Module 1: Introduction to SAP Data Intelligence

• Overview of SAP Data Intelligence (DI)
• DI architecture: Modeler, Metadata Explorer, Connection Management
• SAP DI vs. SAP Data Services
• Use cases: Data integration, ML pipelines, data governance
• SAP DI on BTP and on-premise deployment

Module 2: SAP DI System Architecture

• Kubernetes-based architecture
• Tenant management and system landscape
• Connection management: SAP and non-SAP sources
• Repository and version management
• User management and authorization

Module 3: Data Pipeline Modeler

• Pipeline (graph) concepts
• Operators: Source, target, processing
• SAP-specific operators: ABAP, HANA, S/4HANA
• Custom operators using Python and JavaScript
• Pipeline groups and multiplexing

Module 4: Data Integration with SAP Sources

• SAP HANA integration
• S/4HANA and ECC data extraction (SLT, ABAP)
• BW/4HANA integration
• SuccessFactors and Ariba connectors
• SAP Landscape Transformation Replication Server (SLT)

Module 5: Non-SAP and Cloud Integration

• AWS S3, Azure Blob, GCP connectors
• Hadoop and Spark integration
• REST and OData connectors
• Kafka and message queue integration
• On-premise data source connectivity

Module 6: Data Transformation

• Data transform operator
• SQL-based transformations
• Python-based data processing
• Data quality rules and cleansing
• Aggregation and join operations

Module 7: Machine Learning Pipelines

• ML pipeline design in DI Modeler
• Model training operators
• ML scenario manager
• Model deployment and serving
• Integration with SAP AI Core

Module 8: Metadata Explorer

• Data catalog and metadata management
• Data profiling and quality scoring
• Business glossary management
• Data lineage tracking
• Rule-based data quality monitoring

Module 9: Data Governance

• Data governance framework in DI
• Personal data management (GDPR)
• Data classification and tagging
• Policy enforcement
• Audit and compliance reporting

Module 10: Monitoring and Operations

• Pipeline monitoring and logs
• Error handling and retry logic
• System monitoring dashboard
• Performance optimization
• Alert configuration

Module 11: SAP DI in S/4HANA and BTP Context

• Integration with SAP Datasphere
• Migration from SAP Data Services to DI
• Hybrid data management scenarios
• SAP Business Data Cloud overview

Module 12: Real-Time Projects and Case Studies

• Enterprise data lake integration
• Real-time IoT data pipeline
• End-to-end DI implementation
• Certification and interview preparation

Experience in the Industry Gain knowledge from experts with practical SAP Data Intelligence project experience in data engineering and analytics.

Backgrounds at the Top Prominent corporations such as HCL, TCS, Deloitte, and SAP Labs employ our SAP DI trainers.

Clear & Effective Teaching Excellent communication and real-world examples simplify complex data pipeline and governance concepts.

Hands-On Learning Focus Students apply skills using live SAP DI systems with real data integration and ML pipeline projects.

Up-to-Date Knowledge Trainers stay current with SAP Datasphere, SAP Business Data Cloud, and latest data management practices.