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Course Outline
Introduction to ODI and Architecture
- ODI concepts: the ELT approach and how it differs from traditional ETL.
- Core components: Repositories, Agents, Topology, and Security.
- Installation overview and environment layout.
ODI Studio and Development Components
- Navigating ODI Studio: Designer, Topology, Operator, and Security panels.
- Projects, Models, and Datastores.
- Working with reverse-engineered metadata.
Designing Mappings and Interfaces
- Creating mappings using the graphical interface and ODI components.
- Using procedures, variables, and packages within mappings.
- Error handling and data validation strategies.
Knowledge Modules and ELT Execution
- Understanding Knowledge Modules (KMs) and their categories.
- Selecting and customising KMs for different target systems.
- Performance considerations and push-down optimisation.
Topology, Security, and Connectivity
- Configuring physical and logical schemas and data servers.
- Agent types, configuration, and high availability basics.
- Security setup: users, profiles, and repository protection.
Scheduling, Deployment, and Operational Management
- Packaging and deploying scenarios.
- Scheduling strategies and integration with external schedulers.
- Monitoring jobs and troubleshooting using Operator and Logs.
Advanced Techniques and Integration Patterns
- CDC patterns, incremental loading, and change data capture approaches.
- Integrating with Big Data sources and Hadoop ecosystems.
- Best practices for modular, maintainable integration projects.
Hands-on Labs and Real-World Case Study
- End-to-end lab: design, implement, and deploy an ODI scenario.
- Performance tuning lab: analyse and optimise a slow mapping.
- Case study walkthrough: architecture decisions and lessons learned.
Summary and Next Steps
- Review key ODI concepts and integration design principles.
- Discuss production deployment strategies and optimisation techniques.
- Explore further learning paths and certification options.
Requirements
- A solid understanding of relational database concepts.
- Practical experience with SQL.
- Familiarity with ETL or data integration concepts.
Target Audience
- ETL and data integration developers.
- Data architects and engineers.
- DBAs and middleware engineers responsible for integration solutions.
35 Hours
Testimonials (1)
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