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SAP HANA Vector Engine & GenAI Online Training Certification Course Course Overview
SAP HANA Vector Engine & GenAI Training by Multisoft Systems is a specialized technical program designed for professionals seeking to use enterprise data with modern Generative AI applications. The course explains how SAP HANA Cloud can store, process, and search vector embeddings alongside structured business data, enabling semantic search and AI-driven information retrieval within enterprise environments.
Participants explore vector concepts, embedding generation, vector data handling, similarity measurement, semantic search, and Retrieval-Augmented Generation (RAG). The training further examines how retrieved enterprise context can be supplied to Large Language Models (LLMs) to generate more relevant and context-aware responses. Learners also gain exposure to SAP BTP and GenAI integration patterns, application connectivity, APIs, security, governance, and performance considerations.
The course emphasizes practical implementation scenarios in which SAP business information, documents, product information, knowledge bases, and other enterprise datasets can be transformed into searchable vector representations and incorporated into GenAI applications.
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SAP HANA Vector Engine & GenAI Online Training Certification Course Course curriculum
Curriculum Designed by Experts
SAP HANA Vector Engine & GenAI Training by Multisoft Systems is a specialized technical program designed for professionals seeking to use enterprise data with modern Generative AI applications. The course explains how SAP HANA Cloud can store, process, and search vector embeddings alongside structured business data, enabling semantic search and AI-driven information retrieval within enterprise environments.
Participants explore vector concepts, embedding generation, vector data handling, similarity measurement, semantic search, and Retrieval-Augmented Generation (RAG). The training further examines how retrieved enterprise context can be supplied to Large Language Models (LLMs) to generate more relevant and context-aware responses. Learners also gain exposure to SAP BTP and GenAI integration patterns, application connectivity, APIs, security, governance, and performance considerations.
The course emphasizes practical implementation scenarios in which SAP business information, documents, product information, knowledge bases, and other enterprise datasets can be transformed into searchable vector representations and incorporated into GenAI applications.
- Understand vector processing capabilities within SAP HANA Cloud.
- Explain embeddings and their role in semantic AI applications.
- Store and query vectorized enterprise information in SAP HANA.
- Perform vector similarity and semantic searches.
- Prepare and transform enterprise content for embedding generation.
- Design retrieval pipelines for Generative AI applications.
- Understand and implement RAG architecture concepts.
- Integrate SAP HANA-based retrieval with LLMs and GenAI services.
- Design enterprise semantic-search and knowledge-assistant solutions.
- Apply security, governance, performance, and production considerations to GenAI implementations.
Course Prerequisite
- Basic understanding of SAP HANA or relational databases
- Familiarity with SQL and database concepts
- Basic programming or application-development knowledge
- General understanding of APIs and application integration
- Fundamental awareness of cloud technologies
- Basic knowledge of AI, machine learning, or Generative AI concepts is beneficial but not mandatory
Course Target Audience
- SAP HANA Developers
- SAP HANA Consultants
- SAP BTP Developers and Consultants
- SAP Technical Consultants
- Generative AI Developers
- AI/ML Engineers
- Data Engineers
- Data Architects
- Solution Architects
- Application Developers
- Integration Developers
- Technical Leads
- Professionals working on enterprise AI and RAG implementations
Course Content
- SAP HANA Cloud architecture overview
- Role of SAP HANA in modern data architectures
- Introduction to Generative AI
- Large Language Models and enterprise applications
- Structured versus unstructured enterprise data
- Limitations of traditional keyword-based retrieval
- Role of semantic search in GenAI
- SAP ecosystem for enterprise AI
- Position of SAP HANA Vector Engine in GenAI architecture
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- Introduction to SAP HANA Cloud Vector Engine
- Vector processing concepts
- Vector Engine components and capabilities
- Vector processing within SAP HANA Cloud
- Combining relational and vector data
- Vector workloads and enterprise data
- Vector Engine architecture patterns
- Data flow across GenAI applications
- Understanding vector-based retrieval architecture
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- Understanding vectors and multidimensional representations
- What are embeddings?
