If you’re starting out with databases, hands-on practice is essential. This SQL experiments list is designed for students learning database management systems (DBMS) and relational database concepts. These practical tasks will help reinforce theoretical knowledge with real-world application.
Why Use an SQL Experiments List?
A structured SQL experiments list improves understanding of DDL, DML, aggregate functions, and advanced topics like cursors and triggers. It prepares students for exams, assignments, and future interviews.
SQL Experiments List for Practical Learning
1. Study DDL and DML Commands
Learn how to use CREATE
, ALTER
, and INSERT
commands to define and populate tables.
2. Create Tables and Insert Data
Build sample databases and fill them with mock data for later experiments.
3. Perform Queries with LIKE, BETWEEN, IN
Use these predicates to filter records effectively in your SQL queries.
4. Apply Aggregate Functions and Sorting
Use SUM()
, COUNT()
, ORDER BY
, and other functions to manipulate and display data.
5. Work with Single-Row Functions
Try functions like UPPER()
, ROUND()
, SYSDATE
to transform individual row values.
6. SQL Joins – Display Data from Multiple Tables
Practice INNER JOIN
, LEFT JOIN
to combine data from two or more tables.
7. Grouping Data Using Group Functions
Use GROUP BY
and HAVING
to group and filter aggregate results.
8. Solve Queries Using Subqueries
Understand how subqueries can simplify and optimize complex data retrieval.
9. Security and Privileges in SQL
Learn to manage access using GRANT
and REVOKE
.
10. Transaction Control Commands
Use COMMIT
, ROLLBACK
, and SAVEPOINT
to manage database transactions.
11. Cursor in SQL
Write and manage cursors for row-by-row processing.
12. Trigger Implementation
Create triggers that fire on events like insert, update, or delete.
Conclusion
This structured SQL experiments list is a valuable resource for database learners. By practicing these tasks, you’ll build a strong foundation in SQL and gain the confidence to work with real-world databases.