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 Duration 35 hours

Course Outline

Introduction to ODI and Architecture

  • Core ODI concepts: the ELT approach and distinctions from traditional ETL
  • Essential components: Repositories, Agents, Topology, and Security
  • Installation overview and environment structure

ODI Studio and Development Components

  • Navigation within ODI Studio: Designer, Topology, Operator, and Security panels
  • Management of Projects, Models, and Datastores
  • Utilizing reverse-engineered metadata

Designing Mappings and Interfaces

  • Creating mappings via the graphical interface and ODI components
  • Incorporating procedures, variables, and packages into mappings
  • Implementing error handling and data validation strategies

Knowledge Modules and ELT Execution

  • Comprehending Knowledge Modules (KMs) and their classification
  • Selecting and tailoring KMs for various target environments
  • Addressing performance considerations and push-down optimization

Topology, Security, and Connectivity

  • Configuration of physical and logical schemas and data servers
  • Agent types, setup, and foundational high availability concepts
  • Security implementation: user management, profiles, and repository protection

Scheduling, Deployment, and Operational Management

  • Packaging and deploying scenarios
  • Scheduling strategies and integration with external schedulers
  • Job monitoring and troubleshooting using Operator and Logs

Advanced Techniques and Integration Patterns

  • CDC patterns, incremental loading, and change data capture methods
  • Integration with Big Data sources and Hadoop ecosystems
  • Best practices for creating modular and maintainable integration projects

Hands-on Labs and Real-World Case Study

  • End-to-end lab: designing, implementing, and deploying an ODI scenario
  • Performance tuning lab: analyzing and optimizing a slow mapping
  • Case study review: architectural decisions and key takeaways

Summary and Next Steps

  • Recap of key ODI concepts and integration design principles
  • Discussion on production deployment strategies and optimization techniques
  • Exploration of further learning paths and certification opportunities

Requirements

  • A solid grasp of relational database concepts
  • Proficiency in SQL
  • Knowledge of ETL or data integration principles

Target Audience

  • ETL and Data Integration developers
  • Data architects and engineers
  • DBAs and middleware engineers responsible for integration solutions

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