Unit 624: Comprehensive Database Systems

1. Introduction to Database Systems

• Overview and importance of database systems
• Components of database systems: DBMS, database, users, and applications
• Basic concepts:
  • Data models (hierarchical, network, relational, object-oriented)
  • Schemas and instances
  • Relationships (one-to-one, one-to-many, many-to-many)

2. Relational Database Management Systems (RDBMS)

• Principles of RDBMS
• Tables (relations), rows (tuples), and columns (attributes)
• Keys:
  • Primary keys
  • Foreign keys
  • Candidate and alternate keys
• Normalization:
  • Purpose and benefits
  • Normal forms (1NF, 2NF, 3NF, BCNF)
• Indexing:
  • Types of indexes (B-tree, hash)
  • Index advantages and trade-offs
• Introduction to SQL:
  • Data Definition Language (DDL)
  • Data Manipulation Language (DML)

3. Database Design and Modeling

• Database design process overview
• Entity-Relationship (ER) modeling:
  • Entities, attributes, relationships
  • ER diagrams and notation
• Mapping ER models to relational schemas
• Advanced modeling concepts (weak entities, generalization, aggregation)
• Normalization techniques and schema refinement

4. Data Querying and Manipulation

• Advanced SQL querying:
  • Complex SELECT statements
  • Subqueries and correlated subqueries
  • Joins:
    • INNER JOIN
    • LEFT, RIGHT, FULL OUTER JOIN
    • CROSS JOIN
  • Set operations (UNION, INTERSECT, EXCEPT)
• Aggregate functions (COUNT, SUM, AVG, MIN, MAX)
• Grouping data (GROUP BY, HAVING)
• Transactions and concurrency control basics

5. Database Administration and Security

• Database administration tasks:
  • Installation and configuration
  • Monitoring and performance tuning
• User access control and authentication
• Backup and recovery strategies:
  • Full, incremental, and differential backups
  • Recovery models
• Security measures:
  • Encryption
  • Auditing and compliance
  • SQL injection prevention

6. NoSQL Databases

• Introduction and rationale for NoSQL
• Types of NoSQL databases:
  • Document stores (e.g., MongoDB)
  • Key-value stores (e.g., Redis)
  • Column-family stores (e.g., Cassandra)
  • Graph databases (e.g., Neo4j)
• Differences between NoSQL and RDBMS
• Use cases and application scenarios

7. Data Warehousing and Business Intelligence

• Data warehousing concepts:
  • Data marts
  • Star and snowflake schemas
• ETL (Extract, Transform, Load) processes
• Online Analytical Processing (OLAP):
  • Multidimensional analysis
• Data mining techniques
• Business intelligence and decision support systems

8. Big Data and Distributed Databases

• Challenges of big data
• Distributed database systems:
  • Characteristics and architecture
  • Scalability and fault tolerance
• CAP theorem explained
• Technologies overview:
  • Hadoop ecosystem
  • Apache Spark
  • NoSQL databases in big data

9. Database Trends and Technologies

• Cloud databases and Database as a Service (DBaaS)
• In-memory databases and their advantages
• Blockchain databases:
  • Concepts and use cases
• Impact of artificial intelligence and machine learning on databases
• Future directions and emerging innovations