Data Virtualization: Definition, Importance, How It Works & Key Benefits

Data virtualization is a transformative technology that enables organizations to access, manage, and analyze data without knowing its physical location or format.

This article explores data virtualization's definition, importance, and functioning, highlighting its key benefits and design limitations. It also examines various use cases and offers insights into top data virtualization vendors in today’s market.

What is data virtualization?

Data virtualization enables businesses to access, manage, integrate, and aggregate data from disparate sources independent of its physical location or format in real time. According to DAMA, creators of the Data Management Body of Knowledge (DMBOK), data virtualization is defined as follows:

“Data virtualization enables distributed databases, as well as multiple heterogeneous data stores, to be accessed and viewed as a single database. Rather than physically performing ETL on data with transformation engines, Data Virtualization servers perform data extract, transform and integrate virtually.”

This technology is crucial for modern data strategies, offering a unified view of data without the need for physical consolidation. By leveraging database virtualization tools and software, organizations can streamline data management processes, enhance data cataloging, and improve data service delivery. Data virtualization solutions provide a flexible and efficient approach to data integration, supporting data virtualization for the cloud and enabling seamless access to virtualized data across various data sources.

The importance of data virtualization solutions for modern data strategies

The problem

Organizations recognize that to make smarter decisions, delight customers, and outcompete rivals, they need to exploit their data assets more effectively. This trend towards data-driven business has accelerated due to COVID-19.

“Boards of directors and CEOs believe data and analytics is a game-changing technology to emerge from the COVID-19 crisis and place it as their No. 1 priority for 2021.”

– Gartner, Top Priorities for IT: Leadership Vision for 2021

Leveraging data analytics, business intelligence, and workflow automation helps companies generate new revenue streams and reduce costs by improving data services. However, enterprise data is often stored in disparate locations and formats, such as:

• Relational and non-relational databases (e.g., MySQL, Amazon Redshift, MongoDB)
• Cloud/SaaS applications (e.g., NetSuite, Salesforce, Mailchimp)
• Social media or website data (e.g., Facebook, Twitter, Google Analytics)
• CRM/ERP data (e.g., SAP, Oracle, Microsoft Dynamics)
• Data lakes and enterprise data warehouses
• Flat files (e.g., XML, CSV, JSON)
• Big data

The demand for faster, more complex data leads to challenges like:

• Delivering self-service capabilities for data users
• Creating time efficiency in data management
• Achieving trusted data quality

To address these challenges, organizations must move from data silos and isolated technologies to a strategy where data and analytics are integral to everyday business operations.

“Data and Analytics is no longer simply about dashboards and reports, it’s about augmenting decision-making across the business.”

– Gartner, Top Priorities for IT: Leadership Vision for 2021

The solution

Data virtualization overcomes data management challenges by fully exploiting enterprise data. It aggregates data into a single ‘view’ without moving it to central storage. Data remains in source systems, while DV creates a virtual layer for real-time access, manipulation, and transformation. This approach simplifies and speeds up data management.

DV tools make data accessible via SQL, REST, or other query methods, regardless of source format, further easing data management. Forrester and Gartner confirm that data virtualization is now a critical strategy for enterprises aiming to leverage their data more effectively.

How data virtualization works

The virtual data layer/semantic layer

The core of a data virtualization application lies in the virtual or semantic layer, a critical component within a data fabric. This layer enables users to manipulate, join, and calculate data seamlessly, regardless of its source format or physical location—whether stored in the cloud or on-premises.

Within a unified data fabric, all connected data sources and metadata are accessible through a single user interface. The virtual layer allows users to organize data into different virtual schemas and views, enriching raw data with business logic to prepare it for analytics, reporting, and automation.

Some data virtualization tools extend this layer with advanced data governance and metadata exploration capabilities, enhancing the data fabric further by providing comprehensive data management. However, these features vary across tools. 

Permission management

With sophisticated user-based permission management, the virtual layer creates a single source of truth across the organization in a compliant and secure manner. Authorized users can access the data they need from a single point, eliminating data silos and simplifying the data architecture.

