• Definition: Data is a collection of raw facts and figures that have not been processed or given meaning. It includes values like numbers, text, symbols, or images.
• Nature: Unorganized, without context, and by itself doesn’t convey a clear message.
• Examples: A list of temperatures, names, test scores, etc.
• Storage: Typically stored digitally in bits and bytes.
• Purpose: Once processed, it turns into information that can be used for decision-making.

• Quantitative Data:
  • Numerical in nature.
  • Examples: Age, height, weight, salary.
• Qualitative Data:
  • Descriptive, not numerical.
  • Examples: Name, gender, color, type.

• Definition: Information is processed, organized, and meaningful data that provides context.
• Purpose: Helps in decision-making by providing insights.
• Process: Comes from analyzing and interpreting raw data.
• Example: Knowing there are 100 senior citizens or the average salary is $50,000 are informational insights derived from raw data.

• Data: Unprocessed, raw facts that need interpretation.
• Information: Data that has been given context and meaning.
• Dependency: Information depends on data, but data does not depend on information.
• Use: Decisions are made based on information, not raw data.
• Format: Data = numbers, symbols; Information = insights, summaries, reports.

• A database is an organized collection of data stored electronically.
• Allows for easy access, management, and updating of data.
• Examples: Customer records, student databases, inventory systems.

• DBMS: Stands for Database Management System.
• It is software that interacts with users, applications, and the database itself to capture and analyze data.
• Functions: Create, Read, Update, Delete (CRUD).
• Examples: MySQL, Oracle, SQL Server.

• To overcome file system limitations such as redundancy and isolation.
• Ensures consistency, integrity, and security of data.
• Allows multiple users to access data concurrently without conflicts.
• Enables backup, recovery, and centralized control.

• Disadvantages of File System:
  • Data Redundancy and Inconsistency
  • Data Isolation
  • Difficulty in Accessing Data
  • Integrity Problems
  • Atomicity Issues
  • Concurrent Access Issues
  • Poor Security
• Advantages of DBMS: Solves all above issues using structured mechanisms and access controls.

• Normalization: Process of organizing data to minimize redundancy and dependency.
• Involves dividing large tables into smaller tables and defining relationships.
• Common forms: 1NF, 2NF, 3NF, BCNF.

• A primary key uniquely identifies each record in a table.
• It cannot be NULL and must have unique values.
• Example: Employee_ID in an Employee table.

• Atomicity: All steps in a transaction complete successfully or none do.
• Consistency: Transactions bring database from one valid state to another.
• Isolation: Transactions occur independently without interference.
• Durability: Once committed, changes are permanent despite failures.

• A foreign key is a field (or collection of fields) in one table that refers to the primary key in another table.
• It maintains referential integrity between the two tables.

• DELETE: Removes rows one at a time, can use WHERE clause, logs each row deletion, slower, rollback possible.
• TRUNCATE: Removes all rows quickly by deallocating data pages, no WHERE clause, minimal logging, faster, rollback usually not possible.

• A data structure that improves query performance by allowing faster data retrieval.
• Types include B-tree, Hash indexes.
• Created on columns frequently searched or used in joins.

• A transaction is a sequence of operations performed as a single logical unit of work.
• Must be either fully completed (commit) or fully failed (rollback).
• Ensures database integrity.

• Reduces data redundancy and inconsistency.
• Provides data integrity and security.
• Supports concurrent access and transactions.
• Offers backup and recovery mechanisms.
• Data independence and easier data access.

• Relational Model: Represents data as tables (relations) consisting of rows and columns.
• Relation: A table with attributes (columns) and tuples (rows).
• Attributes: Columns representing properties of entities.
• Tuples: Rows representing individual records.
• Domain: Allowed set of values for an attribute.
• Primary Key: Unique attribute(s) that identifies each tuple.
• Foreign Key: Attribute in one table referring to primary key in another to maintain relationships.
• Integrity Constraints:
  • Entity Integrity: Primary key values must be unique and not null.
  • Referential Integrity: Foreign key must be null or match primary key values of referenced table.
• Relational Algebra: Operations like selection, projection, join, union to query relational data.

• Transaction: A sequence of operations performed as a single logical unit of work.
• ACID Properties:
  • Atomicity: All operations of a transaction are completed or none are.
  • Consistency: Database remains in a consistent state before and after transaction.
  • Isolation: Concurrent transactions do not interfere with each other.
  • Durability: Once committed, changes are permanent even if system fails.
• Commit: Makes all changes made by the transaction permanent.
• Rollback: Undoes all changes made by the transaction in case of failure.
• Concurrency Control: Techniques like locking to ensure correct transaction execution in multi-user environment.
• Deadlock: Situation where transactions wait indefinitely for resources held by each other.

Yes, both ER Model and Relational Model represent real-world data logically. Since they share similar design principles, we can convert ER diagrams into relational tables. This process helps us move from high-level conceptual design to actual database schema.

We convert each element of the ER diagram into a corresponding table format in the relational model. This includes entities, attributes, and relationships. Let’s look at the rules for different components:

• A separate table is created using the entity name.
• All attributes become columns of the table.
• The entity’s primary key becomes the table’s primary key.
• Foreign keys are added if it relates to other tables.

• Create a table for the weak entity with all its attributes.
• Add the primary key of the corresponding strong entity as a foreign key.
• The table’s primary key will be a combination of the foreign key and the weak entity’s partial key.

• Single-valued attributes: Directly become columns in the table.
• Composite attributes: Each sub-attribute becomes its own column in the table. The composite attribute itself is not used.
• Example: Address {street-name, house-no} becomes columns like address-street-name and address-house-no.

• A new table is created with the name of the multivalued attribute.
• It includes a foreign key referencing the original entity’s primary key.
• The multivalued attribute becomes a column.
• The new table’s primary key is a combination of the foreign key and the multivalued attribute.
• Example: For Employee with multivalued attribute dependent-name:
• New table: dependent-name(emp-id, dname)
• Primary Key: {emp-id, dname}
• Foreign Key: emp-id

Derived attributes are not stored in tables. Since their values can be calculated using other attributes, they are excluded from the relational schema.

• Method 1: Create a table for the higher-level entity. Then create separate tables for each lower-level entity that includes:
  • Attributes of the lower-level entity
  • Primary key of the higher-level entity as a foreign key
• Method 2: Used when generalization is disjoint and complete.
  • No table for the higher-level entity is created.
  • Create one table per lower-level entity including all attributes (its own + inherited from higher-level).

• If generalization is overlapping, values like “balance” may be stored multiple times unnecessarily.
• If the generalization is not complete, some entities might not be represented at all.

• Create a new table to represent the aggregated relationship.
• Include primary keys of the participating entity sets and the aggregation.
• Also include any attributes of the relationship.