on OneCompiler:

1️⃣ Open OneCompiler and select Python 3.
2️⃣ Run this optimized iteration test:

import sys

# Using a list (high memory usage)
list_data = list(range(10**6))  # Storing all values
print("List memory usage:", sys.getsizeof(list_data))

# Using a generator (low memory usage)
gen_data = (x for x in range(10**6))  # Generates values on demand
print("Generator memory usage:", sys.getsizeof(gen_data))  # Minimal memory used

3️⃣ Click Run and analyze the memory usage output.

Best Practices:

✔️ Use generators for large datasets to reduce memory usage.
✔️ Prefer yield functions over returning large lists.
✔️ Leverage Python iterators for streaming data efficiently.