Introduction to Database Administration (DBA)

• Role of a DBA:

  • Manage database environments to ensure data integrity, performance, and security.
  • Liaise between business requirements and technical implementations.

• Types of Databases:

  • Relational Databases (RDBMS): Organized in tables with rows and columns.
  • Non-Relational Databases (NoSQL): Flexible schemas designed for scalability, especially in big data applications.

• Industry Trends & Modern Practices:

  • Cloud Databases & DBaaS: Databases hosted in the cloud, providing scalability and reduced administrative overhead.
  • Containerization: Using technologies like Docker to package and deploy database services smoothly.
  • Career Paths: Insight into the ever-evolving career landscape for DBAs with current market demands.

Fundamentals of Relational Database Management Systems (RDBMS)

• Core Database Principles:

  • Organization of data into tables (rows and columns).
  • Keys: Primary keys for unique identification; foreign keys to establish relationships.

• Data Modeling & Normalization:

  • Process of structuring data to reduce redundancy and improve data integrity.

• Basic SQL Operations:

  • SELECT: Retrieve data.
  • INSERT: Add new records.
  • UPDATE: Modify existing data.
  • DELETE: Remove records.

• Architecture & Maintenance:

  • Understanding the internal architecture, data storage mechanisms, and the typical installation and configuration steps for systems like MySQL or PostgreSQL.

Introduction to Non-Relational (NoSQL) Databases

• When to Use NoSQL:

  • Designed for big data, scalability, and flexibility when a fixed schema isn’t necessary.

• Types of NoSQL Databases:

  • Document Stores (e.g., MongoDB): Store data as documents, often in JSON format.
  • Key‑Value Stores (e.g., Redis): Simple, fast storage – ideal for caching.
  • Column Family Databases (e.g., Cassandra): Optimized for large-scale data distribution.
  • Graph Databases (e.g., Neo4j): Designed to handle data with complex relationships.

• Data Modeling in NoSQL:

  • Emphasis on a schema‑free design that allows flexibility, with a focus on trade-offs between consistency and performance.

Data Modeling Techniques for Relational and NoSQL Systems

• Comparing Modeling Techniques:

  • Relational Schema Design:
    • Employs normalization to eliminate redundancy.
    • Denormalization may be used in some cases to improve read performance.
  • NoSQL Data Design:
    • Focuses on flexibility; often designed to optimize specific use cases or application needs.

• Choosing the Right Model:

  • Analysis of case studies to understand when to use a relational model versus a NoSQL approach.
  • Tools include Entity-Relationship (ER) diagrams for relational databases and JSON schema design for document stores.

Database Security, Backup, and Recovery

• Security Practices:

  • Authentication & Authorization: Ensuring that only authorized users can access the data.
  • Encryption: Protecting data at rest and in transit.
  • Auditing & Compliance: Monitoring and ensuring adherence to regulatory standards.

• Backup Strategies:

  • Types include full, differential, and incremental backups.

• Recovery Procedures:

  • Disaster recovery planning including high availability configurations and replication strategies for both RDBMS and NoSQL systems.

Performance Tuning and Monitoring

• Optimization Techniques:

  • For RDBMS: Query optimization, indexing strategies, and understanding execution plans.

• Monitoring Tools & Metrics:

  • Use of performance dashboards and explain plans to identify bottlenecks or inefficient queries.

• Scaling NoSQL Databases:

  • Techniques such as sharding (dividing data across multiple machines) and replication to maintain performance.

• Scheduled Maintenance:

  • Periodic tasks and automated scripts to maintain optimal database performance.

Cloud & DevOps Integration

• Cloud Database Services:

  • Overview of platforms like AWS RDS, Azure SQL Database, and MongoDB Atlas that offer managed database solutions.

• Containerization & Orchestration:

  • Docker: Container technology that packages databases for consistent deployment.
  • Kubernetes: Orchestration tool for managing containers in production.

• CI/CD Pipelines:

  • Integration of databases into continuous integration and continuous deployment processes for streamlined updates and database migration tasks.

• Infrastructure as Code (IaC):

  • Automating database environment setup using scripts and configuration management tools, ensuring repeatability and consistency.