Imagine this: You’re in an interview for a data analyst or database developer role. The interviewer asks, “Show me how you would find the top 5 customers by revenue.” You confidently write a query that returns the correct results instantly. That kind of command over SQL can make the difference between getting hired or not. In 2026, SQL remains a cornerstone skill for data-related roles, and mastering these queries will put you ahead of other candidates.

Exploring a career in Data Analytics? Apply Now!

SQL is the language of data. From extracting insights to analyzing complex datasets, knowing how to write clean, efficient, and optimized queries is crucial. Below are 25 essential SQL queries, explained in detail so you understand not just the code, but also the reasoning and real-world application.

1. Basic Queries

1. Select All Records

SELECT * FROM employees;

This query retrieves all columns and rows from the employees table. It’s often used to quickly inspect data or for exploratory analysis.

2. Select Specific Columns

SELECT first_name, last_name, email FROM employees;

Instead of fetching everything, this query focuses on specific columns. It reduces unnecessary data transfer and improves performance.

3. Filter Records with WHERE

SELECT * FROM employees WHERE department = 'Sales';

WHERE allows you to filter data based on conditions. This is essential for targeting relevant records.

4. Sorting Data with ORDER BY

SELECT * FROM employees ORDER BY salary DESC;

ORDER BY arranges data by a column, here sorting employees by salary from highest to lowest. Useful for ranking or prioritizing data.

2. Aggregate Queries

5. Count Records

SELECT COUNT(*) FROM employees;

Counts total rows in the table. Often used in dashboards and reports to get totals quickly.

6. Sum Column Values

SELECT SUM(salary) AS total_salary FROM employees;

Calculates the total salary. Aggregates like SUM are critical for financial analysis.

7. Average Value

SELECT AVG(salary) AS avg_salary FROM employees;

Finds the average salary. Useful for benchmarking and performance reviews.

8. Maximum and Minimum

SELECT MAX(salary) AS highest_salary, MIN(salary) AS lowest_salary FROM employees;

Identifies extremes in the data, helping spot top performers or underperforming areas.

3. Grouping and Filtering

9. Group By Department

SELECT department, COUNT(*) AS num_employees FROM employees GROUP BY department;

Groups employees by department and counts them. Essential for summary reports.

10. Having Clause

SELECT department, AVG(salary) AS avg_salary FROM employees GROUP BY department HAVING AVG(salary) > 50000;

Filters aggregated data. HAVING works like WHERE but for grouped results.

4. Joins

11. Inner Join

SELECT e.first_name, d.department_name FROM employees e INNER JOIN departments d ON e.department_id = d.department_id;

Fetches employees along with their department name. Inner join only returns matches in both tables.

12. Left Join

SELECT e.first_name, d.department_name FROM employees e LEFT JOIN departments d ON e.department_id = d.department_id;

Includes all employees, even those not assigned to a department. Left joins are useful for identifying missing relationships.

13. Right Join

SELECT e.first_name, d.department_name FROM employees e RIGHT JOIN departments d ON e.department_id = d.department_id;

Returns all departments, including those with no employees.

14. Full Outer Join

SELECT e.first_name, d.department_name FROM employees e FULL OUTER JOIN departments d ON e.department_id = d.department_id;

Combines all employees and departments, returning nulls where there’s no match. Useful for complete data comparison.

5. Advanced Queries

15. Subquery Example

SELECT first_name, salary FROM employees WHERE salary > (SELECT AVG(salary) FROM employees);

Finds employees earning above the average. Subqueries allow dynamic comparisons based on other query results.

16. Using IN

SELECT * FROM employees WHERE department_id IN (1, 2, 3);

Filters rows that belong to specific departments. IN simplifies multiple OR conditions.

17. Using BETWEEN

SELECT * FROM employees WHERE salary BETWEEN 40000 AND 80000;

Retrieves data in a numeric or date range. Practical for reporting salaries, dates, or ages.

18. Using LIKE

SELECT * FROM employees WHERE first_name LIKE 'A%';

Pattern matching for string searches. % represents any sequence of characters.

19. Using DISTINCT

SELECT DISTINCT department_id FROM employees;

Removes duplicates. Important when summarizing or reporting unique values.

20. Using CASE Statement

SELECT first_name, salary, CASE WHEN salary > 80000 THEN 'High' WHEN salary BETWEEN 50000 AND 80000 THEN 'Medium' ELSE 'Low' END AS salary_level FROM employees;

Categorizes rows based on conditions. CASE is key for dynamic reporting and analytics.

6. Window Functions

21. ROW_NUMBER

SELECT first_name, salary, ROW_NUMBER() OVER (ORDER BY salary DESC) AS row_num FROM employees;

Assigns sequential numbers to rows. Useful for ranking or pagination.

22. RANK

SELECT first_name, salary, RANK() OVER (ORDER BY salary DESC) AS rank_num FROM employees;

Ranks rows, giving the same rank to tied values.

23. SUM with OVER

SELECT department_id, salary, SUM(salary) OVER (PARTITION BY department_id) AS total_dept_salary FROM employees;

Cumulative totals by partition. Crucial for departmental or group analysis.

24. AVG with OVER

SELECT department_id, salary, AVG(salary) OVER (PARTITION BY department_id) AS avg_dept_salary FROM employees;

Calculates average within partitions. Window functions provide advanced analytics without collapsing rows.

25. LEAD/LAG

SELECT first_name, salary, LEAD(salary) OVER (ORDER BY salary) AS next_salary FROM employees;

Compares current row with next or previous. Helps in trend analysis or sequential comparisons.

Conclusion

Mastering these 25 SQL queries is a strong step toward acing interviews and excelling in data roles in 2026. Each query serves a specific purpose—from basic data retrieval to advanced analytics with window functions. Practicing these queries with real datasets will give you confidence and make you stand out to potential employers. Remember, SQL is not just about writing queries—it’s about solving business problems efficiently and accurately. By understanding the queries, their use cases, and how to optimize them, you’re positioning yourself as a data professional ready for any challenge.

Dreaming of a Data Analytics Career? Start with Data Analytics Certificate with Jobaaj Learnings.