Enterprise Context Store and RAG on SAP HANA Vector Engine

About this course

Build a Production-Grade AI Context Store on SAP HANA Cloud From First PrinciplesSAP HANA Cloud now ships with a native vector engine. Enterprises running SAP landscapes will build their AI grounding infrastructure on it for Joule, for custom agents, for any RAG-based application that needs to retrieve enterprise context. Understanding how to design and implement that layer from first principles such as schema, embeddings, indexing, retrieval, and pipeline assembly is a critical technical skill. This course introduces you to those fundamental skills in full, using a real corpus, real SQL, and a real API integration. You will leave with working code, a mental model of how context engineering operates at the data layer, and a RAG pipeline.

You will design and implement a complete AI context store and RAG pipeline on SAP HANA Cloud Vector Engine from schema design to live query, using a financial services use case and a native implementation that connects directly to SAP HANA Cloud, without using the AI Launchpad and GenAI Hub. The course opens with a live chat client demo. You will see actual analyst questions answered with and without grounding, so you understand exactly what you are building toward and why it matters.

This course is deliberately compact, designed for professionals with limited time who need the highest possible takeaway per hour invested.

What You Will Build and Learn:

Understand why SAP HANA Cloud is the enterprise foundation context store, grounding Joule, AI Core, custom agents, and MCP and how it sits at the center of enterprise AI architectures

Understand vectors from the ground up: how VECTOR_EMBEDDING() computes them inside HANA, the difference between DOCUMENT and QUERY embedding types, and how cosine similarity measures semantic distance

Analyze a real document corpus — a credit risk policy, an earnings report, and a portfolio risk register, and make deliberate desi

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