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Basic DBMS Terminologies

Understanding DBMS requires familiarity with certain key terms. Below are the most important ones:

1. Database

  • An organized collection of related data stored in a structured format.
  • Example: A university database stores data about students, courses, teachers, and exams.

2. DBMS (Database Management System)

  • Software that manages databases.
  • Provides data storage, retrieval, manipulation, and security.
  • Examples: MySQL, Oracle, PostgreSQL.

3. Data

  • Raw facts and figures without context.
  • Example: 101, "Rahul", "BCA"

4. Information

  • Processed data that is meaningful.
  • Example: “Student with Roll No. 101 is Rahul, enrolled in BCA.”

5. Metadata

  • “Data about data.”
  • Describes structure: data type, constraints, relationships.
  • Example: Name (VARCHAR 50), RollNo (INT, Primary Key)

6. Database Schema

  • The overall logical design of the database.
  • Defines how data is organized into tables and relationships.
  • Types:
    • Logical schema – conceptual design.
    • Physical schema – how data is stored on disk.

7. Instance

  • Snapshot of data in the database at a particular time.
  • Example: Current list of enrolled students is an instance of the “Student” table.

8. Table (Relation)

  • A collection of rows and columns that stores data about one entity.
  • Example: Student (RollNo, Name, Course, Age)

9. Tuple (Row / Record)

  • A single record in a table.
  • Example: (101, Rahul, BCA, 20)

10. Attribute (Column / Field)

  • A property/characteristic of an entity.
  • Example: Name, RollNo, Course are attributes of Student.

11. Primary Key (PK)

  • A unique identifier for each record in a table.
  • Example: RollNo in the Student table.

12. Foreign Key (FK)

  • Attribute in one table that refers to Primary Key in another table.
  • Maintains referential integrity.
  • Example: CourseID in Student table referencing CourseID in Course table.

13. Candidate Key

  • All possible attributes that can uniquely identify records.
  • Example: In Employee table, EmpID and AadharNo can both be candidate keys.

14. Super Key

  • Any set of attributes that uniquely identifies records (may contain extra attributes).
  • Example: {RollNo, Name} is a super key if RollNo alone is unique.

15. Alternate Key

  • Candidate keys that are not chosen as the primary key.

16. Composite Key

  • A key formed by combining two or more attributes to uniquely identify a record.
  • Example: {CourseID, StudentID} in Enrollment table.

17. Null Value

  • Represents missing or unknown data.
  • Example: A student with no phone number → NULL in Phone column.

18. Constraints

Rules to maintain data integrity:

  • NOT NULL – field cannot be empty.
  • UNIQUE – value must be unique.
  • CHECK – condition must be satisfied.
  • DEFAULT – assigns default value if none is provided.

19. Data Independence

  • Ability to change schema at one level without affecting schema at next higher level.
  • Logical data independence: Change conceptual schema without affecting application.
  • Physical data independence: Change storage without affecting logical schema.

20. Transaction

  • A single logical unit of work that must be executed fully or not at all.
  • Follows ACID properties (Atomicity, Consistency, Isolation, Durability).

✅ Summary for Exams:

  • Database = Collection of related data.
  • DBMS = Software to manage database.
  • Schema vs Instance = Design vs Snapshot.
  • Keys = Uniqueness + Relationships.
  • Transaction = Ensures reliability of operations.

Here’s a diagram showing the relationship among Schema → Table → Attributes → Tuples.

This makes it clear how DBMS stores data:

  • Schema = overall design
  • Table = entity representation
  • Attributes = columns
  • Tuples = records