Model Context Protocol (MCP) finally gives AI models a way to access the business data needed to make them really useful at work. CData MCP Servers have the depth and performance to make sure AI has access to all of the answers.
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Turn disparate data into insights 80% faster with an independent semantic layer
Bridge the gap in your heterogeneous data landscape and speed data product delivery
The essentials of modern data architecture—in one layer
Unify cloud, application and on-premises data with a high-performance semantic layer that delivers instant, governed access—regardless of where it lives.
Seamlessly connect heterogeneous data —
bridging the gap between digital platforms and legacy systems for real-time insight
Scale performance on every level —
with intelligent connectors, multi-layer query optimization, and MPP engine
Centralize data governance —
providing holistic management of physical and virtual data assets with corresponding business, operational, and technical metadata
Deliver data products across disparate environments —
put the right data into the hands of the right users at the right time
CData Virtuality in your modern data architecture
Data Consumers
Business Intelligence Tools
AI & ML
Apps
Microservices
Data Product Delivery
Central Data Governance and Metadata Management
Data Modeling
Data Connectivity
Data Sources
Data Warehouse, Databases
Data Lake, No SQL Storages
Data Lakehouse
Online Analytics
Streaming, Kafka
Cloud, API, SaaS
Files (CSV, JSON, XML)
270+
Value for IT—Empowerment for data citizens
What IT gets
Integrate heterogeneous data sources with 270+ connectors easily, breaking down data silos and reducing custom coding and maintenance efforts.
What users experience
Gain real-time insights, greater visibility, and faster, more informed decision-making with a complete, unified view of data from all sources, eliminating data gaps.
What IT gets
Optimize performance across complex workloads using high-speed connectors, layered query tuning, and an MPP engine, with support for rapid prototyping of evolving data models.
What users experience
Make confident decisions backed by reliable performance—even as data volumes surge and complexity increases. Keep analytics consistent, responsive, and trusted across growing systems, sources, and business demands.
What IT gets
Consistently apply access controls and policies across systems, enhancing compliance and security.
What users experience
Reduce risk associated with inconsistent or unsecured data and rely on trustworthy, consistent data.
What IT gets
Deliver data products across disparate environments and enable self-service data access, reducing IT workload by empowering stakeholders to retrieve and analyze data independently.
What users experience
Take action independently, boost productivity, and provide swift responses to market changes by using a central data access point to quickly analyze data.
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How enterprises turn disparate data into insights with CData Virtuality
With CData Virtuality, you can
Build a central data access layer
that unifies complex data, allowing data citizens to perform self-service analytics with ease and efficiency.
Adopt a data mesh architecture
to boost organizational agility and flexibility, empowering domain teams to independently manage their data products.
Establish a data fabric
to enhance operational efficiency, simplify infrastructure, and cut costs.
Accelerate AI initiatives
with a robust data foundation, delivering high-quality, trusted, real-time data to power your AI models.
Integrate hybrid- and multi-cloud architectures
to lower operational costs and enhance business continuity.
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