1. Advanced Data Transformation Techniques
   1.1. Deep dive into Talend’s transformation components (e.g., tMap, tDenormalize, tNormalize)
   1.2. Implementing complex data mapping and joining strategies
   1.3. Using Talend expressions and functions for advanced data transformations
   1.4. Handling nested data structures and hierarchical data
2. Optimizing ETL Job Performance
   2.1. Techniques for optimizing job performance and resource usage
   2.2. Best practices for handling large volumes of data
   2.3. Analyzing and tuning job performance metrics
   2.4. Implementing parallel processing and batch processing strategies
3. Automation and Scheduling with Talend
   3.1. Advanced scheduling techniques using Talend
   3.2. Automating data integration tasks and workflows
   3.3. Using Talend’s command-line tools and scripting for job automation
   3.4. Configuring and managing job triggers and dependencies
4. Error Handling and Logging
   4.1. Implementing advanced error handling and recovery strategies
   4.2. Using Talend’s logging components for detailed job tracking
   4.3. Configuring alerts and notifications for job failures and issues
   4.4. Analyzing and troubleshooting job logs and errors
5. Data Quality and Governance
   5.1. Advanced data quality techniques using Talend Data Quality components
   5.2. Implementing data profiling, cleansing, and enrichment processes
   5.3. Managing metadata and data lineage in complex scenarios
   5.4. Ensuring compliance with data governance policies and standards
6. Creating Reusable Components and Templates
   6.1. Designing and developing custom Talend components
   6.2. Creating reusable job templates and modular components
   6.3. Sharing and managing reusable assets within a team
   6.4. Leveraging Talend Exchange for community components and solutions
7. Integration with External Systems and APIs
   7.1. Connecting and integrating with external systems and services
   7.2. Using Talend components for web services, APIs, and messaging systems(Ref: IBM Watson for Building Chatbox | Computer Vision & Watson API)
   7.3. Handling authentication, authorization, and data exchange with external systems
   7.4. Implementing data synchronization and integration patterns
8. Advanced Data Integration Patterns
   8.1. Exploring advanced integration patterns and best practices
   8.2. Implementing change data capture (CDC) and incremental data processing
   8.3. Using Talend for real-time and near-real-time data integration
   8.4. Managing complex data flows and transformations across multiple systems
9. Case Studies and Real-World Scenarios
   9.1. Analyzing case studies of advanced Talend implementations
   9.2. Lessons learned from complex data integration projects
   9.3. Best practices and innovative approaches for data transformation and automation
   9.4. Future trends and advancements in data integration and automation
10. Final Project: Implementing an Advanced Data Integration Solution
    10.1. Designing and developing a complex data integration solution using Talend
    10.2. Implementing advanced transformations, automation, and optimization techniques
    10.3. Demonstrating error handling, logging, and performance tuning
    10.4. Presenting and reviewing project outcomes and integration solutions
11. Conclusion and Next Steps
    11.1. Recap of advanced concepts and techniques covered in the course
    11.2. Additional resources for continued learning and certification
    11.3. Career development opportunities in advanced data integration and Talend
    11.4. Staying current with Talend updates and industry trends

To conclude; this course provides a comprehensive understanding of advanced data integration solutions with Talend. Participants will gain practical skills and knowledge to enhance their data transformation processes and career opportunities.

Reference