Beyond the Lakehouse: Denodo’s RAG-Driven Data Revolution
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Imagine you’re a financial analyst at a pension fund, racing against the clock to deliver a major corporate client’s portfolio breakdown before fiscal year end. You’re juggling CRM data, financial market reports, and a tangle of Excel exports. Or picture yourself as an automation engineer at a manufacturing giant, hunting for an old product spec buried in manuals, pricing sheets, and archived sales data. Sound familiar? This is the chaos of modern enterprise data-fragmented and siloed. For generative AI (GenAI) applications powered by retrieval-augmented generation (RAG), which thrive on clear, contextualized information, this fragmentation means increased hallucinations or outright failure.

Data lakehouses, touted as the ultimate solution, often fall short, leaving structured and unstructured data at odds. Without a unified layer to make sense of it all, GenAI output can range from vague to outright wrong. The Denodo Platform, with its metadata-driven semantic layer and full support for the retrieval-augmented generation (RAG) framework, cuts through the clutter. Think of it as a digital librarian, with the semantic layer acting as the library’s card catalog, organizing enterprise data into a single, AI-ready source of truth. Known as Query RAG, Denodo’s metadata-driven approach to RAG delivers relevant and accurate data, without the overhead of having to consolidate all the underlying data into a single location, such as a data lakehouse.

The Challenge: Data Fragmentation Meets GenAI 

So why aren’t data lakehouses the answer?  Data lakehouses promise the best of data lakes and data warehouses, but they often become silos themselves. CRM systems don’t sync with ERPs. Legacy databases live in isolation. For GenAI, which needs rich, real-time, contextual data to power applications like chatbots or predictive models, this fragmentation is a dealbreaker. Add regulatory hurdles like Europe’s General Data Protection Regulation (GDPR) or China’s Personal Information Protection Law (PIPL), and the challenge grows. You need secure, compliant data without compromising speed. GenAI applications can’t deliver accurate answers if they’re fed a patchwork of disconnected data. What’s needed is a platform that unifies, contextualizes, and secures data, so AI can shine. That’s where Denodo comes in. 

The Solution: Denodo’s Semantic Layer and RAG 

The Denodo  Platform acts like a master conductor, harmonizing data from CRMs, ERPs, cloud storage, and legacy systems without needing to first physically move it. Its semantic layer, built on rich metadata, creates a unified view, letting you query data with natural language or APIs. Ask, “What’s the sales trend for Q2?” and solutions built with the Denodo Platform can pull it together from multiple sources in real time. The real magic lies in Denodo’s support for Query RAG. Traditional AI models rely on static knowledge, but RAG enables GenAI applications to fetch live, relevant data during inference. The Denodo AI SDK, open-sourced on GitHub, streamlines data preparation – embedding, transforming, and orchestrating data for large language models (LLMs). The result? GenAI responses that are accurate, contextual, and grounded in your enterprise data. 

Alexforbes: From Data Swamp to GenAI-Powered Insights

Alexforbes, a leading South African financial services firm managing over one million pension-fund members each month, faced fragmented CRM, ERP and legacy systems that hindered Power BI-based financial calculations and real-time analytics. To overcome siloed data and integration bottlenecks, the company implemented the Denodo Platform, virtualizing all financial and operational sources in place to enable secure, governed real-time access without requiring data movement. Leveraging the Denodo AI SDK with RAG, Alexorbes embedded contextual metadata – rather than entire datasets – into a vector store, enabling natural-language queries to be translated into optimized SQL on the fly, minimizing compute overhead and hallucinations. A custom Power BI widget, built with the AI SDK and Power BI Extension Kit, enabled non-technical users across nine countries to ask questions like “What’s the broker contribution for Q1?” directly within dashboards – delivering sub-second, accurate responses with multilingual support.

Festo AG: Unifying Automation Data for GenAI

Festo AG, a global leader in factory and process automation serving over 300,000 customers across 35 sectors with more than 30,000 products, sought to power its new GenAI applications – FestoGPT and Skillground – by integrating structured sales and pricing data with semi-structured machine and technical documentation. Early integration efforts using full-dataset embeddings proved costly and ineffective, while governance and security requirements demanded strict access controls and high-quality metadata. With Denodo Platform 9.1’s metadata-driven semantic layer and AI SDK, Festo avoided replicating massive datasets by embedding only metadata into a vector store, dynamically generating optimized queries at inference time via a single RESTful API layer spanning on-premises and cloud sources. Role-based access controls, dynamic masking, and audit logging protected sensitive information in compliance with the GDPR. As a result, FestoGPT and Skillground deliver real-time, accurate insights – from product configuration details to energy-efficiency KPIs – through natural-language queries, empowering consultants and shop-floor engineers alike, while Festo realized improved sales performance and KPI reporting, streamlined data discovery, and significant storage-cost savings by eliminating unnecessary data duplication.

The Denodo Platform’s semantic layer is like a master indexer, categorizing and contextualizing data across hybrid cloud environments – on-premises databases, AWS S3, Azure, and more. Its RESTful APIs and vector database support enable RAG, so GenAI can query live data with minimal latency. Query optimization ensures speed, even for complex, multi-source requests. Security is airtight. The Denodo Platform’s role-based access, dynamic data masking, and encryption align with GDPR, CCPA, and other regulations. The AI SDK simplifies data preparation, embedding structured and semi-structured data for LLMs. For unstructured data like PDFs, the Denodo Platform integrates with third-party tools like AWS Textract, enabling flexibility without compromising performance. 

GenAI’s potential hinges on data quality. As enterprises move from experimentation to deployment, fragmented data remains a key hurdle. The Denodo Platform addresses this head-on, grounding GenAI with real-time, unified, secure data. Whether it’s answering financial queries at Alexforbes or powering automation insights at Festo, the Denodo Platform makes GenAI deliver results, not guesswork. 

Denodo as Your GenAI Enabler 

Denodo isn’t just keeping up with the GenAI revolution – it’s driving it. Its RAG-powered semantic layer transforms chaotic data into a unified, AI-ready asset. Organizations like Alexforbes and Festo are proof: real-time logical data management doesn’t just streamline operations; it redefines what’s possible. Ready to make GenAI work for you? Denodo is the key. Explore Denodo’s GenAI capabilities or check out our AI SDK on GitHub.

Sunny Panjabi
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