Course Outline

Introduction to PostgresAI

  • Understanding PostgresAI architecture and components
  • Core concepts: cloning, snapshots, and sandbox environments
  • Enterprise adoption scenarios and ROI

PostgresAI Installation and Configuration

  • Deploying PostgresAI in Docker and Kubernetes environments
  • Integrating with PostgreSQL and external storage backends
  • Authentication and access management

Database Cloning and Experimentation

  • Creating instant database clones using thin provisioning
  • Testing schema changes safely with ephemeral environments
  • Accelerating CI/CD with PostgresAI clones

Monitoring and Observability

  • Using PostgresAI dashboards for performance insights
  • Monitoring clone health and query execution
  • Integration with Grafana, Prometheus, and ELK

AI-Driven Query Optimization

  • Leveraging AI-based recommendations for query improvement
  • Analyzing query plans and execution patterns
  • Continuous optimization using feedback loops

Data Governance and Security

  • Managing data masking and anonymization
  • Ensuring compliance in cloned environments
  • Audit logging and role-based access controls

Integrating PostgresAI with Enterprise Workflows

  • CI/CD integration using Jenkins, GitLab CI, or GitHub Actions
  • Automated testing pipelines for SQL and schema changes
  • Team collaboration and environment sharing best practices

Scaling PostgresAI Operations

  • Handling large datasets and multi-node clusters
  • Optimizing clone provisioning performance
  • Capacity planning and cost management

Summary and Next Steps

Requirements

  • An understanding of PostgreSQL database administration
  • Experience with Linux server environments
  • Familiarity with containerized or virtualized deployment workflows

Audience

  • Database administrators
  • DevOps and SRE engineers
  • Data infrastructure architects
 21 Hours

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