What is Data Virtualization?

Data virtualization allows businesses to access, manage and integrate data from multiple sources in real-time.

Data Virtualization allows distributed databases as well as heterogeneous data storage to be accessed and viewed together as one database.

Data Virtualization servers can extract, transform, and integrate data virtually, rather than performing ETL on the data using transformation engines.

What is Data Virtualization and How Does It Impact a Modern Data Strategy

The Problem

Companies recognize the importance of maximizing their data assets to make better decisions, delight customers, and outperform their competitors.

Although this trend toward data-driven businesses is not new, Covid-19 has made it a significant step forward.

"Boards and CEOs agree that data and analytics are a game-changing technology and should emerge from the COVID-19 crises and make it their No.1 priority." "2021 is priority number one."

The Solution

These challenges are overcome by data virtualization (DV), which exploits the full potential of enterprise information. It eliminates the need for data to be mapped out in detail and allows data to be consolidated into a single view, which is possible without having to store it in centralized storage.

All data is retained in the source systems. Data Virtuality creates an underlying layer of virtual/logical data that allows real-time access to data and the ability to transform it into virtual views. This virtual layer allows for a more efficient and time-saving data management approach.

Data visualization tools make it possible to access data with SQL, REST, or other common query methods regardless of the source format. This simplifies data management.

Forrester and Gartner both confirm that data virtualization is a key data strategy enabler for enterprises looking to maximize their data.

"By 2022, 60% will have data virtualization as a key delivery method in their data integration architecture."

How Data Virtualization Works

The Virtual Data Layer/Semantic Layer

The virtual or semantic layer is the heart of any data virtualization application. It allows data users or businesses to manipulate, join, and calculate data regardless of where it is stored.

All connected data sources and metadata are displayed in one user interface. However, the virtual layer allows users to organize their data in different virtual schemas.

Users can quickly enrich the data from the source systems using simple business logic, and prepare the data to be used in analytics, reporting, and automation processes.

Many data virtualization tools extend this layer with metadata exploration and data governance capabilities. However, this functionality is not available in all tools.

Permission Management

The virtual layer uses sophisticated permission management that is user-based to create a single source of truth throughout the entire organization. It is fully compliant, secure, and compliant.

All authorized users now have access to the data they require from one point. This helps eliminate data silos and simplifies the data architecture.

Data virtualization doesn't usually persist in the source system data. This is in contrast with simple replication of data stores such as traditional ETL tools.

Data virtualization, however, stores metadata that feeds the virtual views and allows the creation of integration logic. This helps deliver integrated source data in real-time to any front-end application such as:

  • Tools and platforms for data analytics and business intelligence (BI)
  • Tools and programs that can be customized
  • Microservices

The Main Benefits of Data Virtualization

Data virtualization is a great way to integrate business data from different sources. It has many benefits.

Quicker Time to Solution

  • All data can be integrated immediately without the need for technical knowledge or manual code effort by using immediate data access.
  • Real-time data access distinguishes data virtualization from slower batch-style integration methods, which can lead to data accuracy and timeliness issues.
  • Data virtualization allows for faster design and prototyping, resulting in a quicker return on investment (ROI).
  • Instant access to information is available for many different reporting and analysis functions, which greatly accelerates and improves the decision-making process.

Simplicity and Flexibility

  • Rapid prototyping allows for faster test cycles and quicker transition to production environments.
  • Data sources are displayed in one interface. This means that data virtualization conceals the complexity of heterogeneous data landscapes.
  • Users can quickly adapt their business logic to changing demands using the virtual layer.


  • This approach is more cost-effective than traditional ETL solutions, as data remains in the source systems. This is a cheaper option than traditional ETL solutions, where data is converted into different formats and then moved to a storage area.
  • It is not necessary to change data sources or front-end systems. This can be done without costly restructuring and complicated effort.
  • Data virtualization is middleware. It allows existing infrastructure to seamlessly integrate with new applications while eliminating the need for costly and wasteful data silos.

Consistent, secure data governance

  • One data access point for all departments, rather than multiple, allows for simple permission and user management, while still adhering to GDPR.
  • To ensure that critical metrics are understood and managed across the company, KPIs and rules have been centrally defined.
  • Global metadata helps improve the governance of high-quality data. It also provides a better understanding of enterprise information through data lineage, metadata catalogs, and data lineage (depending on which tool).
  • Data virtualization is faster than other data integration methods because it allows for real-time access and can detect mistakes quickly.

Design considerations for data virtualization

Data Virtualization platforms offer many advantages over traditional data solutions.

There are some constraints to consider when designing your solution.

  • Data virtualization technology allows access to source data in real-time via production systems. This is in contrast to a data warehouse or master data management solution where data is often stored in pre-aggregated/sorted storage and therefore typically provides faster response times.
  • Data virtualization cannot provide a historical analysis of data. A data warehouse or analytic database is usually required, which is not the original idea of data virtualization.
  • In the virtual layer, data cleansing and/or transformation can be difficult.
  • Sometimes, changes to the virtual data model may require more effort. This is because all users and applications must accept it for it to be fully implemented.
  • Data virtualization began as a way to retrieve data with a single query language. This allowed for a quick response, and the ability to quickly create different views or models of data to suit specific requirements. This goal is not realized in all products.