ITF+ Databases Notes

1. What is a Database?

• Definition:
  • A database is an organized collection of data that can be accessed, managed, and updated efficiently.
• Purpose:
  • To store data in a structured format for easy retrieval and manipulation.
• Examples:
  • E-commerce websites storing product and customer data.
  • Banking systems managing account transactions.

2. Types of Databases

• Relational Databases:
  • Data is organized into tables with rows and columns.
  • Example: MySQL, PostgreSQL.
• Non-Relational (NoSQL) Databases:
  • Data is stored in flexible formats such as documents or key-value pairs.
  • Example: MongoDB, DynamoDB.
• Cloud-Based Databases:
  • Hosted on cloud platforms for scalability and accessibility.
  • Example: Google Cloud Firestore, Amazon RDS.
• Flat File Databases:
  • Simple text files storing data in plain text or CSV format.
  • Example: Config files or CSV files.

3. Key Components of Databases

• Tables:
  • Structure for organizing data into rows (records) and columns (fields).
• Records (Rows):
  • Individual entries in a table.
  • Example: A single customer’s details.
• Fields (Columns):
  • Specific attributes of data.
  • Example: Name, email, phone number.
• Keys:
  • Primary Key: Unique identifier for records (e.g., Customer ID).
  • Foreign Key: Links records between tables.

4. Database Management Systems (DBMS)

• Definition:
  • Software that enables users to interact with databases.
• Examples:
  • Relational DBMS: MySQL, PostgreSQL, Oracle Database.
  • NoSQL DBMS: MongoDB, Couchbase.
  • Cloud-Based DBMS: Amazon RDS, Google BigQuery.
• Features:
  • Data storage, querying, backup, and access control.

5. SQL vs. NoSQL

• SQL (Structured Query Language):
  • Used in relational databases.
  • Focuses on structured data with predefined schemas.
  • Example: MySQL, PostgreSQL.
• NoSQL:
  • Flexible schemas for unstructured data.
  • Suitable for big data and real-time applications.
  • Example: MongoDB, Cassandra.
• Key Differences:
  • SQL: Relational, structured, uses tables.
  • NoSQL: Non-relational, flexible, uses documents, key-value pairs, or graphs.

6. Data Storage and Retrieval

• Queries:
  • SQL Example: SELECT * FROM customers WHERE city='New York';.
  • NoSQL Example: Querying documents in MongoDB.
• Indexing:
  • Improves data retrieval speed by creating a structure for faster lookups.
• Data Relationships:
  • Linking tables in relational databases using primary and foreign keys.

7. Database Security

• Access Control:
  • Use roles and permissions to restrict access to sensitive data.
• Encryption:
  • Protects data at rest (on disk) and in transit (network).
• Backups:
  • Regularly backing up databases to prevent data loss.
• Authentication:
  • Enforcing strong user authentication (e.g., multi-factor authentication).

8. Questions for Review

1. What is the difference between relational and non-relational databases?
2. How are tables, records, and fields related in a database?
3. What are primary and foreign keys, and why are they important?