{"id":39035,"date":"2024-01-29T10:30:00","date_gmt":"2024-01-29T05:00:00","guid":{"rendered":"https:\/\/www.guvi.in\/blog\/?p=39035"},"modified":"2026-10-27T03:27:12","modified_gmt":"2026-10-26T21:57:12","slug":"sql-queries-with-examples","status":"publish","type":"post","link":"https:\/\/www.guvi.in\/blog\/sql-queries-with-examples\/","title":{"rendered":"SQL Queries with Examples: Complete Guide from Basic to Advanced (2026)"},"content":{"rendered":"\n
SQL is one of the most valuable skills for anyone working with databases, data analytics, or software development. However, knowing SQL syntax is only the beginning. The real challenge is writing queries that retrieve accurate results, combine information from multiple tables, and solve practical business problems.<\/p>\n\n\n\n
A simple SELECT statement can retrieve employee records, whereas JOINs connect related datasets. GROUP BY helps summarise sales figures, and window functions support advanced calculations without removing individual rows.<\/p>\n\n\n\n
This guide covers the most commonly used SQL queries with examples, expected outputs, difficulty levels, and practical applications. You will also learn important SQL interview queries, common mistakes, and the differences between SQL and NoSQL.<\/p>\n\n\n\n
Quick Answer<\/h2>\n\n\n\n
SQL queries help you retrieve, filter, combine, summarise, and analyse structured data for reporting, applications, and technical interviews. <\/p>\n\n\n\n
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Basic Retrieval:<\/strong> SELECT<\/code>, WHERE<\/code>, and ORDER BY<\/code><\/li>\n\n\n\n
SQL was originally called SEQUEL (Structured English Query Language), built by IBM in the 1970s. Nearly 60% of developers worldwide still use it today, making it one of the most durable languages in tech.<\/div>\n\n\n\n
What Are SQL Queries?<\/h2>\n\n\n\n
SQL queries are commands used to retrieve, filter, combine, update, and analyse data stored in relational databases. Common SQL queries include SELECT<\/code>, WHERE<\/code>, JOIN<\/code>, GROUP BY<\/code>, subqueries, CTEs, and window functions, which help users solve everything from basic data retrieval tasks to advanced reporting and business analysis.<\/p>\n\n\n\n
Some of the Most Common SQL Queries<\/strong><\/h2>\n\n\n\n
<\/figure>\n\n\n\n
Query<\/th>
Function<\/th><\/tr><\/thead>
SELECT * FROM table_name;<\/td>
Retrieve all columns and rows from the specified table.<\/td><\/tr>
SELECT column1, column2 FROM table_name;<\/td>
Retrieve specific columns from the specified table.<\/td><\/tr>
SELECT DISTINCT column FROM table_name;<\/td>
Retrieve unique values from a specific column in the table.<\/td><\/tr>
WHERE condition;<\/td>
Filter rows based on a specified condition in the WHERE clause.<\/td><\/tr>
ORDER BY column ASC\/DESC;<\/td>
Sort the result set in ascending (ASC) or descending (DESC) order based on a specified column.<\/td><\/tr>
GROUP BY column;<\/td>
Group rows based on the values in a specific column.<\/td><\/tr>
HAVING condition;<\/td>
Filter groups based on a specified condition in the HAVING clause (used with GROUP BY).<\/td><\/tr>
INSERT INTO table_name (column1, column2) VALUES (value1, value2);<\/td>
Insert new records into a table with specified column values.<\/td><\/tr>
UPDATE table_name SET column1 = value1 WHERE condition;<\/td>
Update existing records in a table based on a specified condition.<\/td><\/tr>
DELETE FROM table_name WHERE condition;<\/td>
Delete rows from a table based on a specified condition.<\/td><\/tr>
3. Find employees with no matching department (LEFT JOIN + IS NULL, a classic Flipkart question):<\/strong><\/p>\n\n\n\n
sql<\/p>\n\n\n\n
SELECT e.first_name\nFROM employees e\nLEFT JOIN departments d ON e.department_id = d.department_id\nWHERE d.department_id IS NULL;<\/code><\/pre>\n\n\n\n
