SQL examples.

Retrieve all columns from a products table.

Retrieve only name and price columns from products table.

Select columns with friendly alias names using AS keyword.

Retrieve unique department names using DISTINCT keyword.

Filter products with price greater than fifty dollars.

Select employees from the Sales department only.

Sort books by publication year in ascending order.

Display student scores from highest to lowest.

Filter high salary employees and sort by name alphabetically.

Generate sorted inventory report with aliases for low stock items.

Select employees with salary greater than 50000 using simple comparison operator.

Find products with price between ranges using AND to combine two conditions.

Select customers from multiple cities using OR to match any condition.

Exclude specific records using NOT to invert condition results.

Match multiple specific values using IN for cleaner query syntax.

Filter numeric ranges using BETWEEN for inclusive boundary checks.

Search text patterns using LIKE with wildcards for partial matches.

Identify missing data using IS NULL to find unset values.

Mix multiple operators with parentheses to control logical evaluation order.

Build comprehensive sales filter using AND OR NOT IN BETWEEN LIKE and NULL checks.

Use COUNT to find the total number of rows in a table.

Calculate the total sum of all salaries using SUM aggregate function.

Find average salary plus minimum and maximum values in one query.

Group employees by department and count how many in each department.

Calculate total salary per department using GROUP BY and SUM together.

Show count average and total salary for each department in one query.

Use HAVING clause to show only departments with average salary above threshold.

Group by two columns to analyze data across department and job title.

Filter grouped results using multiple conditions in HAVING clause with COUNT and SUM.

Mini project analyzing sales data with grouping filtering and multiple aggregate calculations together.

Join customers with their orders using INNER JOIN to show matching records only.

Use LEFT JOIN to display all customers including those without orders.

Use RIGHT JOIN to show all orders including orphaned ones without customers.

Combine all customers and orders showing unmatched records from both tables.

Generate all possible combinations of products and stores using CROSS JOIN.

Use SELF JOIN to match employees with their managers from same table.

Simplify join syntax with USING when column names match in both tables.

Join tables using multiple conditions in ON clause for precise matching.

Combine three tables to show customer orders with product details.

Comprehensive report combining multiple joins with self join and aggregation.

Select employee name and compare salary to company average using scalar subquery.

Find customers who placed orders using IN with subquery.

Find products that have never been ordered using NOT IN subquery.

Find departments that have at least one employee using EXISTS.

Find employees earning above their department average using correlated subquery.

Calculate department statistics by querying a derived table of aggregated data.

Compare each product price against minimum and maximum prices using scalar subqueries.

Find employees in departments located in cities with above average city populations.

Find customers with no orders in current year using NOT EXISTS.

Generate comprehensive sales report combining scalar correlated and derived table subqueries.

Create a table and insert one user record into it.

Insert three products in a single INSERT statement.

Change the price of one product using UPDATE with WHERE clause.

Update both name and age for a user in one statement.

Remove one specific product using DELETE with WHERE condition.

Remove all products below a certain price threshold.

Increase prices by ten percent for products above average price.

Delete orders that belong to inactive customers using a join.

Insert a new user and return the inserted row immediately.

Insert products update stock delete discontinued items and show final inventory.

Creates a basic table with two columns and displays its structure.

Creates a table with a primary key to ensure unique identification.

Ensures required fields cannot be left empty when inserting data.

Prevents duplicate values in a column across all rows.

Automatically assigns default values when no value is provided.

Validates data meets specific conditions before insertion.

Links two tables together ensuring referential integrity between them.

Modifies existing table structure by adding and renaming columns.

Demonstrates creating and permanently deleting tables from database.

Complete mini project with multiple tables constraints and relationships.

Create a basic index on a single column to speed up queries.

Demonstrate query performance before adding an index using EXPLAIN.

Create an index and show how EXPLAIN reflects the index usage.

Create a multi-column index to optimize queries filtering on multiple columns.

Show how a composite index is used for queries on leading columns.

Update table statistics using ANALYZE to improve query planning accuracy.

Use EXPLAIN ANALYZE to see actual query execution times and row counts.

Create an index only for rows matching a specific condition.

Compare table and index sizes to understand storage overhead.

Build a mini-project analyzing query performance with multiple indexes and statistics.

Create a basic index on a single column to speed up queries.

Demonstrate query performance before adding an index using EXPLAIN.

Create an index and show how EXPLAIN reflects the index usage.

Create a multi-column index to optimize queries filtering on multiple columns.

Show how a composite index is used for queries on leading columns.

Update table statistics using ANALYZE to improve query planning accuracy.

Use EXPLAIN ANALYZE to see actual query execution times and row counts.

Create an index only for rows matching a specific condition.

Compare table and index sizes to understand storage overhead.

Build a mini-project analyzing query performance with multiple indexes and statistics.

Assign sequential row numbers to employees ordered by salary.

Compare RANK and DENSE_RANK for tied scores in a competition.

Rank employees within each department by salary using PARTITION BY.

Show each month sales amount with the previous month value using LAG.

Display current temperature and next day temperature using LEAD function.

Calculate cumulative sales total using SUM window function without frame.

Compute three day moving average using ROWS BETWEEN frame clause.

Divide students into four quartiles based on test scores using NTILE.

Show rank dense rank and percentage of total for product sales.

Build analytics report with rank running totals moving averages and lag across regions.

Assign sequential row numbers to employees ordered by salary.

Compare RANK and DENSE_RANK for tied scores in a competition.

Rank employees within each department by salary using PARTITION BY.

Show each month sales amount with the previous month value using LAG.

Display current temperature and next day temperature using LEAD function.

Calculate cumulative sales total using SUM window function without frame.

Compute three day moving average using ROWS BETWEEN frame clause.

Divide students into four quartiles based on test scores using NTILE.

Show rank dense rank and percentage of total for product sales.

Build analytics report with rank running totals moving averages and lag across regions.

Calculate total sales and transaction count grouped by year and month.

Compute cumulative sum of sales amounts ordered by date using window functions.

Find the top three highest-priced products in each product category.

Retrieve the most recent status for each customer order using window functions.

Identify duplicate customer records based on email address and show counts.

Find customers who have never placed an order using LEFT JOIN technique.

Group customers by their first purchase month and count cohort sizes.

Find consecutive date ranges in attendance records identifying gaps and islands.

Build hierarchical employee reporting structure using recursive common table expression.

Combine monthly trends running totals top customers duplicate detection and missing orders analysis.

Calculate total sales and transaction count grouped by year and month.

Compute cumulative sum of sales amounts ordered by date using window functions.

Find the top three highest-priced products in each product category.

Retrieve the most recent status for each customer order using window functions.

Identify duplicate customer records based on email address and show counts.

Find customers who have never placed an order using LEFT JOIN technique.

Group customers by their first purchase month and count cohort sizes.

Find consecutive date ranges in attendance records identifying gaps and islands.

Build hierarchical employee reporting structure using recursive common table expression.

Combine monthly trends running totals top customers duplicate detection and missing orders analysis.