Best Data Virtualization Tools 2025

What are Data Virtualization Tools?

Data Virtualization Tools simplify and expedite access to data stored in data warehouses, databases, and files located on-premises and in the cloud. By connecting multiple data sources and centralizing data acquisition logic in a metadata layer, these tools create a single source of data for data consumers. The tools support real-time and historical data. They’re compatible with a wide range of formats and interfaces and facilitate data modifications and updates.

Data virtualization tools decouple data consumers from the data acquisition logic. This allows business intelligence (BI) tools and a variety of other applications and services to acquire data from the same metadata layer. By creating a single data source, data virtualization expands BI capabilities and streamlines the development and maintenance of apps and web services. The data these tools provide supports analytics, machine learning, artificial intelligence, and application development.

Data virtualization tools can connect data from sources like relational databases, data warehouses, data lakes, other apps, cloud data, web services, IoT data, XML files, and Excel spreadsheets. Data virtualization tools also integrate with a variety of enterprise data applications, such as Amazon Redshift, Google Big Query, Microsoft SQL, IBM DB2, Oracle, and Teradata.

Data virtualization is heavily utilized by the financial services, energy, technology, communications, and manufacturing industries and by government and healthcare agencies.

The tools are used by data architects and engineers, database administrators, developers, and business users. The latest trends for data virtualization include edge data IoT integration, regulations regarding data movement and security, and cloud data sharing.

Data Virtualization vs Data Integration

Data virtualization tools and data integration tools have significant overlap. The key difference is that data virtualization tools do not move or copy data. Instead, they create a standardized virtual interface that connects to the original data. When the original data changes, the virtual interface reflects those changes without needing to run extract, transform, and load (ETL) processes.

Data Virtualization Tools Features

Data virtualization tools commonly include the following features:

• Connect to multiple data sources
• Support for on-premises, cloud, and hybrid data sources
• Support for different data types
• Abstraction of the technical characteristics of data, such as API, query language, structure, and location
• Centralize data acquisition logic in a virtualized metadata layer
• Real-time data retrieval, delivery, and updates
• Data federation, integration, and transformation for data consumers
• Query optimization
• Data modeling and profiling
• API management
• Support for JDBC, ODBC, REST interfaces
• Advanced caching
• Admin dashboards including connection and cache monitoring
• Permission management
• Workflow management
• Quality management
• Data governance
• On-premises or cloud installation

Data Virtualization Tools Comparison

Consider the following when purchasing data virtualization tools.

Integrations: Since the entire purpose of data virtualization is to connect disparate data into a single source, proper integration is all-important. Ensure that your data virtualization tools will work with the systems you already have in place.

Performance: Data virtualization can add overhead that may impact query performance. Make sure to choose a data virtualization product with query monitoring and optimization tools.

Security: Your organization’s data security policies may be impacted by implementing data virtualization. Many solutions have ways to address these concerns, but it’s important to ensure that you choose a tool with the right data security features for your organization.

Scope of virtualization: Data virtualization tools are great for integrating multiple data sources. However, virtualizing and centralizing all of an organization’s data can create new problems. For instance, accessing operational data that is crucial to mission-critical production systems has the potential to impact their performance and integrity. Avoid deploying data virtualization tools for more data sources than necessary.

Pricing Information

Data virtualization tools don’t advertise their pricing, so you will need to contact a vendor for a quote. The scale of your virtualization needs, number of sources, amount of data, and number of queries supported all factor into subscription-based pricing. Free trials are commonly available.