# Best Data Virtualization Software

  *By [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)*

   Data virtualization software is used by teams to facilitate agile data storage, retrieval, and integration processes through the use of virtual data layers.

To qualify for inclusion in the Data Virtualization category, a product must:

- Abstract data through a virtualized layer
- Integrate data from disparate sources
- Facilitate data retrieval and manipulation





## Category Overview

**Total Products under this Category:** 38


## Trust & Credibility Stats

**Why You Can Trust G2's Software Rankings:**

- 30 Analysts and Data Experts
- 2,000+ Authentic Reviews
- 38+ Products
- Unbiased Rankings

G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.


## Best Data Virtualization Software At A Glance

- **Leader:** [SAP HANA Cloud](https://www.g2.com/products/sap-hana-cloud-2025-10-01/reviews)
- **Highest Performer:** [Accelario](https://www.g2.com/products/accelario-accelario/reviews)
- **Easiest to Use:** [Denodo](https://www.g2.com/products/denodo/reviews)
- **Top Trending:** [TIBCO Data Virtualization](https://www.g2.com/products/tibco-data-virtualization/reviews)
- **Best Free Software:** [AWS Glue](https://www.g2.com/products/aws-glue/reviews)

## Top-Rated Products (Ranked by G2 Score)
  ### 1. [SAP HANA Cloud](https://www.g2.com/products/sap-hana-cloud-2025-10-01/reviews)
  SAP HANA Cloud is a modern database-as-a-service (DBaaS) powering the next generation of intelligent data applications. SAP HANA Cloud offers a competitive edge by incorporating advanced machine learning and predictive tools grounded in modern data science. Its powerful in-memory performance safeguards efficient data processing. By securely storing vast amounts of data with its integrated multitier storage and handling various types on a single copy in its native multi-model database, SAP HANA Cloud simplifies data management and connects to other data sources. The seamless integration of these capabilities in a reliable, unified foundation makes it easier for developers to build high-demand intelligent data apps.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 509

**User Satisfaction Scores:**

- **Data Security:** 8.6/10 (Category avg: 8.8/10)
- **Orchestration:** 8.4/10 (Category avg: 8.5/10)
- **Application Performance:** 8.8/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.1/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [SAP](https://www.g2.com/sellers/sap)
- **Company Website:** https://www.sap.com/
- **Year Founded:** 1972
- **HQ Location:** Walldorf
- **Twitter:** @SAP (297,227 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/sap/ (141,341 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Consultant, SAP Consultant
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 61% Enterprise, 26% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (55 reviews)
- Easy Integrations (41 reviews)
- Integrations (40 reviews)
- Speed (39 reviews)
- Scalability (35 reviews)

**Cons:**

- Complexity (33 reviews)
- Expensive (32 reviews)
- Learning Curve (30 reviews)
- Difficult Learning (28 reviews)
- Complex Setup (20 reviews)

  ### 2. [Denodo](https://www.g2.com/products/denodo/reviews)
  Denodo is a leader in data management. The award-winning Denodo Platform is the leading logical data management platform for transforming data to trustworthy insights and outcomes for all data-related initiatives across the enterprise, including AI and self-service. Denodo's customers in all industries all over the world have delivered trusted AI-ready and business-ready data in a third of the time and with 10x better performance than with lakehouses and other mainstream data platforms alone. The Denodo Platform includes the following capabilities: - A semantic layer, with semantic search and embedded data prep in a self-service data catalog. - Unified, real-time-updated data views without expensive replication or copying of data. - Native connectors to over 200 source systems, both cloud and on-premises - An AI SDK which implements metadata-driven RAG (retrieval augmented generation) to provide trusted data to AI agents. - Query acceleration, improving lakehouse performance by 10x while also reducing compute and storage costs. - Federated enterprise-wide governance and privacy compliance. - Greater automation of common data engineering tasks, with the AI-powered Denodo Assistant. Enterprises world-wide across every major industry have used Denodo to achieve greater business self-service and agility, improve operational visibility and efficiency, optimize the performance and cost of modern data infrastructure such as Lakehouses, and ensure success of their AI initiatives. Denodo now offers two options to meet these needs: the Denodo Platform, deployable in all Clouds (AWS, Azure, GCP and Alibaba) and on-premises for full control, and Agora, our fully managed cloud service available on AWS, offering an entirely managed experience with the same rich data capabilities. Denodo provides a unique approach to data integration and management not found in any other platform. Denodo customers reported: 83% increase in business user productivity 67% reduction in time required to prepare data for AI 65% decrease in data delivery time vs. ETL 10x improvement in Lakehouse query performance compared to running queries directly resulting in an average three-year benefit of $6.8M, ROI of 408%, and payback within six months across customers.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 39

**User Satisfaction Scores:**

- **Data Security:** 8.1/10 (Category avg: 8.8/10)
- **Orchestration:** 8.3/10 (Category avg: 8.5/10)
- **Application Performance:** 8.0/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.7/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [Denodo](https://www.g2.com/sellers/denodo)
- **Year Founded:** 1999
- **HQ Location:** Palo Alto, CA
- **Twitter:** @denodo (5,548 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/32150/ (777 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Financial Services, Information Technology and Services
  - **Company Size:** 47% Enterprise, 30% Mid-Market


#### Pros & Cons

**Pros:**

- Functionality (3 reviews)
- Connectors (2 reviews)
- Data Cataloging (2 reviews)
- Data Integration (2 reviews)
- Ease of Use (2 reviews)

**Cons:**

- Expensive (2 reviews)
- Bug Issues (1 reviews)
- Bugs (1 reviews)
- Difficult Learning (1 reviews)
- Learning Curve (1 reviews)

