Business Analytics Course [2026] | Master Business Strategy & Data-Driven Decisions

Lesson 5.2.3 — Performance, Pitfalls, Exercise, Takeaways, CTA

⚡ Performance Tips & Best Practices

✔ Use selective WHERE filters before JOINs to reduce row volume.
✔ Prefer explicit columns instead of SELECT * for clarity and performance.
✔ Add indexes on join/filter keys for large tables (work with your DBA).
✔ Break complex queries into CTEs (WITH clauses) for readability and reuse.
✔ Test queries on a smaller sample before running on full production tables.

Note

The full SQL example is available externally (to avoid server security blocks). View SQL example on GitHub Gist.

⚠️ Common Pitfalls

Mixing aggregate and non-aggregated columns without grouping (SQL error).
Incorrect join keys leading to row multiplication (cartesian explosions).
Using HAVING where WHERE should be used (inefficient).
Not handling NULLs explicitly — they affect aggregations and joins.

🔍 Mini Exercise

Write a SQL query to find the top 5 customers by total purchases in the last 12 months, including customer name and segment. Use appropriate joins, aggregation, and ordering.

Identify the tables and keys
Filter the date range
GROUP BY customer and SUM purchases
ORDER and LIMIT to top 5

✅ Hints

Use a CTE for recent sales, LEFT JOIN customers, SUM(sales_amount), GROUP BY customer_id, ORDER BY total desc, LIMIT 5.

📌 Key Takeaways

SELECT chooses columns and calculations; use aliases for clarity.
JOINs combine tables — pick INNER/LEFT/RIGHT depending on required rows.
GROUP BY aggregates into buckets; HAVING filters those buckets.
Follow best practices: filter early, avoid SELECT *, and test on samples.

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