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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 (ROI)

PostgresAI Installation and Configuration

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

Database Cloning and Experimentation

  • Creating instant database clones using thin provisioning
  • Safely testing schema changes with ephemeral environments
  • Accelerating CI/CD processes with PostgresAI clones

Monitoring and Observability

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

AI-Driven Query Optimization

  • Utilizing AI-based recommendations for query improvement
  • Analyzing query plans and execution patterns
  • Implementing continuous optimization through feedback loops

Data Governance and Security

  • Managing data masking and anonymization
  • Ensuring compliance within 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
  • Best practices for team collaboration and environment sharing

Scaling PostgresAI Operations

  • Handling 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
  • Proficiency in containerized or virtualized deployment workflows

Target Audience

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

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