  ### 3. [Strategy Mosaic](https://www.g2.com/products/strategy-mosaic/reviews)
  Strategy Mosaic, from Strategy (formerly MicroStrategy), is an enterprise-grade universal semantic layer solution designed to enhance the capabilities of AI and Business Intelligence (BI) within organizations. It addresses critical challenges such as data fragmentation and inconsistent metrics, which lead to untrusted AI answers, compliance risks, and runaway cloud costs. The universal semantic layer that Mosaic provides serves as a centralized repository for business definitions, hierarchies, and security rules, ensuring that all users access consistent metrics and KPIs regardless of the tools they employ. This single source of truth is actively monitored by our integrated Sentinel layer, which moves you from reactive audits to proactive, real-time governance. Sentinel provides immediate intelligence on potential data breaches, compliance risks, and cost-saving opportunities, helping you optimize cloud spend and prevent violations before they happen. Additionally, Mosaic empowers organizations to build an auditable foundation for AI. By providing a layer of rich business context and consistent, human-readable definitions, Mosaic gives AI models the deep understanding required to provide more accurate and verifiable answers. This accelerates time to insight, allows you to end vendor lock-in, and dramatically reduces the total cost of ownership (TCO) by eliminating costly data rework and optimizing data management processes. In summary, Strategy Mosaic stands out by addressing the fundamental issues of data fragmentation and governance. Its robust connectivity, centralized semantic layer, and focus on delivering trusted data make it an invaluable tool for organizations aiming to enhance their analytics capabilities and leverage AI effectively.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 15

**User Satisfaction Scores:**

- **Data Security:** 9.4/10 (Category avg: 8.8/10)
- **Orchestration:** 10.0/10 (Category avg: 8.5/10)
- **Application Performance:** 9.2/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.6/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [Strategy (formerly MicroStrategy)](https://www.g2.com/sellers/strategy-formerly-microstrategy)
- **Company Website:** https://www.strategy.com/software
- **Year Founded:** 1989
- **HQ Location:** Tysons Corner, VA
- **Twitter:** @MicroStrategy (302,648 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/strategy/ (3,404 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 53% Enterprise, 40% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (2 reviews)
- Features (2 reviews)
- Reporting (2 reviews)
- Data Analysis (1 reviews)
- Data Modeling (1 reviews)

**Cons:**

- Bugs (2 reviews)
- Bug Issues (1 reviews)
- Debugging Issues (1 reviews)
- Expensive (1 reviews)
- Learning Curve (1 reviews)

  ### 4. [FME Platform](https://www.g2.com/products/fme-platform/reviews)
  FME, made by Safe Software, is the only All-Data, Any-AI Enterprise Integration Platform connecting all your data, applications, and AI services. All Data Velocities - Whether it’s batch, event, or stream processing, FME delivers you no-code business orchestration and automation. All Data Locations - Wherever your data is - in the cloud, on-premises, or both - FME matches your data landscape with processing, increasing scalability, agility, and security. All Data Types - From databases to business systems to IoT, you name it, we connect it. As the only integration platform with support for spatial data, FME delivers new insights so you can make better decisions and stay ahead of the competition. Any AI Technology - Power your workflows with the right data at the right time regardless of modality, location or data velocity. FME powers real-time, context-aware automation, scaling AI where and how you need it.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 92

**User Satisfaction Scores:**

- **Data Security:** 8.9/10 (Category avg: 8.8/10)
- **Application Performance:** 8.1/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.7/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [Safe Software](https://www.g2.com/sellers/safe-software)
- **Company Website:** https://fme.safe.com/
- **Year Founded:** 1993
- **HQ Location:** British Columbia, Canada
- **Twitter:** @SafeSoftware (4,976 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/50027/ (343 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** GIS Analyst, GIS Technician
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 39% Mid-Market, 28% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (33 reviews)
- Data Management (19 reviews)
- Automation (18 reviews)
- Features (16 reviews)
- Data Transformation (15 reviews)

**Cons:**

- Complexity (10 reviews)
- Expensive (10 reviews)
- Learning Curve (7 reviews)
- Difficult Learning (6 reviews)
- Pricing Issues (6 reviews)

  ### 5. [Accelario](https://www.g2.com/products/accelario-accelario/reviews)
  Accelario enhances efficiency and excellence in software development with advanced AI-powered Anonymization and Database Virtualization technologies. By delivering real, compliant, and on-demand test data, our platform enables development teams to optimize workflows, cut costs, and boost productivity. Supporting faster development cycles, superior-quality releases, and strong data privacy, Accelario empowers organizations to meet the challenges of modern software innovation. Trusted by global enterprises, we offer scalable solutions that ensure seamless data provisioning, continuous compliance, and unmatched data security. Whether accelerating DevOps pipelines or improving application quality, Accelario provides the tools to innovate boldly and transform your software development process.


  **Average Rating:** 4.8/5.0
  **Total Reviews:** 30

**User Satisfaction Scores:**

- **Data Security:** 8.4/10 (Category avg: 8.8/10)
- **Orchestration:** 8.5/10 (Category avg: 8.5/10)
- **Application Performance:** 8.6/10 (Category avg: 8.6/10)
- **Ease of Use:** 9.1/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [Accelario](https://www.g2.com/sellers/accelario)
- **Year Founded:** 2017
- **HQ Location:** New York NY
- **Twitter:** @Accelario2 (36 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/accelario/ (16 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 43% Mid-Market, 40% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (8 reviews)
- Efficiency (8 reviews)
- Time-Saving (8 reviews)
- Automation (7 reviews)
- Data Management (6 reviews)

**Cons:**

- Learning Curve (6 reviews)
- Expensive (3 reviews)
- Integration Issues (2 reviews)
- Limited Customization (2 reviews)
- Complexity Management (1 reviews)

  ### 6. [Rocket Data Virtualization](https://www.g2.com/products/rocket-data-virtualization/reviews)
  Rocket Data Virtualization provides the ever-widening gap to automate the process of making mainframe data broadly accessible to developers and applications.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 11

