A few months ago, I spoke with the head of data architecture at a leading European bank. They’d just completed a multi-year investment in a modern data lakehouse platform — a combination of Databricks on Azure, paired with legacy systems like SAS, Oracle, SharePoint, and a few aging dashboards still pulling from Excel.
“We’ve modernized the stack,” he said. “But when the CFO asks for insights across Claims, HR, and Finance… we still have to stitch things together manually, which is a gigantic pain.”
This pain wasn’t about performance. It wasn’t about scalability. It was about semantic chaos, disconnected access, and data governance bottlenecks.
This bank wasn’t alone.
When a Data Lakehouse Isn’t Enough
Data lakehouses like Databricks and Snowflake are powerful. They unify structured and unstructured data, enable scalable machine learning (ML), and streamline pipelines. But they don’t eliminate all the complexity — especially in hybrid, regulated, multi-platform environments.
For this bank, the gaps were clear:
- No Unified Business Layer: Business users couldn’t make sense of cross-domain data without help from engineers. Definitions varied by department. “Customer” meant different things in different reports.
- Fragmented Data Landscape: Critical data was still siloed — spread across an Infocenter, SAS models, SharePoint folders, and multiple Databricks clusters.
- Compliance Headaches: Azure-native policies like RBAC and ABAC were only partially enforced. SharePoint couldn’t be cleanly integrated into the lakehouse model. Auditors flagged the risk.
The bank didn’t need to abandon or bypass the lakehouse.
The bank needed a layer above it — one that could abstract complexity, align semantics, and enforce policies, in real time.
The Missing Link: A Real-Time Semantic Layer
Enter Denodo.
By deploying the Denodo Platform as a real-time semantic layer within Azure, this bank connected over a dozen platforms — including SharePoint, Databricks, Oracle, and SaaS — into a unified virtual layer. No data replication. No re-ingestion.
The Denodo Platform gave them:
- Instant cross-domain access to governed data
- Business-aligned semantics, so HR, Wealth Management, and Finance teams all spoke the same language
- Enterprise-grade data governance with dynamic masking, RBAC, ABAC, and seamless integration into Collibra
The results?
What used to take months — like building reconciliation use cases for loan portfolios, credit card transactions, or treasury cash flows — now took days. Over 50 users, from data engineers to business analysts, could explore data securely and independently.
The Denodo Platform became a key enabler of the bank’s data mesh strategy, seamlessly complementing the existing Databricks deployment to deliver governed, real-time data access across domains, while simplifying self-service access for business users.
Meanwhile, in Another Industry: When GenAI Gets It Wrong
A large regional telco with a data lakehouse had built a generative AI (GenAI)-powered chatbot using retrieval-augmented generation (RAG). Its job? To assist customer service reps and end-users by answering questions drawn from structured internal systems, including service provisioning databases, billing platforms, device inventory systems, and customer account data. This information was embedded, converting key fields and metadata into vector representations, before being stored in the vector database for retrieval.
It sounded great, but it failed in practice:
- Sometimes it returned outdated plan pricing from legacy systems.
- Other times it confused service tiers — mixing up enterprise-grade offerings with consumer bundles.
- On a few occasions, it revealed internal troubleshooting notes that should have been hidden from customers.
Why? Because the model relied on disconnected information from siloed systems and limited data governance and security controls — There was no unified semantic layer to standardize definitions, enforce access policies, or interpret the relationships between data sources in real time.
Once Denodo was introduced, the difference was immediate.
Instead of relying solely on pre-embedded content, the chatbot now used Query RAG — dynamically generating SQL queries to access live, governed data through the Denodo Platform’s logical data management layer. This same layer, already trusted by BI and analytics teams, delivered consistent semantics, dynamic masking, and real-time responses grounded in up-to-date enterprise systems.
The Big Picture
Whether it’s a bank looking to connect HR and Wealth Management information, or a telco building smarter digital assistants, the challenge is the same:
- Lakehouses provide speed and scale, but they lack a universal semantic layer and global data governance and security capabilities.
- The Denodo Platform brings real-time access to distributed data, with the semantic consistency, policy control, and self-service simplicity that business users need.
Together, they solve the puzzle.
Ready to Maximize Your Lakehouse?
If your data lakehouse is running well but your business teams still rely on painful workarounds, Denodo can help.
It’s not a replacement — it’s the semantic and governance layer your lakehouse needs to thrive in hybrid, real-time, AI-powered environments.
Learn more about data lakehouse optimization from our e-book, “A Modern Data Strategy with Denodo and Databricks,” here.
- Why Your Data Lakehouse Needs a Semantic Layer: A Story from the Trenches - May 6, 2025
- Preparing for a Logical Data Management Solution - June 25, 2024
- Getting the Fundamentals Right for Gen AI - March 21, 2024