1. SQL Chip
The CASE statement is your "if-then" logic for SQL. It allows you to create new, descriptive categories on the fly without changing the underlying data. It evaluates conditions in order and returns a value as soon as a condition is met.
2. SQL Chunk
To master data summaries, you must distinguish between filtering rows and filtering groups. Use WHERE to filter individual records before they are grouped, and HAVING to filter the results of an aggregate function (like SUM or AVG) after the grouping is done.
SELECT
department,
job_title,
COUNT(*) AS staff_count,
AVG(salary) AS average_salary
FROM employees
WHERE status = 'Active'
GROUP BY department, job_title
HAVING AVG(salary) > 60000
ORDER BY average_salary DESC
LIMIT 10;3. SQL Challenge
Identifying the "top" record in a group is a classic interview question. While a simple GROUP BY gives you the highest salary, it doesn't easily give you the name of the person who earns it.
The Challenge: See if you can use a Subquery to link the maximum salary back to the specific employee name.
SELECT
name,
department,
salary
FROM employees
WHERE (department, salary) IN (
SELECT department, MAX(salary)
FROM employees
GROUP BY department
)
ORDER BY department;4. SQL Mistake
The "Missing Data" Trap: Using COUNT(column_name) when you actually want a total row count.
If you are analyzing a table where some employees haven't been assigned a commission_pct yet, COUNT(commission_pct) will result in a lower number than the actual headcount because it skips the NULL values.
-- MISTAKE: This might give you 80 if 20 people have no department assigned
SELECT COUNT(department) FROM employees;
-- BETTER: This ensures you count every single person in the building
SELECT COUNT(*) FROM employees; 5. SQL in Practice
In the E-commerce world, identifying "VIP" customers is vital for marketing. We can combine CASE and HAVING to find high-value users who have spent a significant amount across multiple orders.
SELECT
customer_id,
SUM(order_total) AS lifetime_value,
COUNT(order_id) AS order_frequency,
CASE
WHEN SUM(order_total) > 5000 THEN 'VIP'
WHEN SUM(order_total) BETWEEN 1000 AND 5000 THEN 'Loyal'
ELSE 'Standard'
END AS customer_segment
FROM orders
WHERE order_date > '2025-01-01'
GROUP BY customer_id
HAVING COUNT(order_id) >= 2
ORDER BY lifetime_value DESC;6. SQL Resource
This article provides a practical introduction to Databricks SQL Warehouse, explaining how it enables users with traditional SQL backgrounds to process data without needing to learn Python or Scala. It covers essential features like the SQL Editor, creating tables via Unity Catalog, and performing advanced data operations using commands like MERGE INTO, INSERT INTO, and READ_FILES.