**User Satisfaction Scores:**

- **Data Security:** 9.7/10 (Category avg: 8.8/10)
- **Orchestration:** 8.6/10 (Category avg: 8.5/10)
- **Application Performance:** 8.9/10 (Category avg: 8.6/10)
- **Ease of Use:** 9.2/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [Rocket Software](https://www.g2.com/sellers/rocket-software)
- **Year Founded:** 1990
- **HQ Location:** Waltham, MA
- **Twitter:** @Rocket (3,534 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/10127/ (4,314 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 55% Small-Business, 27% Mid-Market


  ### 7. [Informatica PowerCenter](https://www.g2.com/products/informatica-powercenter/reviews)
  Informatica PowerCenter® helps you unleash the value of your data. It offers a unique end-to-end data integration platform, with a broad set of capabilities to integrate raw, fragmented data from disparate sources, transforming it into complete, high-quality, business-ready information. As your environment grows and matures, PowerCenter scales to meet your expanding business needs, data volumes, and complexity, while providing insights to IT and the business


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 81

**User Satisfaction Scores:**

- **Data Security:** 9.2/10 (Category avg: 8.8/10)
- **Orchestration:** 8.5/10 (Category avg: 8.5/10)
- **Application Performance:** 8.8/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.1/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [Informatica](https://www.g2.com/sellers/informatica)
- **Year Founded:** 1993
- **HQ Location:** Redwood City, CA
- **Twitter:** @Informatica (99,880 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/3858/ (5,337 employees on LinkedIn®)
- **Ownership:** NYSE: INFA

**Reviewer Demographics:**
  - **Who Uses This:** Software Engineer
  - **Top Industries:** Information Technology and Services, Insurance
  - **Company Size:** 80% Enterprise, 15% Mid-Market


#### Pros & Cons

**Pros:**

- Automation (3 reviews)
- Data Integration (3 reviews)
- Ease of Use (3 reviews)
- Data Management (2 reviews)
- Drag (2 reviews)

**Cons:**

- Expensive (2 reviews)
- Integration Issues (2 reviews)
- Cloud Dependency (1 reviews)
- Complexity (1 reviews)
- Complex Management (1 reviews)

  ### 8. [AWS Glue](https://www.g2.com/products/aws-glue/reviews)
  AWS Glue is a serverless data integration service that makes it easier for analytics users to discover, prepare, move, and integrate data from multiple sources for analytics, machine learning, and application develop-ment. You can discover and connect to 70+ diverse data sources, manage your data in a centralized data catalog, and visually create, run, and monitor ETL pipelines to load data into your data lakes. You can im-mediately search and query catalogued data using Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 191

**User Satisfaction Scores:**

- **Data Security:** 9.1/10 (Category avg: 8.8/10)
- **Orchestration:** 8.9/10 (Category avg: 8.5/10)
- **Application Performance:** 9.1/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.4/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Year Founded:** 2006
- **HQ Location:** Seattle, WA
- **Twitter:** @awscloud (2,223,984 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (156,424 employees on LinkedIn®)
- **Ownership:** NASDAQ: AMZN

**Reviewer Demographics:**
  - **Who Uses This:** Data Engineer, Software Engineer
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 48% Enterprise, 29% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (6 reviews)
- Data Integration (3 reviews)
- ETL Solutions (3 reviews)
- Features (3 reviews)
- Simple (3 reviews)

**Cons:**

- Slow Performance (3 reviews)
- Debugging Difficulty (2 reviews)
- Difficult Debugging (2 reviews)
- Performance Issues (2 reviews)
- Time-Consuming (2 reviews)

  ### 9. [Oracle Virtualization](https://www.g2.com/products/oracle-virtualization/reviews)
  Oracle VM reduces operations and support costs while increasing IT efficiency and agility on premises and in the cloud.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 30

**User Satisfaction Scores:**

- **Data Security:** 8.5/10 (Category avg: 8.8/10)
- **Orchestration:** 7.9/10 (Category avg: 8.5/10)
- **Application Performance:** 8.2/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.1/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [Oracle](https://www.g2.com/sellers/oracle)
- **Year Founded:** 1977
- **HQ Location:** Austin, TX
- **Twitter:** @Oracle (827,310 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1028/ (199,301 employees on LinkedIn®)
- **Ownership:** NYSE:ORCL

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services
  - **Company Size:** 58% Enterprise, 23% Mid-Market


  ### 10. [CData Connectors](https://www.g2.com/products/cdata-connectors/reviews)
  CData Drivers & Connectors is a data connectivity platform that provides standards-based drivers and connectors for real-time access to over 300 SaaS applications, databases, APIs, and big data sources. The solution enables organizations to integrate live data from any source into their existing BI tools, analytics platforms, ETL processes, and custom applications using familiar interfaces like ODBC, JDBC, ADO.NET, and Python without requiring data replication or complex custom coding. This data integration software serves enterprises, mid-market companies, and development teams who need to connect disparate data sources for business intelligence, reporting, analytics, application development, and data warehousing initiatives. Users can access live data from popular platforms including Salesforce, SharePoint, QuickBooks, SAP, NetSuite, Snowflake, Amazon Redshift, and MongoDB through a unified SQL interface that eliminates the technical complexity of API integration. Key Features and Benefits: • Universal Data Connectivity: Access 300+ data sources through a single platform with support for major database systems, cloud applications, NoSQL databases, and web APIs, reducing integration complexity and development time • Standards-Based Integration: Native support for ODBC, JDBC, ADO.NET, Python, Excel, SSIS, and PowerShell enables seamless integration with existing tools and applications without requiring specialized technical expertise • Live Data Access: Real-time connectivity ensures users always work with current information without data movement, replication, or synchronization delays, maintaining data accuracy and reducing storage costs • High-Performance Architecture: Optimized drivers feature dynamic metadata discovery, intelligent caching, query pushdown optimization, and parallel processing capabilities that deliver enterprise-grade performance for large-scale data operations The platform processes over 2.7 billion queries monthly across 7,000+ enterprise customers and has been recognized in the 2024 Gartner Magic Quadrant for Data Integration Tools, demonstrating proven scalability and market validation for mission-critical data connectivity requirements.


