CData Virtuality
Customer data platforms (CDP)
Data virtualization software
Big data integration platforms
ETL tools
On-premise data integration software
Data warehouse solutions
Data replication software
Backup software
Data integration tools
Cloud data integration software
Data recovery software
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
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What is CData Virtuality
CData Virtuality is a data virtualization and integration platform that provides a unified SQL-based access layer across multiple data sources and supports building a logical data warehouse. It is used by data engineering and analytics teams to connect, transform, and serve data for BI, reporting, and operational use cases without requiring all data to be physically moved first. The platform combines virtualization with optional replication/ELT and caching to improve performance and availability. It is typically deployed in enterprise environments that need governed access to heterogeneous data sources across on-premises and cloud systems.
Unified SQL access layer
The platform exposes disparate sources through a consistent SQL interface, which can reduce the need for custom point-to-point integrations. This approach supports faster onboarding of new sources for analytics and operational reporting. It also fits teams that standardize on SQL skills rather than specialized integration scripting. Compared with tools focused mainly on customer success or marketing operations, it is oriented toward enterprise data access and analytics enablement.
Broad connector ecosystem
CData Virtuality leverages CData connectivity to integrate with a wide range of databases, SaaS applications, and file/object storage systems. This breadth can simplify integration projects where data spans many vendor systems. It is useful for organizations that need to combine operational and analytical sources in one governed layer. Connector availability can reduce time spent building and maintaining custom APIs.
Virtualization plus replication options
In addition to virtualization, the platform supports patterns such as caching and replication/ELT to address latency, source-system load, and availability constraints. This hybrid model helps when some workloads require near-real-time access while others benefit from materialized copies. It can support incremental data movement where full warehouse loading is not practical. The combination provides flexibility for different performance and reliability requirements.
Not a CDP by design
Although it can unify customer-related data, it does not provide the packaged identity resolution, audience management, and campaign activation workflows typically expected in a dedicated CDP. Teams looking for out-of-the-box customer lifecycle features may need additional applications on top. It is better positioned as an integration and data access layer than as a marketer-operated customer platform. This can increase implementation effort for customer-facing activation use cases.
Requires data engineering expertise
Successful deployments typically require strong data modeling, SQL, and data governance skills to design virtual schemas and manage performance. Organizations without dedicated data engineering resources may find setup and ongoing tuning challenging. Operationalizing semantic layers, security, and lineage often needs careful planning. This can lengthen time-to-value compared with more guided, workflow-driven tools.
Performance depends on sources
Virtualized queries can be constrained by the performance and concurrency limits of underlying systems, especially when joining across multiple remote sources. Caching/replication can mitigate this, but it introduces additional configuration and data freshness trade-offs. Some workloads may still require a physical warehouse or dedicated marts for predictable performance. Monitoring and query optimization become important as usage scales.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Standard | Not publicly listed — contact sales | Cloud: 10 concurrent queries; 20 connections. Includes data virtualization (live data access), ETL/ELT, and standard connectors. |
| Professional | Not publicly listed — contact sales | Cloud: 15 concurrent queries; 50 connections. Everything in Standard plus: 1 premium connector, Business Data Shop (data portal), 1 development environment, Git integration. |
| Enterprise | Not publicly listed — contact sales | Cloud: 25+ concurrent queries; unlimited connections. Everything in Professional plus: unlimited premium connectors, multiple development environments, Single Sign-On (SSO), Massively Parallel Processing (MPP), clustering (clustering requires min. 75 concurrent queries). |
Notes: Deployment options include Cloud (SaaS) and Self-hosted (on-prem / self-hosted cloud). Packages can be optimized by adding concurrent queries or switching to core-based (CPU/core) pricing; for tailored pricing contact sales (email listed on site).
Seller details
CData Software, Inc.
Chapel Hill, North Carolina, USA
2006
Private
https://www.cdata.com/
https://x.com/cdata
https://www.linkedin.com/company/cdata-software/
