📊 SQL Grouping & Filtering – GROUP BY and HAVING Explained

🧲 Introduction – Why Learn SQL Grouping & Filtering?

When analyzing large datasets, it’s often essential to group similar records and apply filters on aggregated data. SQL provides two powerful clauses to do this:

• GROUP BY: Groups rows sharing a common value
• HAVING: Filters the results after aggregation

Mastering these clauses allows you to:

• 🧠 Aggregate metrics like COUNT, SUM, AVG
• 📂 Organize data into meaningful categories
• 🔎 Apply filters to grouped results (unlike WHERE)

🎯 In this tutorial, you’ll learn how to:

• Group data using GROUP BY
• Filter grouped data using HAVING
• Combine both for powerful reporting queries

📘 Topics Covered

👥 SQL GROUP BY – Categorize and Aggregate Data

The GROUP BY clause is used to group rows based on one or more columns. It’s commonly used with aggregate functions like SUM(), AVG(), COUNT(), etc.

✅ Syntax:

SELECT column1, AGGREGATE_FUNCTION(column2)
FROM table_name
GROUP BY column1;

📌 Example – Total Salary by Department:

SELECT department, SUM(salary) AS total_salary
FROM employees
GROUP BY department;

✅ This groups employees by department and calculates the total salary per group.

🔎 SQL HAVING – Filter Aggregated Results

The HAVING clause works like WHERE, but it applies after the GROUP BY has aggregated data.

✅ Syntax:

SELECT column1, AGGREGATE_FUNCTION(column2)
FROM table_name
GROUP BY column1
HAVING condition;

📌 Example – Departments with Total Salary > 100,000:

SELECT department, SUM(salary) AS total_salary
FROM employees
GROUP BY department
HAVING SUM(salary) > 100000;

✅ This filters out departments where the total salary is ≤ 100,000.

⚙️ GROUP BY vs HAVING vs WHERE – Key Differences

🧪 Example – GROUP BY + HAVING in Action

SELECT department, COUNT(*) AS employee_count
FROM employees
WHERE status = 'active'
GROUP BY department
HAVING COUNT(*) >= 5;

📎 This query:

• Filters out inactive employees
• Groups active employees by department
• Returns only departments with 5 or more active employees

📘 Best Practices for GROUP BY and HAVING

📌 Summary – Recap & Next Steps

SQL’s GROUP BY and HAVING clauses let you analyze, categorize, and filter data with precision. They are fundamental for data analysis, reporting, and summarizing datasets.

🔍 Key Takeaways:

• Use GROUP BY to organize data for aggregation
• Use HAVING to filter groups after aggregation
• Combine with aggregate functions for insights like totals, averages, and counts

⚙️ Real-World Relevance:
These clauses are widely used in analytics dashboards, financial reports, performance metrics, and customer segmentation queries.

➡️ Next Up: Explore 🧬 SQL Subqueries & Nested Queries to build more dynamic and layered logic.

❓ FAQs – SQL Grouping & Filtering

❓ Can I use HAVING without GROUP BY?
✅ Technically yes, but it’s only meaningful when you’re using aggregate functions. Otherwise, use WHERE.

❓ What’s the difference between WHERE and HAVING?
✅ WHERE filters before grouping; HAVING filters after aggregation.

❓ Can I group by multiple columns?
✅ Yes, just separate them with commas:

GROUP BY department, location;

❓ Can I use aliases in HAVING?
✅ Not in all databases. It’s safer to repeat the aggregate expression in HAVING.

❓ What aggregate functions can be used with GROUP BY?
✅ COUNT(), SUM(), AVG(), MIN(), MAX(), and even nested functions like ROUND(AVG(salary), 2).