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 16

**User Satisfaction Scores:**

- **Data Security:** 10.0/10 (Category avg: 8.8/10)
- **Orchestration:** 10.0/10 (Category avg: 8.5/10)
- **Application Performance:** 8.3/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.3/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [CData](https://www.g2.com/sellers/cdata)
- **Company Website:** https://cdata.com
- **Year Founded:** 2014
- **HQ Location:** Chapel Hill, NC
- **Twitter:** @cdatasoftware (2,003 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/cdatasoftware/ (496 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 31% Mid-Market, 31% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (9 reviews)
- Connectors (6 reviews)
- Easy Setup (5 reviews)
- Implementation Ease (3 reviews)
- Integrations (3 reviews)

**Cons:**

- Expensive (5 reviews)
- Connection Issues (3 reviews)
- Poor Performance (3 reviews)
- Data Inaccuracy (2 reviews)
- Large Datasets Management (2 reviews)

  ### 11. [CData Virtuality](https://www.g2.com/products/cdata-virtuality/reviews)
  CData Virtuality - Data Virtualization for Flexible Data Architectures By uniquely combining data virtualization and physical data replication, CData Virtuality provides data teams the flexibility to always choose the right method for the specific requirement. It is an enabler for Data Fabric and Data Mesh by providing the self-service capabilities and data governance features that are indispensable for these frameworks. Enterprises around the world, such as BSH, PGGM, PartnerRe or Crédit Agricole use CData Virtuality to build modern data architectures that meet today’s and tomorrow’s business requirements. Imprint: https://www.cdata.com/company/legal/impressum/ Privacy notice: https://www.cdata.com/company/legal/privacy/


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 20

**User Satisfaction Scores:**

- **Data Security:** 9.7/10 (Category avg: 8.8/10)
- **Orchestration:** 9.2/10 (Category avg: 8.5/10)
- **Application Performance:** 9.2/10 (Category avg: 8.6/10)
- **Ease of Use:** 9.0/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [CData](https://www.g2.com/sellers/cdata)
- **Year Founded:** 2014
- **HQ Location:** Chapel Hill, NC
- **Twitter:** @cdatasoftware (2,003 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/cdatasoftware/ (496 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 65% Mid-Market, 35% Small-Business


  ### 12. [CData Connect AI](https://www.g2.com/products/cdata-connect-ai/reviews)
  CData Connect AI is the first managed Model Context Protocol (MCP) platform that enables enterprises to safely connect AI assistants, agents, and automation workflows to live data across 300+ business systems. Unlike traditional approaches that copy data into warehouses, Connect AI provides real-time, semantic-rich access to data in-place—preserving metadata, relationships, and user permissions from the source. This means AI understands your business context, not just raw data. Connect AI solves the critical challenge that causes 95% of AI pilots to fail: siloed data and poor governance. With point-and-click setup, enterprise-grade security (SOC2, GDPR, ISO 27001 certified), and inherited role-based access controls, Connect AI makes AI deployments both powerful and compliant. From enabling marketers to query campaign data conversationally, to helping finance teams automate reporting, to empowering product teams to embed data connectivity in their AI features—Connect AI transforms how organizations make AI enterprise-ready.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 24

**User Satisfaction Scores:**

- **Data Security:** 9.4/10 (Category avg: 8.8/10)
- **Orchestration:** 8.8/10 (Category avg: 8.5/10)
- **Application Performance:** 9.4/10 (Category avg: 8.6/10)
- **Ease of Use:** 9.0/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [CData](https://www.g2.com/sellers/cdata)
- **Company Website:** https://cdata.com
- **Year Founded:** 2014
- **HQ Location:** Chapel Hill, NC
- **Twitter:** @cdatasoftware (2,003 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/cdatasoftware/ (496 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services
  - **Company Size:** 56% Mid-Market, 28% Small-Business


#### Pros & Cons

**Pros:**

- Connectors (3 reviews)
- User Interface (3 reviews)
- Ease of Use (2 reviews)
- Easy Integrations (2 reviews)
- Automation (1 reviews)

**Cons:**

- Poor Customer Support (2 reviews)
- Learning Curve (1 reviews)
- Performance Issues (1 reviews)
- Poor Performance (1 reviews)

  ### 13. [TIBCO Data Virtualization](https://www.g2.com/products/tibco-data-virtualization/reviews)
  An enterprise data virtualization solution that orchestrates access to multiple and varied data sources and delivers the data sets and IT-curated data services foundation for nearly any analytics solution


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 30

**User Satisfaction Scores:**

- **Data Security:** 8.3/10 (Category avg: 8.8/10)
- **Orchestration:** 8.6/10 (Category avg: 8.5/10)
- **Application Performance:** 8.5/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.2/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [Cloud Software Group](https://www.g2.com/sellers/cloud-software-group)
- **HQ Location:** Fort Lauderdale, FL
- **Twitter:** @cloudsoftware (123 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/cloudsoftwaregroup/ (9,677 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services
  - **Company Size:** 63% Enterprise, 28% Small-Business


  ### 14. [Red Hat jBoss Data Virtualization](https://www.g2.com/products/red-hat-jboss-data-virtualization/reviews)
  Red Hat JBoss Data Virtualization is a data supply and integration solution that sits in front of multiple data sources and allows them to be treated as single source, delivering the needed data in the required form at the right time to any application or user.


