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Top 103 Data Virtualization Software SaaS Companies in May 2026

As of May 2026, there are 103 SaaS companies in Data Virtualization Software. They have combined revenues of $1.3B and employ 6.9K people. They have raised $1.5B and serve 2.6K customers combined.

Data Virtualization Software enables organizations to effectively access, manage, and integrate data across diverse sources without the need for physical data movement. This technology provides a unified view of data from multiple systems while improving data governance and accessibility. Its primary use cases include real-time data access for analytics, reporting, and decision-making processes. Common features of data virtualization solutions include data abstraction, integration capabilities, centralized data management, and user-friendly interfaces for data queries. Buyer personas typically consist of IT professionals, data analysts, and business intelligence teams looking to improve data accessibility and streamline complex workflows across various platforms. These tools are invaluable in environments where managing data proliferation and ensuring data integrity are crucial to business operations.

Companies
103
Revenue
$1.3B
Funding
$1.5B
Employees
6.9K

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Top Data Virtualization Software Companies

Showing 10 of 11 companies ranked by annual revenue.

1
Aleph

New York, New York, United States

One source of truth for financial data.

Revenue
$10M
Customers
-
Year founded
2020
Funding
-
Team size
41
Growth
-
2
TimeXtender

Aarhus, Middle Jutland, Denmark

Our Why TimeXtender purpose is to empower the world with data, mind and heart. We do this for one simple reason: because time matters. Our goal is to free up the time, resources and energy of entire organizations so they can be used for purposeful growth, innovation and breakthroughs. Our How What makes our business model unique is that we are 100% channel driven, with a business to human approach. We serve 3300+ customers in 95 countries, but we do not sell software. We build solutions. TimeXtender is successfully distributed and implemented by an ecosystem of 200+ partners. Our What TimeXtender is the holistic solution for data integration. TimeXtender provides all the features you need to build a future-proof data infrastructure capable of ingesting, transforming, modeling, and delivering clean, reliable data in the fastest, most efficient way possible - all within a single, low-code user interface. Working at TimeXtender - Imagine working for a company with a global presence that embraces and empowers diversity. - Imagine a culture where you can make your own decisions. All day, every day. - Imagine being encouraged to prioritize silent moments throughout your day, to practice deep work, and to breathe. - Imagine a company that asks you to be curious, to engage in possibility. - Imagine a culture that focuses on output, not on hours worked. - Imagine working from anywhere, asynchronously, while remaining part of multidisciplinary project teams. - Imagine a virtual HQ, in the cloud. - Imagine building long-lasting relationships, both personal and professional. - Imagine a company with a flat hierarchy where "I hear you" becomes "I am listening to you” This is TimeXtender. This is how we operate. To learn more about TimeXtender, visit: timextender.com and to join our team, visit: timextender.com/careers. We are looking forward to meeting you.

Revenue
$9.6M
Customers
-
Year founded
2006
Funding
-
Team size
87
Growth
-
3
Cube Dev

San Francisco, California, United States

Cube helps organizations modernize how they deliver, consume, and automate data and analytics across teams, tools, and AI agents by bringing consistency, context, and trust to the next generation of data experiences. Cube Cloud is a leading universal semantic layer platform, providing a single source of truth for both humans and Cube D3’s agentic analytics. Any data source can be unified, governed, optimized, and integrated with any data application: AI, BI, spreadsheets, and embedded analytics. Cube is installed on 90,000 servers and used by more than 5 million users. Customers include 20% of the Fortune 1000. Based in San Francisco, Cube is backed by Decibel, Bain Capital Ventures, Eniac Ventures, 645 Ventures, Databricks Ventures, and Betaworks. To learn more, visit cube.dev.

Revenue
$7.9M
Customers
-
Year founded
2019
Funding
-
Team size
72
Growth
-
4
DQLabs

Pasadena, California, United States

DQLabs is the Modern Data Quality Platform enabling organizations to deliver reliable and accurate data for better business outcomes. With an automation-first approach and self-learning capabilities, the DQLabs platform harnesses the combined power of Data Observability, Augmented Data Quality and Data Discovery to enable data producers, consumers, and leaders to turn data into action faster, easier, and more collaboratively.

Revenue
$7.8M
Customers
-
Year founded
2020
Funding
-
Team size
71
Growth
-
5
Uni B Solutions

Chicago, Illinois, United States

Provider of a platform for data storage and processing. The company offers a cloud-based platform UNiTY that helps to access real-time insight to any information needed without storing client's data.

Revenue
$7.8M
Customers
-
Year founded
2012
Funding
-
Team size
3
Growth
2777.09%
6
Leapfin

United States

Unified Financial Data Platform

Revenue
$6.7M
Customers
150
Year founded
2015
Funding
$19.7M
Team size
30
Growth
33.16%
7
Bobsled

Los Angeles, California, United States

Bobsled is a cross-cloud data sharing platform that makes it painless to share data between any data lake or warehouse. We enable product and data teams to share data products directly into a customer or partner’s preferred analytical environment without ever leaving their own.

Revenue
$6.3M
Customers
-
Year founded
2021
Funding
-
Team size
57
Growth
-
8
CloverDX

Prague, Czech Republic

The CloverDX Data Integration Platform helps boost productivity and trust in data and processes by focusing on automation and robustness of data pipelines. It’s a single platform that covers the needs of the IT teams as well as providing a self-service interface to business users, covering the entire lifecycle of data from ingestion and processing to delivery and consumption.

Revenue
$6.1M
Customers
-
Year founded
2002
Funding
-
Team size
55
Growth
-
9
Adaptive

Aliso Viejo, California, United States

Information Supply Chains need Integration & Assurance - Driven by increasing demands of compliance, regulatory pressures and governance? - Concerned your BI Reports might be unreliable or reporting cycles take too long? - Seeking unambiguous shared meaning around data, business terms and metrics? We *can* help you. Talk to Adaptive to learn more: [email protected] Our points of view: http://blog.adaptive.com

Revenue
$5.9M
Customers
-
Year founded
1997
Funding
-
Team size
54
Growth
-
10
Datafiniti

Austin, Texas, United States

There are billions of web sites on the Internet. Those web sites contain trillions of points of information. All of that information is potential data. Data on businesses, products, homes, people, and much more. All of that data can be used for a wide variety of business applications like lead generation, pricing intelligence, competitive analysis, and much more. The problem is that web data is very hard to access for businesses. Businesses must go through a complicated and expensive process of acquiring, cleaning, bundling, and distributing web data before they can consume it. Datafiniti simplifies all of these steps into a single step of accessing our API. Datafiniti's promise is to take the entire Internet, and using our proprietary technology, transform it into a single database that all businesses can use to access the web data they need. In other words, Datafiniti is instant access to web data.

Revenue
$5.6M
Customers
-
Year founded
2011
Funding
-
Team size
8
Growth
-

Inclusion Criteria

- Must provide a virtualized data layer for abstraction and integration of data from multiple sources. - Should enable real-time access to data for analytics and reporting. - Must support centralized data governance and management functionalities. - Should have user-friendly interfaces for data querying and analysis. - Not limited to traditional data integration; must also facilitate seamless access to live data.