Upon completion of this course, participants will be able to:

• Design and implement high-availability and disaster recovery solutions for databases.
• Perform advanced query optimization and performance tuning techniques.
• Develop and maintain robust backup, recovery, and data retention policies.
• Implement strong security controls, access management, and data encryption.
• Manage database resources, capacity, and scaling across different platforms.
• Compare and manage both relational (SQL) and non-relational (NoSQL) databases.
• Automate routine DBA tasks using scripting and configuration management tools.
• Ensure database compliance with organizational and regulatory data governance policies.

• Database Administrators (DBAs) and Junior DBAs
• IT Infrastructure and Operations Managers
• Data Architects and Data Engineers
• Software Developers working with complex data models
• Information Security and Compliance Specialists
• System Administrators managing database servers

• Hands-on workshops in writing and tuning inefficient SQL queries.
• Scenarios involving simulated database failure and recovery execution.
• Group activities to design a high-availability solution for a critical application.
• Individual exercises in configuring database security and user permissions.
• Discussions on NoSQL use cases and migration strategies.

Unit 1: Database Architecture and Fundamentals

• Review of relational database management systems (RDBMS) architecture.
• Introduction to NoSQL databases (key-value, document, graph) and use cases.
• Data modeling techniques for transactional and analytical workloads.
• Understanding data normalization and denormalization.

• Best practices for OS and hardware sizing for database servers.
• Optimizing instance configuration parameters for performance.
• Managing storage allocation and file system best practices.
• Implementing automated deployment scripts.

Unit 2: Performance Tuning and Query Optimization

• Reading and interpreting execution plans for query optimization.
• Techniques for rewriting inefficient SQL and index usage.
• Identifying and resolving blocking, deadlocks, and contention.
• Monitoring and profiling tools for real-time performance tracking.

• Advanced indexing strategies (clustered, non-clustered, column-store).
• Partitioning, sharding, and table compression techniques.
• Managing statistics and ensuring query optimizer accuracy.
• Maintenance strategies: re-indexing and consistency checks.

Unit 3: High Availability and Disaster Recovery (HA/DR)

• Designing for high availability using clustering and mirroring.
• Implementing replication and Always On availability groups.
• Automatic failover and manual switchover procedures.
• Monitoring HA/DR configuration health and performance.

• Developing a comprehensive backup strategy (full, differential, transaction logs).
• Defining Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO).
• Implementing offsite and cloud backup storage.
• Regularly testing restore procedures and data validation.

Unit 4: Database Security and Access Management

• Implementing encryption at rest (TDE) and in transit (SSL/TLS).
• Access control and managing permissions (least privilege principle).
• Auditing database activity for security and compliance.
• Vulnerability assessment and security patching strategies.

• Creating and managing users, roles, and schemas.
• Integrating database authentication with Active Directory or LDAP.
• Implementing Multi-Factor Authentication (MFA) for administrative access.
• Managing privileged access and super-user accounts.

Unit 5: Automation, Monitoring, and Governance

• Scheduling and managing database maintenance jobs (e.g., backups, integrity checks).
• Using scripting (Python, PowerShell) for configuration management.
• Automating deployment of schema changes and patches.
• Tools for centralized monitoring and management.

• The role of the DBA in data governance and quality.
• Ensuring compliance with data privacy regulations (e.g., GDPR, CCPA).
• Managing data retention and archival policies.
• Implementing data masking and pseudonymization for non-production environments.