  **Average Rating:** 4.1/5.0
  **Total Reviews:** 16

**User Satisfaction Scores:**

- **Data Security:** 8.3/10 (Category avg: 8.8/10)
- **Orchestration:** 7.6/10 (Category avg: 8.5/10)
- **Application Performance:** 8.3/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.3/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [Red Hat](https://www.g2.com/sellers/red-hat)
- **Year Founded:** 1993
- **HQ Location:** Raleigh, NC
- **Twitter:** @RedHat (299,757 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/3545/ (19,305 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 38% Enterprise, 31% Small-Business


  ### 15. [VMware Cloud Director](https://www.g2.com/products/vmware-cloud-director/reviews)
  The VMware vCloud Suite is a complete cloud infrastructure solution that simplifies IT operations while delivering the best SLAs for all applications.


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 16

**User Satisfaction Scores:**

- **Data Security:** 8.9/10 (Category avg: 8.8/10)
- **Orchestration:** 8.3/10 (Category avg: 8.5/10)
- **Application Performance:** 8.9/10 (Category avg: 8.6/10)
- **Ease of Use:** 7.6/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [Broadcom](https://www.g2.com/sellers/broadcom-ab3091cd-4724-46a8-ac89-219d6bc8e166)
- **Year Founded:** 1991
- **HQ Location:** San Jose, CA
- **Twitter:** @broadcom (63,117 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/broadcom/ (55,707 employees on LinkedIn®)
- **Ownership:** NASDAQ: CA

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services
  - **Company Size:** 53% Mid-Market, 24% Small-Business


  ### 16. [SAS Federation Server](https://www.g2.com/products/sas-federation-server/reviews)
  Data federation tools from SAS give you the agility, accessibility and flexibility you need as part of your data virtualization strategy.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 16

**User Satisfaction Scores:**

- **Data Security:** 10.0/10 (Category avg: 8.8/10)
- **Orchestration:** 8.3/10 (Category avg: 8.5/10)
- **Application Performance:** 10.0/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.3/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [SAS Institute Inc.](https://www.g2.com/sellers/sas-institute-inc-df6dde22-a5e5-4913-8b21-4fa0c6c5c7c2)
- **Year Founded:** 1976
- **HQ Location:** Cary, NC
- **Twitter:** @SASsoftware (60,996 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1491/ (18,238 employees on LinkedIn®)
- **Phone:** 1-800-727-0025

**Reviewer Demographics:**
  - **Company Size:** 56% Enterprise, 25% Small-Business


  ### 17. [Datometry](https://www.g2.com/products/datometry/reviews)
  Replatforming with Datometry is the most cost-effective, quickest, and most risk-free process in the industry. We pride ourselves in having devised and implemented the world’s first engineering solution to a problem that has long been the bane of the entire database industry.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 30

**User Satisfaction Scores:**

- **Data Security:** 8.4/10 (Category avg: 8.8/10)
- **Orchestration:** 8.3/10 (Category avg: 8.5/10)
- **Application Performance:** 8.3/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.7/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [Datometry](https://www.g2.com/sellers/datometry)
- **Year Founded:** 2013
- **HQ Location:** Santa Clara, US
- **Twitter:** @datometry (301 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/datometry/about (4 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services
  - **Company Size:** 47% Small-Business, 40% Mid-Market


  ### 18. [Varada](https://www.g2.com/products/varada/reviews)
  Varada's data platform runs in your AWS VPC and can dramatically accelerate queries that run directly on your data lake. No need to move data or model it to get those blazing fast queries users demand. Any SQL app, BI tool or even analysts and data scientists can easily query any data source in your data lake, without the need to move data, prepare or model it in advance. \>\>How fast is “fast”? Varada is 10x-100x faster than any other data lake query engine. Data teams and users no longer need compromise on performance in order to achieve agility and fast time-to-insights. You can really have it all! You can find a Visual Benchmark against AWS Athena. \>\>How does it work? Queries perform so much faster based on Varada’s dynamic and adaptive indexing technology. Unlike partitioning-based platforms, Varada indexes any column in any table so we can fetch data extremely fast. The indexing is adaptive to the type of data and Varadad’s engine knows automatically which data to index based on a smart observability layer that continuously monitors demand. Indexing is best for complex queries that run on highly dimensional data, that would have otherwise required extensive modeling to achieve acceptable response time. We didn’t just stop at performance. We know how important budgets are. Different queries have different priorities and requirements. Admins can now easily assign budgets and priorities to each set of queries so you can say goodbye to notorious budget-busting surprises. You can also expect a 40%-60% reduction in TCO because Varada’s query engine is very light on compute resources and doesn’t require any data duplication or additional ETLs.


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 11

**User Satisfaction Scores:**

- **Data Security:** 10.0/10 (Category avg: 8.8/10)
- **Orchestration:** 9.2/10 (Category avg: 8.5/10)
- **Application Performance:** 8.3/10 (Category avg: 8.6/10)
- **Ease of Use:** 9.4/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [Starburst](https://www.g2.com/sellers/starburst)
- **Year Founded:** 2017
- **HQ Location:** Boston, MA
- **Twitter:** @starburstdata (3,461 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/starburstdata/ (525 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 45% Small-Business, 36% Mid-Market


  ### 19. [lyftrondata](https://www.g2.com/products/lyftrondata/reviews)
  Your ultimate next-gen agile, data delivery platform with modern cloud data warehouse & lake. Create and manage all of your data workloads on one platform. Boost the productivity of your data professionals and shorten your time to value in order to deliver modern and integrated data solutions swiftly from anywhere in your organization. We build the best data models around various source API’s, to ensure you will get all of your data and can query the non relational source like API/Json/XML with ANSI Sql. Move on if you data platform is not moving you forward with Lyftron govern and secure data delivery platform for your data migration and modernization needs as Lyftron eliminates traditional ETL/EDW bottlenecks with automatic data pipeline & modern next-gen cloud warehouse which makes data instantly accessible to BI user with the modern cloud compute of Spark & Snowflake. Lyftron connectors automatically convert any source into normalized, ready-to-query relational format and provide search capability on your enterprise data catalog. At Lyftron Data, we eliminate the time spent by engineers building data pipelines manually- and make data instantly accessible to analysts by providing real-time access to all your data with simple ANSI SQL. Our pre-built connectors automatically deliver data to warehouses in normalized, ready-to-query schemas and provide data governance and sensitive data encryption capability.


