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Course Outline

Introduction to ODI and Architecture

  • Core ODI concepts: The ELT approach and distinctions from traditional ETL
  • Key components: Repositories, Agents, Topology, and Security
  • Overview of installation and environment setup

ODI Studio and Development Components

  • Navigating ODI Studio: Designer, Topology, Operator, and Security panels
  • Understanding Projects, Models, and Datastores
  • Working with reverse-engineered metadata

Designing Mappings and Interfaces

  • Creating mappings using the graphical interface and ODI components
  • Utilizing procedures, variables, and packages within mappings
  • Implementing error handling and data validation strategies

Knowledge Modules and ELT Execution

  • Comprehending Knowledge Modules (KMs) and their various categories
  • Selecting and customizing KMs for different target systems
  • Addressing performance considerations and push-down optimization

Topology, Security, and Connectivity

  • Configuring physical and logical schemas along with data servers
  • Agent types, configuration settings, and basics of high availability
  • Establishing security: managing users, profiles, and repository protection

Scheduling, Deployment, and Operational Management

  • Packaging and deploying scenarios
  • Developing scheduling strategies and integrating with external schedulers
  • Monitoring jobs and troubleshooting using the Operator and Logs

Advanced Techniques and Integration Patterns

  • Exploring CDC patterns, incremental loading, and change data capture methods
  • Integrating with Big Data sources and Hadoop ecosystems
  • Adhering to best practices for modular, maintainable integration projects

Hands-on Labs and Real-World Case Study

  • Comprehensive lab: designing, implementing, and deploying an ODI scenario
  • Performance tuning lab: analyzing and optimizing a slow-performing mapping
  • Case study walkthrough: reviewing architecture decisions and key lessons learned

Summary and Next Steps

  • Reviewing essential ODI concepts and integration design principles
  • Discussing production deployment strategies and optimization techniques
  • Exploring further learning paths and certification opportunities

Requirements

  • A solid understanding of relational database concepts
  • Practical experience with SQL
  • Familiarity with ETL or general data integration concepts

Audience

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

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