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Duration 21 hours
Course Outline
Foundations of PostgresAI
- Exploring PostgresAI architecture and its core components.
- Key concepts: database cloning, snapshots, and sandbox creation.
- Enterprise use cases and Return on Investment (ROI) analysis.
Deploying and Configuring PostgresAI
- Setting up PostgresAI in Docker and Kubernetes ecosystems.
- Establishing connections with PostgreSQL and external storage backends.
- Implementing secure authentication and access management protocols.
Database Cloning and Testing Strategies
- Generating rapid database clones via thin provisioning techniques.
- Validating schema modifications within secure ephemeral environments.
- Enhancing CI/CD velocity through PostgresAI clone utilization.
Monitoring and Observability
- Utilizing PostgresAI dashboards for deep performance insights.
- Tracking clone health and query execution metrics.
- Connecting with monitoring stacks like Grafana, Prometheus, and ELK.
AI-Powered Query Optimization
- Applying AI-based recommendations to improve query efficiency.
- In-depth analysis of query plans and execution behaviors.
- Implementing continuous optimization cycles through feedback loops.
Data Governance and Security
- Managing data masking and anonymization processes.
- Maintaining compliance standards within cloned environments.
- Configuring audit logs and role-based access controls (RBAC).
Enterprise Workflow Integration
- Connecting PostgresAI with Jenkins, GitLab CI, or GitHub Actions.
- Automating testing pipelines for SQL and schema modifications.
- Adopting best practices for team collaboration and environment sharing.
Scaling PostgresAI Operations
- Managing large datasets and multi-node cluster architectures.
- Refining clone provisioning speed and efficiency.
- Strategic capacity planning and cost control.
Conclusion and Future Directions
Requirements
- Solid knowledge of PostgreSQL database administration.
- Practical experience managing Linux server environments.
- Proficiency with containerized or virtualized deployment workflows.
Target Audience
- Database Administrators (DBAs)
- DevOps and Site Reliability Engineering (SRE) professionals
- Data Infrastructure Architects
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