- Text-to-vector transformation
- Semantic representation of enterprise content
- Embedding dimensions
- Embedding spaces
- Similarity between embeddings
- Dense vector concepts
- Documents, chunks, and embeddings
- Embedding model selection considerations
- Embedding lifecycle in GenAI applications
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- Vector data representation in SAP HANA
- REAL_VECTOR data type
- Creating tables for vector data
- Storing embeddings with business data
- Loading vector information
- Converting vector representations
- Vector-related SQL operations
- Managing vector columns
- Combining vector and conventional attributes
- Querying structured and vector information
- Managing document metadata with embeddings
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- Fundamentals of similarity search
- Semantic similarity concepts
- Query vectors
- Distance and similarity measurements
- Cosine similarity concepts
- Euclidean distance concepts
- Dot-product-based similarity
- Vector distance functions
- Ranking retrieved results
- Top-K retrieval
- Threshold-based retrieval
- Metadata-assisted filtering
- Hybrid retrieval patterns
- Evaluating retrieval relevance
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- Role of embedding models
- Embedding generation workflow
- Query and document embeddings
- Choosing appropriate embedding models
- Understanding LLM context
- Prompt and context relationships
- Connecting enterprise information with LLMs
- Model endpoint considerations
- Embedding consistency
- Handling model changes and re-embedding
- Token and context-window considerations
- Enterprise LLM integration patterns
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- Traditional search versus semantic search
- Semantic retrieval workflow
- Preparing enterprise documents
- Document extraction and preprocessing
- Chunking strategies
- Generating embeddings for chunks
- Storing chunks and metadata
- Creating semantic queries
- Executing vector similarity searches
- Ranking relevant passages
- Combining semantic search with business filters
- Improving retrieval quality
- Semantic enterprise knowledge search scenario
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- Introduction to RAG
- Why RAG is used with LLMs
- RAG architecture components
- Ingestion pipeline
- Document preprocessing
- Chunking and embedding pipeline
- Vector storage layer
- User-query embedding
- Retrieval workflow
- Context augmentation
- Prompt construction
- LLM response generation
- Grounding responses with enterprise data
- Reducing hallucination risks
- RAG versus model fine-tuning
- RAG architecture with SAP HANA Vector Engine
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- Designing an SAP HANA-based RAG pipeline
- Preparing enterprise knowledge sources
- Data ingestion strategies
- Document chunk management
- Embedding generation
- Persisting embeddings in SAP HANA
- Performing vector retrieval
- Applying metadata filters
- Selecting relevant context
- Prompt enrichment
- Passing retrieved context to an LLM
- Processing generated responses
- Source and context traceability
- Retrieval quality evaluation
- End-to-end RAG application workflow
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- SAP AI architecture overview
- Introduction to SAP AI Core
- Role of SAP AI Launchpad
- Introduction to Generative AI Hub
- Model access through Generative AI Hub
- Foundation model concepts
- Model selection considerations
- Connecting GenAI models with SAP applications
- SAP HANA Vector Engine as a retrieval layer
- Embedding and inference workflows
- Prompt orchestration concepts
- Combining HANA data with Generative AI Hub models
- Enterprise GenAI integration architecture
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- Designing enterprise GenAI applications
- Application architecture considerations
- Backend integration with SAP HANA Cloud
- Connecting applications to vector search
- Query processing workflow
- Context retrieval and augmentation
- LLM request and response handling
- API-based integration concepts
- Integration with SAP BTP applications
- CAP-based application integration concepts
- Conversational application patterns
- Knowledge assistants and enterprise copilots
- Handling sessions and conversational context
- Error handling and fallback strategies
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- Enterprise AI security considerations
- SAP HANA authentication and authorization concepts
- Protecting sensitive enterprise information
- Role-based data access
- Securing model and application connections
- Data privacy considerations
- Responsible AI principles
- Prompt and response governance
- Grounding and information traceability
- Vector storage considerations
- Retrieval performance optimization
- Managing large vector datasets
- Query optimization considerations
- Monitoring GenAI applications
- Cost and resource considerations
- Production deployment considerations
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Enterprise Use Cases
- Intelligent enterprise knowledge search
- Product information assistant
- Customer support knowledge assistant
- HR policy and employee information assistant
- Technical documentation search
- Procurement and supplier information retrieval
- Financial document intelligence
- SAP business data question-answering scenarios
- Context-aware enterprise copilots
Capstone Project – SAP HANA Vector Engine RAG Application
- Define enterprise GenAI use case
- Prepare sample knowledge documents
- Design document chunking strategy
- Generate vector embeddings
- Store vectors and metadata in SAP HANA
- Implement similarity-based retrieval
- Apply business and metadata filters
- Retrieve relevant contextual information
- Construct augmented prompts
- Integrate an LLM
- Generate context-grounded responses
- Test retrieval accuracy
- Evaluate generated responses
- Review security and governance considerations
- Present complete GenAI solution architecture
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SAP Implementation, Migration & Support Experts
End-to-end SAP services across transformation, BTP, HANA, cloud, migrations and managed operations.
