Data Management
November 4, 2026
CData’s platform connects and governs access to hundreds of enterprise systems for people, applications and AI agents
CHAPEL HILL, N.C., Nov. 4, 2026 — CData Software today announced that it has been positioned as a Challenger in the 2026 Gartner Magic Quadrant for Data Integration Tools. CData is the only vendor positioned in the Challenger quadrant in this year’s report.
CData’s platform provides a unified data layer that enables people, applications and AI agents to access and work with enterprise data across cloud and on-premises systems. As organizations move AI initiatives from experimentation into production, the platform brings together the connectivity, context and governance required to make enterprise data accessible and useful to a growing range of data consumers.
“We believe this placement reflects where we’ve put our effort: one data layer that works for people, applications and now AI agents, and now through our Connect AI Gateway,” said Amit Sharma, Co-founder and CEO of CData Software. “Agents need the same data foundation that people and applications already rely on, which is why our AI gateway builds on it.”
One data layer for people, applications and AI agents
Enterprise AI requires more than a connection to a dataanding of what that information means, and the ability to operate within established policies and permissions. The CData Platform brings these capabilities together through a common foundation that addresses three requirements for enterprise data access:
- Connectivity: Access to data across hundreds of cloud and on-premises systems, supporting integration, replication and live data access.
- Context: Enterprise data, documented business definitions and organizational knowledge that help AI systems interpret requests and produce relevant results.
- Control: Governance and permissions that help ensure people, applications and AI agents access data in accordance with organizational policies.
Extending data integration to AI
CData Connect AI Gateway extends the company’s data integration foundation to this emerging category of users. The gateway provides a control point for AI models, tools, data and actions, applying governed access while connecting agents to enterprise systems. It also supports model routing and access to the context agents need to work with business data.
The company continues to invest its platform across all three areas. Recent work includes change data capture for SAP HANA, DB2 for i, and SAP Gateway, along with built-in Git integration. The Context Engine builds a governed graph of system context, business definitions and company knowledge, so AI agents interpret enterprise data the way teams mean it. The AI gateway routes each LLM request to the right model and each MCP call to the right tools and data.
CData is also expanding the ways developers and organizations can connect AI systems to enterprise data through published connectors and integrations for leading AI platforms. In addition, CData Labs provides benchmark data for enterprise AI, so teams can see how AI performs on real enterprise data.
Read the full 2026 Gartner Magic Quadrant for Data Integration Tools report here.
CData is the AI gateway between AI and the systems that run the business. One control point connects agents and individuals to data across hundreds of enterprise systems, grounds every request in company context, governs it down to the record and routes it to the most efficient model. Built on CData’s data layer, it delivers accurate answers, governed access and falling cost, whether the user is an agent, an application or a person. CData powers AI workloads for Anthropic, Databricks, Microsoft, Google, Palantir and more than 10,000 customers worldwide.
The Big Data Inside Amazon’s New Fire Phone
The new Fire phone that Amazon launched this week looks like your ordinary black smartphone,…
Three Reasons to be Scared of the Internet of Things
We know the Internet of Things forecasts: 50 billion connected devices by 2020. Apparently, there’s…
Wanted: Intelligent Middleware That Simplifies Big Data Analytics
We’ve seen tremendous technological innovation in the data analytics space over the past 10 years….
GPUs Tackle Massive Data of the Hive Mind
LIVE from GTC12 — The flock of birds that weaves seamlessly through the sky, propelled…
Why Hadoop on IBM Power
In the quest to achieve data-driven insight, Hadoop running on Intel X86-based processors has emerged…
Datanami’s Leverage Big Data Summit Wraps Up
Dialog and networking were on the Datanami agenda this week as we kicked off our…
Who Controls Enterprise AI Agents? Data Platform Vendors Race to Provide the Answer
AI agents are everywhere. Every other news story is related somehow to AI agents. As…
The Token Trap: Why AI Costs Spiral at Scale
Most enterprise AI teams discover the token cost problem somewhere between the demo and production,…
Hugging Face Launches ProtoStar to Tackle Science’s Data Problem
There’s a lot of scientific data out there – and universities, government agencies, and research…
Dataiku Headless Tackles a Growing Problem With AI Coding Agents
Getting an AI coding assistant to write a data pipeline is becoming easier. No surprises…
Oracle’s New Lakehouse Tools Target a Blind Spot in Enterprise AI
Giving AI agents access to company data is one thing. Making sure they understand what…
Europe RAISEs the Stakes for AI in Science
Europe is making progress with its AI for science initiative, dubbed Resource for AI Science…
