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CData Virtuality

by CData Software · Since 2012
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Active1+ countriesCloudOn-premise
Quick facts
VendorCData Software
Year launched2012
StatusActive
Location101 Europa Dr. #110 Chapel Hill, NC 27517 USA
Countries served1+
Languages1
IntegrationsN/A
Free tierNO
Free trialYES
Contact salesYES

About CData Virtuality

CData Virtuality is a modern data virtualization and logical data warehouse platform designed to help organizations unify disparate data sources and deliver real‑time business insights without the need to physically relocate data.

CData Virtuality is a modern data virtualization and logical data warehouse platform designed to help organizations unify disparate data sources and deliver real‑time business insights without the need to physically relocate data. Originally developed by Data Virtuality (founded in 2012), it became part of CData Software’s product suite following an acquisition in 2024, bringing together robust abstraction capabilities and CData’s extensive live data connectivity solutions. At its core, Virtuality enables enterprises to build a central semantic layer that integrates cloud, application, and on‑premises systems — allowing users to perform analytics, reporting, and governance with consistent, governed access. The platform supports real‑time access, automated ETL and ELT workflows, and powerful SQL‑based data modeling. Users benefit from hundreds of pre‑built connectors that reduce complexity and accelerate deployment across hybrid environments. With an emphasis on data democratization, Virtuality supports data mesh and data fabric architectures, empowering domain teams while enforcing centralized governance and compliance. The solution is well‑suited for large enterprises aiming to drive analytics, AI initiatives, and agile data delivery at scale.

Pros & Cons

Pros
  • Connects to hundreds of heterogeneous data sources with unified semantic access.
  • Supports real‑time analytics without duplicating data for faster insights.
  • Empowers data governance with centralized policies across distributed systems.
  • Enables hybrid and multi‑cloud deployment, reducing infrastructure limits.
  • Accelerates analytics adoption by providing self‑service access to data teams.
Cons
  • High upfront setup complexity for connecting numerous heterogeneous sources.
  • Advanced procedural SQL knowledge needed for complex transformations.
  • Performance may degrade with extremely large datasets without tuning.
  • Lacks built-in visualization tools; requires third-party BI software.
  • Version upgrades can disrupt existing ETL pipelines if not carefully managed.

Features

Key features

Real‑time data access

Enables querying live data across sources without physical movement.

Semantic layer

Provides unified definitions and business logic across analytics tools.

Centralized governance

Enforces consistent policies for access and compliance.

Massively Parallel Processing

Handles complex analytical workloads efficiently.

Broad connector library

Supports hundreds of enterprise data sources.

Hybrid cloud support

Works across cloud, on‑premises, and multi‑cloud environments.

Scalable architecture

Adapts to growing enterprise data needs.

Additional features

Logical data warehousing

Combines virtualization and automated ETL for unified reporting.

Data federation

Aggregates data from distributed sources for analytics.

Data product delivery

Facilitates sharing data products with stakeholders.

Data modeling

Simplifies complex business logic across systems.

Connector optimization

Speeds data integration with intelligent tuning.

Governance metadata management

Tracks and controls metadata across data assets.

Self‑service access

Empowers users to explore data independently.

Pricing

Free trial
Free version
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Countries & Languages

1
Countries served
1
Interface languages
17
Billing currencies

Available in

All Countries.

Interface languages

English

Billing currencies

🇺🇸USD🇪🇺EUR🇬🇧GBP🇯🇵JPY🇨🇳CNY🇦🇺AUD🇨🇦CAD🇨🇭CHF🇸🇪SEK🇳🇴NOK🇩🇰DKK🇮🇳INR🇸🇬SGD🇭🇰HKD🇰🇷KRW🇷🇺RUB🇧🇷BRL

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