  **Average Rating:** 4.9/5.0
  **Total Reviews:** 131

**User Satisfaction Scores:**

- **Data Security:** 10.0/10 (Category avg: 8.8/10)
- **Orchestration:** 10.0/10 (Category avg: 8.5/10)
- **Application Performance:** 10.0/10 (Category avg: 8.6/10)
- **Ease of Use:** 9.9/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [Lyftron](https://www.g2.com/sellers/lyftron)
- **Year Founded:** 2019
- **HQ Location:** Reston, Virginia
- **Twitter:** @lyftron (22 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/35607092/ (66 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Architect, Data Analyst
  - **Top Industries:** Information Technology and Services, Health, Wellness and Fitness
  - **Company Size:** 84% Mid-Market, 11% Small-Business


  ### 20. [Querona](https://www.g2.com/products/querona/reviews)
  Querona is a virtual database that seamlessly connects any data source with Power BI, Microsoft Excel or others.


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 8

**User Satisfaction Scores:**

- **Data Security:** 9.4/10 (Category avg: 8.8/10)
- **Orchestration:** 9.2/10 (Category avg: 8.5/10)
- **Application Performance:** 8.1/10 (Category avg: 8.6/10)
- **Ease of Use:** 10.0/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [Querona](https://www.g2.com/sellers/querona)
- **HQ Location:** Warsaw, Poland
- **Twitter:** @QueronaCom (24 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/querona/ (3 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services
  - **Company Size:** 50% Enterprise, 38% Mid-Market


  ### 21. [IBM Netezza Performance Server](https://www.g2.com/products/ibm-netezza-performance-server/reviews)
  Integrates database, server, storage and analytics into a single system with petabyte scalability. Fast analytics Provides a high-performance, massively parallel system that enables you to gain insight from your data and perform analytics on very large data volumes. Smart, efficient queries Simplifies analytics by consolidating all activity in one place, where the data resides. Simplified infrastructure Easy to deploy and manage; simplifies your data warehouse and analytic infrastructure. Does not require tuning, indexing or aggregated tables and needs minimal administration. Advanced security Enhanced data security is provided through self-encrypting drives as well as support for the Kerberos authentication protocol. Integrated platform Supports thousands of users, unifying data warehouse, Hadoop and business intelligence with advanced analytics.


  **Average Rating:** 4.1/5.0
  **Total Reviews:** 68

**User Satisfaction Scores:**

- **Data Security:** 9.2/10 (Category avg: 8.8/10)
- **Orchestration:** 9.2/10 (Category avg: 8.5/10)
- **Application Performance:** 9.6/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.8/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [IBM](https://www.g2.com/sellers/ibm)
- **Year Founded:** 1911
- **HQ Location:** Armonk, NY
- **Twitter:** @IBM (709,023 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®)
- **Ownership:** SWX:IBM

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Banking
  - **Company Size:** 62% Enterprise, 27% Mid-Market


#### Pros & Cons

**Pros:**

- Speed (5 reviews)
- Performance (4 reviews)
- Ease of Use (3 reviews)
- Fast Processing (3 reviews)
- Efficiency (2 reviews)

**Cons:**

- Expensive (3 reviews)
- High Maintenance Costs (2 reviews)
- Integration Issues (1 reviews)
- Limited Customization (1 reviews)
- Slow Performance (1 reviews)

  ### 22. [Zipstack Data Productivity Cloud](https://www.g2.com/products/zipstack-data-productivity-cloud/reviews)
  Zipstack’s Data Productivity Cloud combines engines for transformation, orchestration, and security with over 250+ Connectors, a Lakehouse, and an in-place federated querying engine. With Zipstack's zero ETL approach, data analysis is ready without unnecessary complications or squandered time. Zipstack's Data Productivity Cloud also simplifies data cleansing and transformation, freeing time and resources for more critical business problems. Instead of complicating data operations by using a variety of tools, define, manage, control, and protect data products with effortless single-pane management. Streamline data management and get control of your data right now.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 7

**User Satisfaction Scores:**

- **Data Security:** 9.3/10 (Category avg: 8.8/10)
- **Orchestration:** 9.2/10 (Category avg: 8.5/10)
- **Application Performance:** 9.5/10 (Category avg: 8.6/10)
- **Ease of Use:** 9.7/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [Zipstack](https://www.g2.com/sellers/zipstack)
- **Year Founded:** 2022
- **HQ Location:** Los Altos, US
- **Twitter:** @ZipstackHQ (57 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/zipstack/ (13 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 57% Mid-Market, 29% Small-Business


  ### 23. [Quanta WebHIMS](https://www.g2.com/products/quanta-webhims/reviews)
  Parallels is a worldwide leader in virtualization and automation software that optimizes computing for consumers, businesses, and Cloud services providers across all major hardware, operating systems, and virtualization platforms.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 6

**User Satisfaction Scores:**

- **Data Security:** 7.8/10 (Category avg: 8.8/10)
- **Orchestration:** 7.8/10 (Category avg: 8.5/10)
- **Application Performance:** 7.8/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.3/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [Quanta His](https://www.g2.com/sellers/quanta-his)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 67% Small-Business, 50% Mid-Market


  ### 24. [Perforce Delphix](https://www.g2.com/products/perforce-delphix/reviews)
  Enterprises around the world choose Perforce Delphix to automate compliant data for DevOps. The Delphix DevOps Data Platform provides integrated data masking and virtualization to rapidly deploy compliant data into non-production environments. With Delphix, customers automate test data management and CI/CD, delivery compliant data for AI, and swiftly recover from downtime events, while ensuring data privacy and security. For more information, visit www.perforce.com/products/delphix


