Three years ago, Cloudera customers began exploring generative AI to transform data interactions—building intelligent assistants, summarizing complex documents, and generating insights on demand. And today, our customers manage more than 25 exabytes (that’s 25 billion gigabytes!) of enterprise data across on-premises and cloud environments.
The Context Gap in Enterprise AI
How organizations manage their data is key: in the age of AI, context isn’t just helpful—it’s the difference between accurate decisions and hallucinations. AI models need seamless access to proprietary data to generate insights, answer questions, or automate workflows. Yet, in most organizations, this data remains fragmented across siloed object stores, Iceberg tables, Kafka streams, and operational databases. Developers waste valuable time writing custom connectors and maintaining fragile pipelines—a tax on innovation that slows time to value.
Introducing Cloudera MCP Servers: A Universal Gateway to Your Data
That’s where Cloudera’s Model Context Protocol (MCP) Servers come in. Our servers are built on MCP and provide a universal gateway to govern enterprise data. MCP is an open standard that aims to standardize AI integration in the same way that Microsoft Open Database Connectivity (ODBC) standardized relational databases (more on MCP in the next section).
To support this mission, we’re launching with Cloudera MCP Server for Apache Iceberg via Impala. Apache Iceberg is the backbone of modern lakehouses, offering petabyte-scale management, ACID compliance, time travel, and granular governance. It’s the perfect starting point for bridging the gap between data and AI.
By starting with Apache Iceberg, we address a critical challenge: AI applications need real-time, governed access to analytical data without additional custom code. Our MCP Server enables developers to query Iceberg tables in natural language, integrate seamlessly with frameworks—like CrewAI, Microsoft AutoGen, LangChain or LangGraph, LlamaIndex, and agentic AI toolkits that work with these frameworks, like NVIDIA Agent Intelligence (AIQ) toolkit—while maintaining robust security with Cloudera SDX policies. And this is just the beginning: future Cloudera MCP Servers will extend support to streaming data, operational databases, and file/object stores.
Figure 1: Two scenarios of AI agents accessing data for AI context:
- Left: Without a common protocol, AI agents must handle multiple unique APIs to access context from each source.
- Right: MCP unifies access, enabling agents to retrieve context through a single interface, simplifying integration and improving scalability.
Agentic Architectures Need a Standard Integration Layer
As organizations rush to adopt agentic architectures, a consistent integration layer is more important than ever.
“The frenzy around adopting agentic architectures is driving organizations to launch multiple initiatives in parallel. While this momentum is encouraging, it also risks creating the modern equivalent of spaghetti code—something we’ve seen before in the early days of software engineering. What companies truly need is a simplified, standards-based architecture that ensures interoperability across the diverse systems participating in the agentic ecosystem. Anthropic’s MCP is emerging as a promising standard in this space, already seeing broad adoption from AI vendors.”
- Sanjeev Mohan, Principal at SanjMo and former Gartner analyst
MCP isn’t a proprietary Cloudera tool—it’s a widely adopted standard that avoids vendor lock-in while tapping into a growing ecosystem of tools. Cloudera’s approach to MCP Servers aligns with the MCP philosophy of openness, simplicity, and control. Cloudera MCP Servers run natively within Cloudera’s unified platform, eliminating risky data movement and enabling seamless deployment across both multi-cloud and on-premises environments.
A Tenet of Private AI: Bringing AI Compute to Data
AI’s transformative power relies on the quality of the data that fuels it. When data and AI systems operate in isolation, disconnected information delays insights, creates fragile pipelines, and leaves models without the necessary context for accurate decisions.
Cloudera brings data and AI together in a cohesive lifecycle. Data flows smoothly into AI workflows, governed by shared metadata, security policies, and optimized compute resources. This approach eliminates costly data duplication and movement while making every prediction traceable to its origin—ensuring transparency, trust, and compliance.
Take the Next Step
Ready to eliminate integration friction? Explore Cloudera MCP Server for Apache Iceberg here—currently available in preview—and discover how you can empower your AI applications with the context they need, right where your data lives. To put this into action today, try our FREE 5-day trial.
