Unit 265: Comprehensive Database Systems

1. Introduction to Database Systems

• Definition and purpose of databases
• Types of databases
  • Relational databases
  • NoSQL databases (document, key-value, graph, column-family)
• Importance of efficient database management
• Overview of database management systems (DBMS)

2. Database Design Principles

• Entity-Relationship (ER) Modeling
  • Entities, attributes, and relationships
  • ER diagrams and notation
• Normalization
  • Normal forms (1NF, 2NF, 3NF, BCNF)
  • Benefits of normalization
• Denormalization
  • When and why to denormalize
  • Trade-offs involved
• Best practices for database design
  • Scalability considerations
  • Data integrity and consistency

3. Querying Databases

• Introduction to SQL
  • Basic syntax and commands
  • Data Definition Language (DDL) and Data Manipulation Language (DML)
• Writing queries
  • SELECT statements
  • Filtering data with WHERE clause
  • Sorting with ORDER BY
  • Aggregation functions (COUNT, SUM, AVG, etc.)
• Querying multiple tables
  • JOIN types (INNER, LEFT, RIGHT, FULL)
  • Subqueries and nested queries

4. Indexing and Optimization

• Concept of indexing in databases
• Types of indexes
  • B-Tree
  • Hash
  • Bitmap
• How indexes improve query performance
• Strategies for query optimization
  • Analyzing query execution plans
  • Avoiding common performance pitfalls
• Index maintenance and costs

5. Database Security and Backup

• Importance of database security
• User access control
  • Roles and permissions
• Encryption techniques
  • Data-at-rest and data-in-transit encryption
• Data masking and anonymization
• Backup strategies
  • Full, incremental, differential backups
  • Backup scheduling and retention
• Recovery procedures and disaster recovery planning

6. Data Migration and ETL Processes

• Data migration techniques
  • Source and target system considerations
  • Data mapping and transformation
• Extract, Transform, Load (ETL) processes
  • Extracting data from multiple sources
  • Transforming data (cleansing, aggregating, converting)
  • Loading data into target database/data warehouse
• Tools and frameworks for ETL

7. Data Warehousing and Data Mining

• Concept of data warehousing
  • OLAP vs OLTP
  • Data warehouse architecture
• Data mining techniques
  • Association rules
  • Classification
  • Clustering
• Use cases for data warehousing and mining
  • Business intelligence
  • Decision support systems

8. Cloud Databases and Big Data

• Cloud-based database systems
  • Benefits: scalability, cost-effectiveness, availability
  • Examples: Amazon RDS, Azure SQL Database, Google Cloud Spanner
• Big data challenges
  • Volume, velocity, variety
  • Managing unstructured and semi-structured data
• Technologies for big data management
  • Hadoop ecosystem
  • NoSQL solutions
• Opportunities and considerations in cloud and big data environments

9. Database Management Tools

• Overview of popular DBMS platforms
  • MySQL
  • Oracle Database
  • Microsoft SQL Server
  • MongoDB
• Features and capabilities
  • Administration tools
  • Query editors
  • Performance monitoring
• Use cases and selection criteria