• SAP Data Intelligence enables end-to-end data orchestration and governance
• It supports data-driven innovation and enterprise analytics
• Key use cases for data pipelines and machine learning are introduced
• Platform positioning within SAP Data & Analytics portfolio is explained
• Certification scope and learning objectives are outlined
• It supports data-driven innovation and enterprise analytics
• Key use cases for data pipelines and machine learning are introduced
• Platform positioning within SAP Data & Analytics portfolio is explained
• Certification scope and learning objectives are outlined
• SAP Data Intelligence runs on SAP Business Technology Platform
• Core components and services are explained
• Integration with SAP HANA, SAP BW, and cloud services is covered
• Tenant and landscape setup concepts are introduced
• Architecture supports scalable data operations
• Core components and services are explained
• Integration with SAP HANA, SAP BW, and cloud services is covered
• Tenant and landscape setup concepts are introduced
• Architecture supports scalable data operations
• Connections to SAP and non-SAP data sources are configured
• Batch and real-time data ingestion methods are explained
• Adapters support databases, files, and streaming data
• Secure data connectivity is established
• Integration ensures reliable data movement
• Batch and real-time data ingestion methods are explained
• Adapters support databases, files, and streaming data
• Secure data connectivity is established
• Integration ensures reliable data movement
• Pipelines define end-to-end data processing flows
• Operators manage data extraction, transformation, and loading
• Graphical pipeline modeling simplifies orchestration
• Scheduling controls pipeline execution
• Orchestration supports automated data workflows
• Operators manage data extraction, transformation, and loading
• Graphical pipeline modeling simplifies orchestration
• Scheduling controls pipeline execution
• Orchestration supports automated data workflows
• Metadata captures technical and business data context
• Data catalog supports data discovery and reuse
• Lineage tracking shows data flow across systems
• Impact analysis supports data governance
• Metadata management improves data transparency
• Data catalog supports data discovery and reuse
• Lineage tracking shows data flow across systems
• Impact analysis supports data governance
• Metadata management improves data transparency
• Data governance enforces enterprise data standards
• Validation rules ensure data accuracy and consistency
• Profiling analyzes data structure and quality
• Error handling manages invalid data records
• Governance supports compliant data operations
• Validation rules ensure data accuracy and consistency
• Profiling analyzes data structure and quality
• Error handling manages invalid data records
• Governance supports compliant data operations
• Integration with machine learning frameworks is explained
• Model training and execution pipelines are introduced
• Python-based operators support advanced analytics
• ML lifecycle management concepts are covered
• Advanced analytics enable intelligent data processing
• Model training and execution pipelines are introduced
• Python-based operators support advanced analytics
• ML lifecycle management concepts are covered
• Advanced analytics enable intelligent data processing
• Role-based access controls manage user permissions
• Secure data access protects sensitive information
• Authorization concepts support enterprise security
• Audit logs track data usage and changes
• Privacy controls ensure regulatory compliance
• Secure data access protects sensitive information
• Authorization concepts support enterprise security
• Audit logs track data usage and changes
• Privacy controls ensure regulatory compliance
