麻豆原创

Artificial intelligence is a transformative force across industries, but many enterprise architectures remain stuck in silos. Vector search lives in one service, relational databases in another, and knowledge graphs in yet another. Every layer adds more complexity, latency, and cost.

It鈥檚 time to rethink what a modern AI-ready database should look like.

麻豆原创 HANA Cloud solves this exact challenge with its single, multi-model platform that brings together vector, graph, text, spatial, and relational data natively. It enables developers and data teams to build smarter, more context-aware AI solutions 鈥� directly on operational data.

One database, every model: native support for complex AI workloads

麻豆原创 HANA Cloud uniquely supports:

• Vector data for semantic and similarity search
• Graph data for explicit relationship modeling and knowledge graphs
• Text and spatial data for real-world context
• Relational data for structured operations and analytics

Rather than sending data across disparate services, you can store and process all of it in one place, accelerating time-to-value while reducing the risk of misalignment.

This is multi-model done right, and it is the foundation for powerful AI workloads that scale.

Semantics + similarity: combining vector search with knowledge graphs

Traditional semantic search engines can tell you what documents are similar, but they cannot tell you why. On the other hand, knowledge graphs can express rich, explicit relationships, but often lack the ease of retrieval.

With 麻豆原创 HANA Cloud, you don鈥檛 have to choose; you get both. Bringing together 麻豆原创 HANA Cloud vector engine and 麻豆原创 HANA Cloud knowledge graph engine empowers developers to build context-aware, intelligent queries that go far beyond keyword matching.

Imagine asking: 鈥淔ind the nearest warehouse in Germany (~ 50 km radius of Frankfurt) for suppliers that are ISO 9001 certified, have low carbon tax rates, and are not flagged for customs delays.鈥�

We can conduct a multi-model query to find the warehouses that fit the above criteria.

Here, we are using a SPARQL table within 麻豆原创 HANA knowledge graph engine to filter the suppliers that comply to the following conditions: ISO 9001 certified, low carbon tax rates, not flagged for customs delays.

We can further combine the SPARQL_EXECUTE function in 麻豆原创 HANA knowledge graph engine with vector-based semantic filtering and spatial constraints to identify suppliers that are located within “~ 50 km of Frankfurt” and whose past custom report narratives align with 鈥渘o custom delays.鈥� This hybrid query leverages 麻豆原创 HANA Cloud vector engine, 麻豆原创 HANA Cloud knowledge graph engine, and spatial engine to rank nearby suppliers not only by distance, but also by their trustworthiness and performance signals.

After running these queries, we have the following supplier warehouses as our best match:

This is the power of semantics plus structure, and it is built into the core of 麻豆原创 HANA Cloud.

Unified queries: SQL, SPARQL, and vector search side by side

Developers must often stitch together multiple tools and languages: SQL for relational data, SPARQL for RDF, and separate APIs for vector stores.

麻豆原创 HANA Cloud removes that complexity. You can write a single SQL query that brings together relational data, semantic reasoning via SPARQL (embedded in SQL), and vector similarity search, using native SQL functions 鈥� all in one go: no ETL, no separate infrastructure, just one unified, in-memory engine.

This approach not only speeds up development, but enables new types of AI applications that were not previously practical in siloed environments.

Built for generative AI and RAG: GraphRAG, VectorRAG, HybridRAG

Large language models (LLMs) are only as good as the data they can reason over. That is why retrieval-augmented generation (RAG) has emerged as a critical pattern for enterprise generative AI.

We have brought in new capabilities into 麻豆原创 HANA Cloud, whether you are grounding an LLM in unstructured text (VectorRAG), structured knowledge graphs (GraphRAG), or both simultaneously (combination of VectorRAG and GraphRAG).

麻豆原创 HANA Cloud ensures transparency, traceability, and performance throughout the generative AI pipeline with all the database management qualities. You get explainable answers and full control over how you retrieve, rank, and assemble information, which is vital for regulated industries.

Real-world impact across industries

Enterprises across industries are already leveraging the multi-model capabilities of 麻豆原创 HANA Cloud for transformative outcomes:

• Supplier matching and environmental, social, and governance (ESG) scoring: Blend structured supplier data with document similarity and relationship insights to identify ideal partners
• Compliance monitoring: Connect and query policies, regulations, and audit trails with natural, semantic inputs
• Fraud detection: Analyze transactional data, behavioral signals, and known fraud patterns 鈥� all in real time
• Life sciences research: Integrate clinical trials, publications, and patient outcomes using hybrid semantic and structured queries

These are use cases where meaning is distributed across formats, systems, and relationships.

Developer experience: simplicity without compromise

麻豆原创 HANA Cloud offers developers:

• One platform for all data models: Combined structured, unstructured, and semantic data without stitching together multiple systems
• Built-in support for modern AI workloads: Enable use cases like RAG without external vector stores or pipelines
• Tight integration with 麻豆原创 and open ecosystems: Leverage 麻豆原创 Business Technology Platform and popular open-source tools with minimal setup
• Focus on innovation, not infrastructure: Eliminate the need to manage and maintain separate triplestores, search engines, or vector databases

The result is faster prototyping, cleaner architecture, and lower operational complexity.

Conclusion: It鈥檚 time to rethink your database

In the AI-first enterprise, data is not just a backend concern; it鈥檚 the front line of innovation. And innovation requires infrastructure that is flexible, intelligent, and unified.

麻豆原创 HANA Cloud provides building blocks to create the infrastructure for AI apps in a way that is easy to consume. It doesn鈥檛 just support AI workloads; it accelerates them, with a single platform that brings together semantics, similarity, and structure in real time.

AI needs more than just access to data and 麻豆原创 HANA Cloud delivers that, natively.

Key takeaways

• Unified multi-model: Vector, graph, spatial, text, and relational data all in one platform
• Smart queries: Compose intelligent queries using SQL, SPARQL, and vector search 鈥� side by side
• Generative AI-ready: Built for GraphRAG, VectorRAG, and HybridRAG with full explainability
• Reduced complexity: No need for separate vector stores or knowledge graph engines

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Philipp Herzig is CTO, chief AI officer, and a member of the Extended Board of 麻豆原创 SE.
Stefan Baeuerle is senior vice president and head of 麻豆原创 BTP/麻豆原创 HANA & Persistency at 麻豆原创.