S/4HANA Transformation
Greenfield, Brownfield and Selective Data Transition to S/4HANA with readiness assessments, conversion planning and smooth business cutover.
- Greenfield Implementation
- Brownfield System Conversion
- Selective Data Transition
- S/4HANA Readiness Assessment
SAP BTP & Integration
Integration Suite, side-by-side extensions, Build Apps, automation and analytics on a clean-core model for modern SAP landscapes.
- BTP Foundation Setup
- Integration Suite
- Extension & Clean Core
- Data & Analytics
HANA & Database
HANA administration, performance tuning, tenant management, backup, recovery and HA/DR design to keep the SAP core stable and fast.
- HANA Administration
- Performance & Tuning
- HA/DR Design & Build
- Backup, Recovery & Refresh
Cloud & Infrastructure
RISE with SAP, hyperscaler landing zones, sizing, monitoring, security, IaC and FinOps for scalable SAP cloud operations.
- RISE with SAP Advisory
- Hyperscaler Landing Zone
- Sizing & Capacity Planning
- Monitoring & FinOps
Migrations & Upgrades
Low-risk SAP migrations, OS/DB moves, release upgrades, Unicode conversion and datacenter relocation with rehearsed cutovers.
- OS/DB Migration
- Release & EHP Upgrades
- Unicode Conversion
- Datacenter / Cloud Migration
Managed Services / AMS
24×7 L1–L3 SAP support, Basis operations, incident management, system refresh and continuous improvement under SLA governance.
- Application Management L1–L3
- Basis Operations
- System Refresh
- Continuous Improvement
Explore Course Resources
Strengthen your learning with useful resources designed to help you prepare, practice, and evaluate your understanding.
SAP HANA Vector Engine & GenAI Training (MCQ) Assessment
This assessment tests understanding of course content through MCQ and short answers, analytical thinking, problem-solving abilities, and effective communication of ideas. Some Multisoft Assessment Features :
- User-friendly interface for easy navigation
- Secure login and authentication measures to protect data
- Automated scoring and grading to save time
- Time limits and countdown timers to manage duration.
SAP HANA Vector Engine & GenAI Corporate Training
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Why SAP HANA Vector Engine & GenAI Online Training Certification Course for Your Professional Growth
Strengthen your professional capabilities with practical learning, industry-relevant knowledge, and skills applicable to real-world business and technology environments.
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Gain knowledge aligned with current industry practices, technologies, processes, and professional requirements.
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Understand concepts through practical scenarios, instructor-led discussions, exercises, and use cases.
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Add valuable capabilities to your profile and explore opportunities across relevant roles, projects, and industries.
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Stay familiar with evolving technologies, methodologies, and practices shaping modern enterprise environments.
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Demonstrate your training achievement with a course completion certificate from Multisoft Systems.
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SAP HANA Vector Engine & GenAI Online Training Certification Course Trainer Profile
19+ Years Experienced
Our SAP HANA Vector Engine & GenAI Corporate & Certification Program trainers bring 13+ years of proven industry expertise, delivering practical insights aligned with real project environments.
Trained 3950+ Professionals
Our expert trainers have successfully trained 3350+ professionals through structured, real-time training programs designed for industry readiness and career growth.
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Gain practical exposure through real-time scenarios, industry case studies, and hands-on assignments that simulate actual project challenges.
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Receive expert support to prepare effectively, practice strategically, and confidently achieve globally recognized certification success.
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SAP HANA Vector Engine & GenAI Online Training Certification Course FAQ's
It is a technical training program focused on using SAP HANA's vector capabilities for embeddings, similarity search, semantic retrieval, RAG, and enterprise Generative AI applications.
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