AI TOOL PROFILE
CData Virtuality: Enterprise Data Virtualization & Integration Platform
- Data and Analytics
- Data Integration
- Enterprise companies
- Mid-market companies
- Data engineering teams
- Organizations using data mesh or data fabric architectures
Pricing
Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
At a glance
- Best for
- Enterprise companies, Mid-market companies, Data engineering teams, Organizations using data mesh or data fabric architectures
- Key use cases
- Central Data Access Layer, Data Mesh Implementation, AI Data Foundation, Hybrid and Multi-Cloud Integration
- Integrations
- Snowflake, Databricks, Salesforce, Workday, BigQuery
- Official website
- Visit data virtuality platform official website
How AI is used
CData Virtuality is a data virtualization and integration platform designed to unify data across heterogeneous environments. It creates a semantic layer that allows users to access data from cloud applications, on-premises systems, and legacy platforms through a single point of entry without needing to move data into a single repository.
The tool is designed for operations that manage complex data landscapes. It supports the delivery of data products and is designed for use within data mesh or data fabric architectures, which may help organizations manage data across different departments.
From a technical perspective, it includes an MPP engine and multi-layer query optimization to handle large volumes of data. It also provides centralized governance to manage access controls and metadata across the connected sources.
Buyers should confirm their technical capacity, as the platform is aimed primarily at enterprise and mid-market companies with dedicated data engineering resources.
Key Features
Semantic Layer
Provides a unified view of data from cloud, application, and on-premises sources for governed access.
Data Connectors
Includes 270+ connectors to integrate various heterogeneous data sources.
MPP Engine
A massively parallel processing engine designed to scale performance for complex workloads.
Centralized Data Governance
Supports the application of access controls and policies across systems using business and technical metadata.
Query Optimization
Uses multi-layer query tuning to help manage performance across disparate data environments.
Self-Service Data Access
Designed to allow stakeholders to retrieve and analyze data independently through a central access point.
Use Cases
Central Data Access Layer
Creating a single entry point for users to perform self-service analytics across complex datasets.
Data Mesh Implementation
Supporting domain teams in managing and delivering their own data products.
AI Data Foundation
Providing a layer of governed, real time data to power AI models and production-ready AI agents.
Hybrid and Multi-Cloud Integration
Unifying data across different cloud providers and on-premises systems to support business continuity.
Integrations
- Snowflake
- Databricks
- Salesforce
- Workday
- BigQuery
- Dataverse
- Dynamics 365
- MongoDB
- SAP
- NetSuite
- Smartsheet
- Jira
- SharePoint
- Sage Intacct
- Tableau
- Power BI
- Excel
FAQ
What is CData Virtuality used for?
- It is used to create a semantic layer that unifies data from various cloud, application, and on-premises sources, allowing users to access data in real time without moving it.
Who is the target audience for this software?
- The platform is primarily designed for mid-market and enterprise companies with complex data landscapes and data engineering resources.
How does it support AI initiatives?
- CData Virtuality acts as a data layer that provides live connectivity and governed data to power AI models in production.
Source category: Data & Analytics
Source subcategory: Data Integration
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