A Database Management System (DBMS) is the backbone of modern data handling. Whether you are a student, developer, or IT professional, understanding DBMS terms is essential. Below is a simple glossary of important DBMS concepts explained in easy words.
1. Database
A structured collection of related data. Example: A university database stores student details, courses, and results.
2. DBMS
Software that manages databases. It helps in storing, retrieving, and securing data efficiently. Examples: MySQL, Oracle, PostgreSQL.
3. RDBMS (Relational DBMS)
A type of DBMS based on the relational model. Data is stored in tables (rows and columns). Example: SQL Server.
4. Table (Relation)
A collection of related data in rows (tuples) and columns (attributes).
5. Tuple (Row/Record)
A single entry in a table. Example: one student’s details in the student table.
6. Attribute (Column/Field)
A property of data stored in a table. Example: Student Name, Roll Number.
7. Schema
The overall design or structure of a database, defining how data is organized.
8. Instance
The actual data stored in the database at a particular moment in time.
9. Primary Key
A unique identifier for each record in a table. Example: Roll Number in a student table.
10. Foreign Key
An attribute in one table that links to the primary key of another table, establishing relationships.
11. Candidate Key
Attributes that can uniquely identify a record. One of them is chosen as the primary key.
12. Composite Key
A primary key made of two or more attributes.
13. Super Key
A set of attributes that can uniquely identify a record in a table (may include extra attributes).
14. Normalization
The process of organizing data to avoid redundancy and improve efficiency.
15. Denormalization
The reverse process, where redundancy is introduced for faster query performance.
16. Entity
An object in the real world represented in the database. Example: Student, Teacher, Course.
17. Entity-Relationship (ER) Model
A diagram that shows entities, their attributes, and relationships.
18. Cardinality
Defines the relationship between entities (one-to-one, one-to-many, many-to-many).
19. SQL (Structured Query Language)
The language used to manage and query databases. Example: SELECT * FROM Students;.
20. DML (Data Manipulation Language)
SQL commands used to insert, update, delete, and retrieve data. Example: INSERT, UPDATE.
21. DDL (Data Definition Language)
SQL commands used to define or modify database schema. Example: CREATE, ALTER, DROP.
22. DCL (Data Control Language)
SQL commands used to control user permissions. Example: GRANT, REVOKE.
23. TCL (Transaction Control Language)
SQL commands to handle transactions. Example: COMMIT, ROLLBACK.
24. Transaction
A sequence of operations performed as a single unit. Example: Transferring money between accounts.
25. ACID Properties
Rules that ensure reliable transactions:
- Atomicity – All or nothing execution.
- Consistency – Data must remain valid.
- Isolation – Transactions don’t interfere.
- Durability – Data is permanent after commit.
26. Index
A data structure that speeds up searching in a table.
27. View
A virtual table created from a query on existing tables.
28. Trigger
A set of instructions that automatically executes in response to certain events (like insert or update).
29. Stored Procedure
A saved SQL program that can be executed multiple times.
30. Concurrency Control
Techniques to ensure multiple users can access the database without conflicts.
31. Deadlock
A situation where two or more transactions wait for each other and cannot proceed.
32. Locking
Restricting access to data to maintain consistency during transactions.
33. Data Redundancy
Storing the same data at multiple places, often avoided using normalization.
34. Data Integrity
Ensuring accuracy and consistency of data in the database.
35. Replication
Copying data from one database to another for backup or faster access.
36. Backup and Recovery
Techniques used to restore data in case of failure or loss.
37. Distributed Database
A database spread across multiple locations but appearing as a single system.
38. NoSQL Database
Non-relational databases used for unstructured or big data. Example: MongoDB, Cassandra.
39. OLAP (Online Analytical Processing)
Used for complex queries and data analysis in business intelligence.
40. OLTP (Online Transaction Processing)
Used for managing day-to-day transaction-oriented applications.
Final Thoughts
This glossary covers the essential DBMS terms every learner should know. From basic concepts like tables and keys to advanced topics like transactions, normalization, and OLAP, these definitions will help you build a strong foundation in databases.