Plan du cours

Introduction to ODI and Architecture

  • ODI concepts: ELT approach, differences 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 with graphical interface and ODI components
  • Using procedures, variables, and packages in mappings
  • Error handling and data validation strategies

Knowledge Modules and ELT Execution

  • Understanding Knowledge Modules (KMs) and their categories
  • Selecting and customizing KMs for different targets
  • Performance considerations and push-down optimization

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 integrating with external schedulers
  • Monitoring jobs and troubleshooting with 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: analyze and optimize 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 optimization techniques
  • Explore further learning paths and certification options

Pré requis

  • An understanding of relational database concepts
  • Experience with SQL
  • Familiarity with ETL or data integration concepts

Audience

  • ETL/Data integration developers
  • Data architects and engineers
  • DBA and middleware engineers responsible for integration solutions
 35 Heures

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