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Duration 21 hours
Course Outline
Introduction to PostgresAI
- Exploring PostgresAI architecture and key components
- Core concepts: cloning, snapshots, and sandbox environments
- Enterprise adoption scenarios and return on investment
PostgresAI Installation and Configuration
- Deploying PostgresAI in Docker and Kubernetes environments
- Connecting with PostgreSQL and external storage backends
- Setting up authentication and access management
Database Cloning and Experimentation
- Creating instant database clones using thin provisioning
- Safely testing schema changes within ephemeral environments
- Accelerating CI/CD processes with PostgresAI clones
Monitoring and Observability
- Leveraging PostgresAI dashboards for performance insights
- Monitoring clone health and query execution
- Integrating with Grafana, Prometheus, and ELK
AI-Driven Query Optimization
- Applying AI-based recommendations to enhance query performance
- Analyzing query plans and execution patterns
- Continuous optimization through feedback loops
Data Governance and Security
- Implementing data masking and anonymization strategies
- Maintaining compliance within cloned environments
- Configuring audit logging and role-based access controls
Integrating PostgresAI with Enterprise Workflows
- CI/CD integration using Jenkins, GitLab CI, or GitHub Actions
- Building automated testing pipelines for SQL and schema changes
- Best practices for team collaboration and environment sharing
Scaling PostgresAI Operations
- Managing large datasets and multi-node clusters
- Optimizing clone provisioning performance
- Capacity planning and cost management
Summary and Next Steps
Requirements
- A solid understanding of PostgreSQL database administration
- Practical experience with Linux server environments
- Knowledge of containerized or virtualized deployment workflows
Target Audience
- Database administrators
- DevOps and SRE engineers
- Data infrastructure architects
Testimonials (2)
Tuning strategies.
Jeffrey Zieg - Matrix Consulting
Course - PostgreSQL Performance Tuning
Logging behaviour when the instance is under stress, and the hierarchy/nomenclature of instances, databases, files, etc.