Data Virtualization Tools

Data Virtualization Tools Overview

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.

Top Rated Data Virtualization Products

TrustRadius Top Rated for 2022

These products won a Top Rated award for having excellent customer satisfaction ratings. The list is based purely on reviews; there is no paid placement, and analyst opinions do not influence the rankings. Read more about the Top Rated criteria.

Data Virtualization Products

(1-25 of 35) Sorted by Most Reviews

The list of products below is based purely on reviews (sorted from most to least). There is no paid placement and analyst opinions do not influence their rankings. Here is our Promise to Buyers to ensure information on our site is reliable, useful, and worthy of your trust.

SAP HANA Cloud
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Top Rated
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SAP HANA is an application that uses in-memory database technology to process very large amounts of real-time data from relational databases, both SAP and non-SAP, in a very short time. The in-memory computing engine allows HANA to process data stored in RAM as opposed to reading…

TIBCO Data Virtualization
Customer Verified
Top Rated
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TIBCO Data Virtualization is an enterprise data virtualization solution that orchestrates access to multiple and varied data sources and delivers the datasets and IT-curated data services foundation for nearly any solution.

Delphix

Delphix, headquartered in Redwood City, provides test data management for DevOps. Businesses need to transform application delivery but struggle to balance speed with data security and compliance. The Delphix DevOps Data Platform automates data security, while rapidly deploying test…

Denodo

Denodo is the eponymous data integration platform from the global company headquartered in Silicon Valley.

Oracle Data Service Integrator

Oracle Data Service Integrator provides companies the ability to develop and manage federated data services for accessing single views of disparate information. Oracle Data Service Integrator is standards based, declarative, and enables re-usability of data services. For more information…

CONNX

CONNX, from Software AG (acquired in 2016), is a mainframe integration solution that allows users to access and integrate mainframe data, relational data, and cloud data wherever it resides and however it is structured, without altering core systems.

Presto

Presto is an open source SQL query engine designed to run queries on data stored in Hadoop or in traditional databases. Teradata supported development of Presto followed the acquisition of Hadapt and Revelytix.

IBM Db2 Big SQL

IBM offers Db2 Big SQL, an enterprise grade hybrid ANSI-compliant SQL on Hadoop engine, delivering massively parallel processing (MPP) and advanced data query. Big SQL offers a single database connection or query for disparate sources such as HDFS, RDMS, NoSQL databases, object stores…

Oracle Big Data SQL Cloud Service

The Oracle Big Data Cloud Service is a PaaS supporting data scientists with secured and encrypted Hadoop clusters, running a diverse set of workloads from Hadoop-only workloads (ETL, Spark, Hive etc.) to interactive, all-encompassing interactive SQL queries using SQLon-Hadoop tools…

AtScale

AtScale, headquartered in San Mateo, offers intelligent data virtualization which they state provides live, secured and governed access to Big Data across disparate systems wherever it resides—and without disruption.

Starburst Enterprise

Starburst Enterprise is a fully supported, production-tested and enterprise-grade distribution of open source Trino (formerly Presto® SQL). It aims to improve performance and security while making it easy to deploy, connect, and manage a Trino environment. Through connecting to any…

Keenlog Analytics

Keenlog Analytics provides intelligence to the SMB business and supports decision-makers with the necessary data visibility to control and take actions to meet their logistics’ financial and market goals. The vendor states the product includes the following: • More…

ChaosSearch

ChaosSearch, in Boston, is a log analytics solution aims to provide enterprises with data lakes that turn cloud object storage into analytics engines. ChaosSearch features a stateless architecture that separates storage from compute, and data is stored in Amazon S3. It is accessible…

NavigatorCRE

NavigatorCRE is a patented data visualization platform built for the Commercial Real Estate market.

Datometry Hyper-Q

Datometry Hyper-Q is a SaaS platform that lets applications originally written for a specific database run natively on a cloud data warehouse. Hyper-Q enables enterprise to replatform to public cloud without a highly time-consuming, costly, and risk-laden database migration.

VividCharts

For users exporting data out of ServiceNow to get the calculation, aesthetic control, and reporting experience (slide decks and printing) desired:The vendor states teams spend on average 1/2 an FTE / month/process doing this. As soon as the data leaves the platform it's unreliable.…

Fraxses

Fraxses provides customers with access to data on demand, delivering insights via a solution that enables a data mesh or data fabric architecture. The Fraxses (meta)data platform boasts a decentralized architecture. While Fraxses fully supports traditional data integration processes,…

Starburst Galaxy

Starburst Galaxy provides fast access to data stored on AWS S3, Microsoft Azure Data Lake Storage (ADLS) and Google Cloud Storage (GCS) along with open table formats like Iceberg and Delta Lake to reduce time to insight while lowering infrastructure costs. Its Great Lakes connectivity…

TCS MasterCraft DataPlus

TCS MasterCraft™ DataPlus is an integrated data management software designed to enable trustworthy and privacy-safe data for enterprises, supporting data governance initiatives, data privacy management, and data quality.

Promethium Data Navigation System

A collaborative data and analytics solution built on top of Promethium's Data Fabric. With it, there is no need for endless handoffs, tool switching and copying data. Data, analytics and business teams can collaborate with this data and analytics solution that makes it possible…

GenRocket

GenRocket is a provider of synthetic test data headquartered in Ojai, working closely with IT services companies and enterprise customers to reduce cycle times and increase the quality of software development and testing caused by a lack of test data. GenRocket boasts users among…

Informatica Cloud Test Data Management

Informatica's Cloud Test Data Management solution helps users discover, create, and subset test data; visualize test data coverage; and protect data to improve Salesforce development.

Trino

Trino (formerly known as Presto SQL) is an open-source distributed SQL query engine for big data analytics that helps to explore a data universe. Trino is presented as a highly parallel and distributed query engine, that is built from the ground up for efficient, low latency analytics.…

SAS Federation Server

SAS Federation Server is a data integration service.

Learn More About Data Virtualization Tools

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.

Data Virtualization Tools Best Of Awards

The following Data Virtualization Tools offer award-winning customer relationships, feature sets, and value for price. Learn more about our Best Of Awards methodology here.

Best Data Virtualization Tools

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Frequently Asked Questions

What do data virtualization tools do?

Data virtualization tools connect multiple data sources, centralize data retrieval logic, transform data, and deliver data to data consumers. By creating a single view of data, these tools facilitate data access for BI tools, applications, and web services.

What are the benefits of using data virtualization tools?

By eliminating the need for data consumers to know the location or configuration of data, data virtualization tools reduce complexity, minimize data redundancy, help enforce consistency, streamline application development and maintenance, and lower costs. Their flexibility facilitates faster business intelligence gathering, analytics, and reporting.