CDATA VIRTUALITY

Break down data silos, accelerate insights: CData Virtuality with Hopmann.

WHAT IS CDATA VIRTUALITY?

CData Virtuality is an enterprise semantic layer that combines data virtualization and ETL/ELT processes in a single platform. It creates a unified, governed data foundation across heterogeneous sources — without the need to replicate data unnecessarily. More than 300 pre-built connectors link databases, cloud services, SaaS platforms, APIs, and flat files via SQL.

The central data model ensures that all downstream reporting — whether in Tableau, Power BI, or any other BI tool — is based on the same, consistent KPI definitions. At the same time, CData Virtuality provides the foundation for AI applications within the organisation: clean, contextualised, and governed data is the prerequisite for reliable AI insights.

Hopmann Marketing Analytics is a CData Virtuality partner and supports you end-to-end — from license sales and implementation to ongoing operations.

OUR SERVICES AROUND CDATA VIRTUALITY

FEASIBILITY ANALYSIS

As the basis for your decision, we analyse whether and how CData Virtuality can be deployed within your existing data architecture — including an assessment of your data sources, requirements, and integration potential.

LICENSE CONSULTING & SALES

We advise you on selecting the right licensing model — cloud, on-premise, or hybrid — and handle the entire licensing process on your behalf.

IMPLEMENTATION

We carry out the technical implementation of CData Virtuality: from installation and semantic layer configuration to connecting your first data sources.

DATA HUB SETUP

We design and build a central data hub based on CData Virtuality — with a unified data model, defined KPIs, and a clear governance structure.

INTEGRATION OF NEW DATA SOURCES

We integrate your existing and new data sources — from legacy systems to cloud platforms and SaaS applications — quickly and with minimal development effort, thanks to 300+ pre-built connectors.

DATA INTEGRATION & TRANSFORMATION

We support you in modelling your data within the semantic layer: from defining consistent KPIs and transforming heterogeneous source structures to delivering unified views for your BI tools.

DATA GOVERNANCE & DATA QUALITY

We configure fine-grained permission structures — at schema, table, column, and row level — and ensure that your analyses are based on complete, consistent, and compliance-ready data.

TRAINING & ENABLEMENT

We offer individually tailored training sessions to enable your teams to use and develop CData Virtuality independently. Topics, scope, and timeline are defined together with you.

ONGOING SUPPORT & OPERATIONS

We are available as a long-term partner for all technical questions, further development needs, and the stable operation of your CData Virtuality environment.

CDATA VIRTUALITY – KEY BENEFITS

Semantic Layer as central data foundation

KPIs and business logic are defined centrally — ensuring that Tableau, Power BI, and all other BI tools deliver the same consistent numbers. No more contradictory reports from different departments.

300+ connectors for fast integration

New data sources can often be connected in minutes — without extensive custom development. All sources are addressed uniformly via SQL, from legacy systems to modern SaaS platforms.

Data virtualization & ETL/ELT combined

CData Virtuality combines real-time data virtualization with physical data movement in a single platform. An AI-based engine automatically recommends which data should be materialised for optimal performance.

Data Governance & Compliance

Fine-grained permission structures at schema, table, column, and row level. Integrated metadata management and data lineage provide full traceability of all data access.

AI-ready data foundation

CData Virtuality delivers the reliable data foundation on which AI applications, GenAI models, and RAG systems can be built — clean, contextualised, and governed data without quality compromises.

Flexible deployment options

Cloud-native by design, with support for on-premise, cloud, and hybrid environments. A fully managed SaaS option enables a fast start with minimal infrastructure overhead — scalable as needed.

What our customers often want to know.

FAQ on CData Virtuality

What is the difference between Data Virtuality and CData Virtuality?

Data Virtuality was acquired by CData Software in 2024 and has since operated under the name CData Virtuality. The product was strategically developed further: the data integration tool evolved into a full enterprise semantic layer — with expanded governance, AI-readiness, and 300+ connectors from the CData ecosystem.

What is a semantic layer and why do I need one?

A semantic layer is a virtual layer between your raw data and the tools that access it. It standardises business terms and KPI definitions, ensuring that Tableau, Power BI, and all other BI tools display the same numbers. Without a semantic layer, contradictory reports from different systems are a common problem in grown data landscapes.

How long does a CData Virtuality implementation take?

This depends on the number of data sources, the complexity of the data models, and the intended scope. An initial feasibility analysis with concrete results is possible within a few weeks. A full implementation including the semantic layer and first productive views typically takes two to four months. We recommend starting with a clearly scoped pilot project.

Can CData Virtuality work with our existing infrastructure?

Yes. CData Virtuality is designed to integrate into existing architectures without requiring fundamental changes. It connects with databases, data warehouses such as Snowflake, cloud services, SaaS platforms, and legacy systems alike — and supports on-premise, cloud, and hybrid environments equally.

When does CData Virtuality make more sense than a traditional data warehouse?

A data warehouse physically replicates data into a central system. CData Virtuality leaves data in place and creates a virtual, unified view instead. This makes sense when you need to respond quickly to changing requirements, have many heterogeneous sources, or want to avoid unnecessary data movement. Many organisations combine both approaches — CData Virtuality supports this natively.

How does CData Virtuality support AI initiatives within the organisation?

AI applications need clean, consistent, and contextualised data. CData Virtuality delivers exactly that: a governed data foundation with unified definitions that serves as the basis for GenAI models, RAG systems, and predictive analytics applications. Building a solid semantic layer today creates the prerequisite for deploying AI reliably in production tomorrow.