Database Management System (DBMS) is software designed to manage, store, retrieve, and organize data efficiently. It eliminates the complexities of handling data manually and provides a structured framework for storing and manipulating data securely.

Key Features of DBMS:

• Data Organization: It organizes data into tables, rows, and columns for easier access and management.
• Data Security: Offers robust authentication and encryption methods to ensure the safety of sensitive data.
• Data Integrity: Ensures that data remains accurate and consistent across the system.
• Concurrency Control: Allows multiple users to access data simultaneously without conflicts.
• Backup and Recovery: Ensures data is not lost due to system failures.
• Examples of DBMS: MySQL, PostgreSQL, Oracle DB, and SQLite.

• DBMS: Manages data as files. It lacks relationships between data sets, leading to redundancy and inefficiency.
• RDBMS: Stores data in a tabular format with rows and columns, making it easier to establish relationships using primary and foreign keys.

• RDBMS follows the ACID properties for transaction management, while DBMS may not.
• RDBMS systems like MySQL and PostgreSQL provide better scalability and performance for large datasets.

• Data Redundancy Control: Eliminates duplicate data entries.
• Data Consistency: Ensures uniform data across all systems.
• Indexing and Searching: Provides faster data retrieval using advanced indexing techniques.
• Concurrency Management: Multiple users can access the database simultaneously without conflicts.
• Data Integrity and Security: Enforces rules to maintain data accuracy and restrict unauthorized access.
• Backup and Recovery: Automates data recovery processes during system failures.

1. Atomicity: Transactions are all-or-nothing; if any part fails, the entire transaction is rolled back.
2. Consistency: Data remains valid and follows predefined rules before and after the transaction.
3. Isolation: Transactions occur independently without interference.
4. Durability: Data changes persist even after system crashes.

These properties guarantee data integrity and system reliability, which are crucial for database management.

• Primary Key values must be unique and cannot be NULL.
• Foreign Key values can be repeated and may include NULL values.

Example: In a school database, the "Student_ID" in the Students table is the Primary Key, while the same "Student_ID" in the Enrollments table acts as a Foreign Key.

• DDL (Data Definition Language): Defines database structure. Commands: CREATE, ALTER, DROP.
• DML (Data Manipulation Language): Manages data. Commands: INSERT, UPDATE, DELETE, SELECT.
• DCL (Data Control Language): Controls user access. Commands: GRANT, REVOKE.
• TCL (Transaction Control Language): Manages transactions. Commands: COMMIT, ROLLBACK, SAVEPOINT.

These languages ensure smooth operation and control of database systems.

• Zero (0): Represents a numerical value.
• Blank Space: Represents an empty string or character.

• NULL: The value is unknown.
• 0: The student hasn’t taken any courses.

• Primary Key Constraint: Ensures each record is unique.
• Foreign Key Constraint: Maintains relationships between tables.
• Check Constraint: Validates data values based on conditions.
• Unique Constraint: Restricts duplicate values in a column.

Importance:

• 1NF (First Normal Form): Ensures atomicity of data by removing repeating groups.
• 2NF (Second Normal Form): Eliminates partial dependencies.
• 3NF (Third Normal Form): Removes transitive dependencies.
• BCNF (Boyce-Codd Normal Form): Handles overlapping candidate keys.

• Hierarchical Model: Organizes data in a tree-like structure.
• Network Model: Represents data as interconnected nodes.
• Relational Model: Uses tables (rows and columns) to manage data.
• Object-Oriented Model: Combines object-oriented programming with databases.

• Physical Level: Describes how data is stored physically in the database (e.g., files, indexes).
• Logical Level: Defines what data is stored and the relationships among them (schemas).
• View Level: Provides a user-friendly representation of data, hiding complexities at lower levels.

• Schema: Static, defines the structure.
• Instance: Dynamic, represents data at a specific moment.

• Faster query execution.
• Efficient sorting and filtering.
• Reduced I/O operations.

1. Active: The transaction is being executed.
2. Partially Committed: All operations are executed, but changes are yet to be finalized.
3. Committed: Changes are permanently saved.
4. Failed: An error occurred, and the transaction cannot proceed.
5. Aborted: The transaction is rolled back, and changes are undone.

1. Inner Join: Returns records with matching values in both tables.
2. Left Join: Returns all records from the left table and matching records from the right table.
3. Right Join: Returns all records from the right table and matching records from the left table.
4. Full Join: Returns all records when there’s a match in either table.

• A clustered index determines the physical order of data in a table. Each table can have only one clustered index.
• A non-clustered index creates a separate structure to store pointers to the actual data in a table.

Differences:

• Clustered index rearranges table data; non-clustered doesn’t.
• Clustered index is faster for retrieval; non-clustered is better for lookups.

• Physical Data Independence: Changing physical storage without altering logical schema.
• Logical Data Independence: Modifying logical schema without affecting user views.

• Timeouts: Abort transactions after a specific time.
• Deadlock Detection and Recovery: Identify and resolve deadlocks dynamically.
• Resource Ordering: Follow a consistent order for resource allocation.

• DELETE: Removes specific rows based on a condition. It is slower and maintains a transaction log.
• TRUNCATE: Removes all rows from a table quickly and doesn’t log individual row deletions.

Key Difference: DELETE allows rollback; TRUNCATE doesn’t.

• Before Trigger: Executes before the triggering event.
• After Trigger: Executes after the triggering event.
• Instead Of Trigger: Replaces the triggering action.

• Horizontal Partitioning: Divides data rows into separate tables.
• Vertical Partitioning: Divides columns into different tables.

Partitioning improves database performance and scalability.

• Simplifies complex queries.
• Enhances security by restricting access to specific data.
• Provides data abstraction.

• OLTP (Online Transaction Processing): Manages real-time transaction systems.
• OLAP (Online Analytical Processing): Handles data analysis and decision-making systems.