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Duration 21 hours
Course Outline
Introduction to PostgresAI
- Exploring the architecture and key components of PostgresAI.
- Core concepts: cloning, snapshots, and sandbox environments.
- Enterprise adoption scenarios and return on investment (ROI).
PostgresAI Installation and Configuration
- Deploying PostgresAI in Docker and Kubernetes environments.
- Integration with PostgreSQL and external storage backends.
- Setting up authentication and access management.
Database Cloning and Experimentation
- Generating instant database clones using thin provisioning.
- Safely testing schema changes within ephemeral environments.
- Accelerating CI/CD workflows with PostgresAI clones.
Monitoring and Observability
- Utilizing PostgresAI dashboards for performance insights.
- Tracking clone health and query execution metrics.
- Integrating with Grafana, Prometheus, and ELK.
AI-Driven Query Optimization
- Applying AI-based recommendations for query improvement.
- Analyzing query plans and execution patterns.
- Implementing continuous optimization via feedback loops.
Data Governance and Security
- Managing data masking and anonymization processes.
- Ensuring compliance within cloned environments.
- Implementing 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 the performance of clone provisioning.
- Capacity planning and cost management strategies.
Summary and Next Steps
Requirements
- A solid understanding of PostgreSQL database administration.
- Practical experience with Linux server environments.
- Familiarity with containerized or virtualized deployment workflows.
Target Audience
- Database Administrators.
- DevOps and SRE Engineers.
- Data Infrastructure Architects.
Testimonials (4)
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