  **Average Rating:** 4.1/5.0
  **Total Reviews:** 11

**User Satisfaction Scores:**

- **Ease of Use:** 9.0/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [Perforce](https://www.g2.com/sellers/perforce)
- **Year Founded:** 1995
- **HQ Location:** Minneapolis, MN
- **Twitter:** @perforce (5,092 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/perforce/ (2,032 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 55% Enterprise, 36% Mid-Market


#### Pros & Cons

**Pros:**

- Database Management (2 reviews)
- Data Management (2 reviews)
- Data Security (2 reviews)
- Ease of Use (2 reviews)
- Features (2 reviews)

**Cons:**

- Expensive (3 reviews)
- Expensive Pricing (3 reviews)
- Complexity (2 reviews)
- Complex Setup (2 reviews)
- Integration Issues (2 reviews)

  ### 25. [IBM watsonx.data intelligence](https://www.g2.com/products/ibm-watsonx-data-intelligence/reviews)
  IBM watsonx.data intelligence revolutionizes the way organizations curate, manage, and utilize data by leveraging the power of AI to simplify data delivery across hybrid ecosystems. IBM watsonx.data intelligence is a comprehensive solution that integrates capabilities such as data governance (formerly IBM Knowledge Catalog), data lineage (formerly IBM Manta Data Lineage), data sharing, and data quality management. It empowers organizations to discover, trust, and access meaningful data, providing consumers with reliable data products. Explore Demo Library - https://www.ibm.com/products/watsonx-data-intelligence/demo-library Start your free trial - https://dataplatform.cloud.ibm.com/registration/stepone?context=df&apps=all&uucid=1227cc9e37cb9292&preselect\_region=true


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 24

**User Satisfaction Scores:**

- **Data Security:** 9.2/10 (Category avg: 8.8/10)
- **Orchestration:** 8.8/10 (Category avg: 8.5/10)
- **Application Performance:** 9.6/10 (Category avg: 8.6/10)
- **Ease of Use:** 8.4/10 (Category avg: 8.7/10)


**Seller Details:**

- **Seller:** [IBM](https://www.g2.com/sellers/ibm)
- **Year Founded:** 1911
- **HQ Location:** Armonk, NY
- **Twitter:** @IBM (709,023 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®)
- **Ownership:** SWX:IBM

**Reviewer Demographics:**
  - **Company Size:** 38% Small-Business, 34% Enterprise


#### Pros & Cons

**Pros:**

- Automation (3 reviews)
- Data Lineage (3 reviews)
- Data Quality (2 reviews)
- Ease of Use (2 reviews)
- Efficiency (2 reviews)

**Cons:**

- Complex Implementation (3 reviews)
- Complexity (2 reviews)
- Expensive (2 reviews)
- Expertise Required (2 reviews)
- Extra Costs (2 reviews)



## Parent Category

[Analytics Tools & Software](https://www.g2.com/categories/analytics-tools-software)



## Related Categories

- [ETL Tools](https://www.g2.com/categories/etl-tools)
- [Big Data Integration Platforms](https://www.g2.com/categories/big-data-integration-platforms)
- [Machine Learning Data Catalog Software](https://www.g2.com/categories/machine-learning-data-catalog)



---

## Buyer Guide

### What You Should Know About Data Virtualization Software

### What is Data Virtualization Software?

Data virtualization provides agile data storage, retrieval, and integration. It accomplishes this through data layers, which serve as an abstraction of said data, allowing one to access and understand data in a simple and streamlined manner. With this software in place, businesses can access and modify diverse data through a single view. ** **

Data is only valuable if it is accessible. A perennial problem that occurs at organizations, especially larger ones, is that business functions and departments can get siloed. In these cases, company data is not easily accessible across departments, oftentimes leading to different data sources for the same sets of data. In addition, those within a specific department (marketing, for example) might not communicate with another department (finance, for example) and as a result, thereof will maintain the same data in different systems.

### What are the Common Features of Data Virtualization Software?

The following are some core features within data virtualization software that can help users in administrating, federating, and transforming their data:

**Data administration:** This software helps users make sense of and manage their data. As such, administrative features, such as database management, access control, and data security are musts. Data is only valuable if it can be accessed and understood so it is key that users can use data virtualization to manage different types of databases and integration methods. 

In addition, it is not only about the “what” (e.g., data types and sources), but also the “who” (i.e., who can access the data). Data virtualization tools must give administrators control over data-related privileges and accessibility. Finally, it must allow users to secure access to data and provide additional support for security practices like IP whitelisting, attack mitigation, and data encryption.

**Data federation:** Data federation refers to the ability to map data or metadata from multiple autonomous databases into a single (i.e., federated) database or data view. With data federation, businesses can begin to manage and organize storage, networks, and data centers, as well as integrate this data into various systems and applications.

**Data transformation:** Data need not and should not remain stagnant, stuck in databases and merely glanced at from time to time. Instead, it is important to analyze it, combining different datasets and discovering trends across them. Data virtualization software can help with this through data modeling and data visualization. The former assists in structuring data in a manner that allows extracting insights quickly and accurately and the latter provides the ability to represent data in a variety of graphic formats.

### What are the Benefits of Data Virtualization Software?

**Profitability:** Data virtualization can help to consolidate data and provide a bird’s eye view of a business' data. As a result, these platforms can assist in rooting out and removing duplicate data records and ensuring that data is consistent and clean. Dirty data can be extraordinarily costly, both in terms of the costs involved in cleaning as well as the costs involved in cleaning up messes that come about as a result of inconsistent data.