Interviewers are usually less interested in the exact syntax and more in whether you can explain your logic out loud. Practice narrating your thought process, not just typing the query.<\/p>\n\n\n\n
Advanced SQL Interview Queries With Examples<\/h2>\n\n\n\n
Advanced SQL interviews test your ability to solve business problems using multiple SQL concepts. Employers may ask you to calculate rankings, identify missing records, analyse customer behaviour, or compare performance across reporting periods.<\/p>\n\n\n\n
Here are five advanced SQL interview queries worth practising:<\/p>\n\n\n\n
1. Find the Top Three Highest-Paid Employees in Each Department<\/h3>\n\n\n\n
Difficulty: Advanced<\/p>\n\n\n\n
This query uses DENSE_RANK() to rank employees by salary within each department.<\/p>\n\n\n\n
WITH ranked_employees AS (\n SELECT\n first_name,\n department_id,\n salary,\n DENSE_RANK() OVER (\n PARTITION BY department_id\n ORDER BY salary DESC\n ) AS salary_rank\n FROM employees\n)\nSELECT *\nFROM ranked_employees\nWHERE salary_rank <= 3;<\/code><\/pre>\n\n\n\n
Output:<\/p>\n\n\n\n
first_name<\/th>
department_id<\/th>
salary<\/th>
salary_rank<\/th><\/tr>
Vikram<\/td>
101<\/td>
91000<\/td>
1<\/td><\/tr>
Rohan<\/td>
101<\/td>
82000<\/td>
2<\/td><\/tr>
Ananya<\/td>
101<\/td>
78000<\/td>
3<\/td><\/tr>
Priya<\/td>
102<\/td>
95000<\/td>
1<\/td><\/tr>
Neha<\/td>
102<\/td>
85000<\/td>
2<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
DENSE_RANK() assigns the same rank to employees with equal salaries. It does not skip ranking numbers after ties. Consequently, the query may return more than three employees per department.<\/p>\n\n\n\n
2. Calculate a Running Total of Monthly Sales<\/h3>\n\n\n\n
Difficulty: Advanced<\/p>\n\n\n\n
Running totals help analysts track cumulative revenue and business growth.<\/p>\n\n\n\n
SELECT\n sale_month,\n revenue,\n SUM(revenue) OVER (\n ORDER BY sale_month\n ROWS BETWEEN UNBOUNDED PRECEDING\n AND CURRENT ROW\n ) AS running_total\nFROM monthly_sales;<\/code><\/pre>\n\n\n\n
NOT EXISTS checks whether a matching order exists for each customer. This approach is useful for identifying inactive customers and investigating conversion problems.<\/p>\n\n\n\n
Month-over-month analysis measures how revenue changes between consecutive reporting periods.<\/p>\n\n\n\n
WITH revenue_comparison AS (\n SELECT\n sale_month,\n revenue,\n LAG(revenue) OVER (\n ORDER BY sale_month\n ) AS previous_revenue\n FROM monthly_sales\n)\nSELECT\n sale_month,\n revenue,\n ROUND(\n (revenue - previous_revenue) * 100.0\n \/ NULLIF(previous_revenue, 0),\n 2\n ) AS growth_percentage\nFROM revenue_comparison;<\/code><\/pre>\n\n\n\n
LAG() retrieves revenue from the preceding row. NULLIF() prevents division-by-zero errors when previous revenue is zero.<\/p>\n\n\n\n
The first month returns NULL because no earlier period exists.<\/p>\n\n\n\n
5. Find Products Generating More Revenue Than Their Category Average<\/h3>\n\n\n\n
Difficulty: Advanced<\/p>\n\n\n\n
This query compares individual product revenue against the average revenue within its category.<\/p>\n\n\n\n
WITH product_comparison AS (\n SELECT\n product_name,\n category,\n revenue,\n AVG(revenue) OVER (\n PARTITION BY category\n ) AS category_avg\n FROM products\n)\nSELECT\n product_name,\n category,\n revenue\nFROM product_comparison\nWHERE revenue > category_avg;<\/code><\/pre>\n\n\n\n
The window function calculates category-level averages while retaining individual product records. The outer query then identifies products performing above their category average.<\/p>\n\n\n\n
These examples use illustrative datasets. Actual outputs depend on the underlying records and database system.<\/p>\n\n\n\n
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