Unlike traditional ETL tools that replicate data, data virtualization does not persist source system data. Instead, it stores metadata to feed virtual views and create integration logic, delivering real-time integrated data to front-end applications such as:

• Business intelligence (BI) tools and data analytics platforms
• Custom programs and tools
• Microservices

4 key benefits of data virtualization

Using data virtualization to integrate business data from disparate sources offers numerous benefits:

Faster time-to-solution

• Immediate data access allows real-time integration without extensive technical knowledge or manual coding.
• Real-time access sets data virtualization apart from slower, batch-style integration, ensuring timely and accurate data.
• Data virtualization enables faster design and rapid prototyping, leading to quicker ROI.
• Information is instantly available for reporting and analysis, accelerating decision-making.

Flexibility and simplicity

• Rapid prototyping allows faster test cycles before moving to production.
• Data sources appear in a unified interface, hiding the complexity of a heterogeneous data landscape.
• The virtual layer lets users quickly adapt business logic to changing demands.

Cost-effectiveness

• No extra infrastructure is needed as data remains in source systems, making it cheaper than traditional ETL solutions.
• Changes in data sources or front-end solutions do not require expensive restructuring.
• Data virtualization acts as middleware, integrating existing infrastructure with new applications and eliminating data silos.

Consistent and secure data governance

• A single data access point simplifies user and permission management, ensuring GDPR compliance.
• Centralized KPIs and rules ensure company-wide understanding and management of critical metrics.
• Global metadata improves data governance and understanding through data lineage and metadata catalogs.
• Real-time access to data allows quicker detection and resolution of mistakes compared to other integration approaches.

5 data virtualization design constraints

Data virtualization platforms offer many benefits over traditional data solutions. However, there are certain constraints to consider:

• Real-time access: Data virtualization accesses source data in real-time via production systems, unlike data warehouses or master data management solutions that store pre-aggregated data for faster response times.
• Historical analysis: Data virtualization cannot provide historical data analysis. A data warehouse or analytical database is typically required for this purpose.
• Data cleansing and transformation: These tasks can still be complex in the virtual layer.
• Model changes: Changes to the virtual data model can require significant effort, as they must be accepted by all-consuming applications and users.
• Query language: The goal of using a single query language for speedy responses and assembling different data models has not been fully realized in every product.

Data virtualization use cases

Virtual data mart

A data mart provides an aggregated view of data, typically extracted from a traditional data warehouse, and serves as a foundation for effective data visualization. Data virtualization simplifies the creation of a virtual data mart, offering a faster, more flexible approach.

By combining an organization’s primary data infrastructure with auxiliary data sources specific to certain data-driven business units, teams can move forward more quickly than if they had to onboard data into a traditional data warehouse.

Rapid prototyping

Modern agile businesses often experiment with new ideas and models, using data to implement initiatives and measure success. A flexible system is essential to test, adjust, and implement these ideas.

With a logical data warehouse, the data virtualization component allows for quick setup, faster iteration, and data materialization, enabling an easy transition to production. The built-in recommendation engine analyzes prototype data usage and suggests optimal storage methods for production, including automatic database index creation and other optimizations.

Organizations recognize that to make smarter decisions, delight customers, and outcompete rivals, they need to exploit their data assets more effectively.

Data virtualization vendors

• CData Virtuality is a data virtualization platform for instant data access, easy data centralization, and enterprise data governance. CData Virtuality combines two distinct technologies, data virtualization and data replication, for a high-performance architecture and flexible data delivery.
• IBM Cloud Pak for Data, formerly known as IBM Cloud Private for Data, is a data and AI platform that helps collect, organize, and analyze data using data virtualization.
• Denodo offers a data virtualization platform with an associated data catalog feature, enabling users to combine, identify, and structure existing data.
• Informatica PowerCenter is an enterprise data integration platform with features such as archiving data from older applications and impact analysis to examine structural changes before implementation.
• TIBCO’s data virtualization product includes a business data directory to assist with analyses and a built-in transformation engine for unstructured data sources.

Experience data virtualization in action with CData 

Data virtualization is transforming how businesses access and integrate their data, enabling faster insights and streamlined operations. CData Virtuality helps you simplify your data strategy, gain real-time access to all your data sources, and ensure scalability as your business grows. Get a free trial and discover how CData can help your organization unlock the full potential of your data with seamless, efficient integration.