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Duration 21 hours
Course Outline
Introduction to PostgresAI
- Exploring the architecture and key components of PostgresAI.
- Core concepts including cloning, snapshots, and sandbox environments.
- Scenarios for enterprise adoption and return on investment (ROI).
Installation and Configuration of PostgresAI
- Deploying PostgresAI in Docker and Kubernetes environments.
- Integrating with PostgreSQL and external storage backends.
- Setting up authentication and access management protocols.
Database Cloning and Experimentation
- Creating instant database clones via thin provisioning.
- Safely testing schema changes using ephemeral environments.
- Streamlining CI/CD processes with PostgresAI clones.
Monitoring and Observability
- Leveraging PostgresAI dashboards for actionable performance insights.
- Monitoring clone health and query execution metrics.
- Integrating with Grafana, Prometheus, and ELK stacks.
AI-Driven Query Optimization
- Applying AI-based recommendations to improve query efficiency.
- Analyzing query plans and execution patterns.
- Implementing continuous optimization through feedback loops.
Data Governance and Security
- Managing data masking and anonymization strategies.
- Maintaining compliance standards within cloned environments.
- Implementing audit logging and role-based access controls.
Integrating PostgresAI with Enterprise Workflows
- Integrating with CI/CD tools such as 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 cluster configurations.
- Optimizing the performance of clone provisioning.
- Strategic capacity planning and cost management.
Summary and Next Steps
Requirements
- A solid grasp of PostgreSQL database administration.
- Practical experience with Linux server environments.
- Familiarity with deployment workflows involving containers or virtualization.
Target Audience
- Database administrators.
- DevOps and SRE engineers.
- Data infrastructure architects.
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