For example, a company without any form of data consolidation might have a metaphorical brick wall between the finance and marketing departments. If the marketing team, based on their datasets and sources, believes that the business is succeeding, they may pour exorbitant amounts of money into their campaigns. However, if they had a proper view of company data, they might realize that things were not as great as they thought. The overspend from the marketing team, in this case, could have been forestalled with data virtualization tools, helping them better understand their data across teams and functions.

**Productivity:** As is the case with other data tools, such as self-service analytics, efficiency can be significantly boosted with data virtualization software. Historically, data access and analysis was the purview of specialized individuals and teams, such as IT. As a result, others who had an interest in analyzing or even accessing this data were forced to wait in line and get it handed to them by the data gatekeepers. This was not a quick and efficient solution and was costly as well, both in terms of the need for specialized workers, as well as the fact that by the time the data was presented it might be stale and outdated. 

**Scalability:** As an agile solution, data virtualization can easily scale as a business. In addition, it can be deployed across on-premises, cloud, or hybrid infrastructure.

### Who Uses Data Virtualization Software?

**Database administrators:** Those who are in charge of storing and organizing data will typically be using or evaluating a host of different software offerings and categories. First, they will typically focus on data storage solutions such as [database software](https://www.g2.com/categories/database-software). Concurrently or thereafter, they should consider data virtualization technology that can help them develop a robust data storage solution, helping their colleagues gain access to the company’s data.

**Data analyst:** Data analysts work with a variety of data sources and resources, often needing to access various systems to extract data. With data virtualization software, they get a logical data extraction layer, which makes their work easier. Now, they need not move around data and can instead use pointers to data blocks in order to conduct their analysis.

**Data engineer:** Similar to database administrators, data engineers focus on the consolidation and integration of data. They aid other team members, such as analysts. With their focus on the data within databases, as opposed to data itself, data engineers can benefit greatly from virtualization tools which can help them reduce issues with company data.

### What are the Alternatives to Data Virtualization Software?

Alternatives to data virtualization software can replace this type of software, either partially or completely:

[Data replication software](https://www.g2.com/categories/data-replication) **:** As opposed to data virtualization software which serves as a layer that connects disparate data sources, data replication software helps companies store data in more than one location to improve both availability and accessibility. Both software types can reduce the workload on databases (for example, transactional databases) where performance is key. 

[Data fabric software](https://www.g2.com/categories/data-fabric): Businesses focused on the integration of data can look to data fabric software, which is a unified data platform that enables organizations to integrate their data and data management processes. This software offers benefits such as the ability to explore and extract value from any form of data regardless of location by connecting stores of structured and unstructured data. It provides centralized access via a single, unified view of an organization's data that inherits access and governance restrictions.

#### Software Related to Data Virtualization Software

Related solutions that can be used together with data virtualization software include:

[Database software](https://www.g2.com/categories/database-software) **:** In order to use data virtualization tools, there must be data in the first place, which is frequently stored in repositories, such as databases, including relational, noSQL, and nonnative database management systems. Before looking to adopt a layer on top of one’s data it is important to have a firm understanding of and strategy for managing the data.

[Analytics platforms](https://www.g2.com/categories/analytics-platforms) **:** Data virtualization provides the ability to analyze data without needing direct access to the original source data. As such, data is ready to be analyzed and examined with tools such as analytics platforms, which provide a toolset for businesses to absorb, organize, discover, and analyze data. This helps reveal actionable insights that can help improve decision making and inform business strategy.

[DataOps platforms](https://www.g2.com/categories/dataops-platform) **:** Although data virtualization can assist in a host of data-related tasks, it often does not provide a holistic end-to-end solution for data operations. For this task, DataOps platforms can help control the entire workflow and related processes and ensure data-driven decisions are being made; cycle times are reduced significantly and users are empowered with a single point of access to manage the data. Companies can leverage DataOps platforms to derive on-demand insights for successful business decisions.

### Challenges with Data Virtualization Software

Software solutions can come with their own set of challenges. For data virtualization, it is critical that those who interact with, share, and analyze company data adopt the solution. Without adoption, business users risk accessing old and outdated data, or not being able to access data at all.

**User adoption:** It is not always easy to transform a business into a data-driven company. Particularly at more established companies that have done things the same way for years, it is not simple to force analytics tools upon employees, especially if there are ways for them to avoid it. If there are other options, such as spreadsheets or existing tools that employees can use instead of analytics software, they will most likely go that route. However, if managers and leaders ensure that analytics tools are a necessity in an employee’s day to day, then adoption rates will increase.

**Data organization:** Big data solutions are only as good as the data that they consume. To get the most of the tool, that data needs to be organized. This means that databases should be set up correctly and integrated properly. This may require building a data warehouse, which stores data from a variety of applications and databases in a central location. Businesses may need to purchase a dedicated data preparation software as well to ensure that data is joined and clean for the virtualization solution to consume in the right way. This often requires a skilled data analyst, IT employee, or an external consultant to help ensure data quality is at its finest for easy analysis.

**Data security:** Companies must consider security options to ensure the right users see the correct data, to guarantee strict data security. Effective analytics solutions should offer security options that enable administrators to assign verified users different levels of access to the platform, based on their security clearance or level of seniority.

### Which Companies Should Buy Data Virtualization Software?

Businesses from across industries can benefit from this technology. 

**Health care:** Within health care, a large amount of data is produced, such as patient records, clinical trial data, and more. In addition, as the process of drug discovery is particularly costly and takes a significant amount of time, health care organizations are using data virtualization software to speed up the process, using data from past trials, research papers, and more. It should be noted that data privacy concerns that arise in a health care context will still be relevant when deploying these solutions.

With data virtualization they are able to better access their data, thus helping health care organizations innovate effectively and efficiently. Sometimes, this technology is coupled with [synthetic data software](https://www.g2.com/categories/synthetic-data), which allows organizations to share and use the synthetic data without compliance concerns or exposing personal data.

**Retail:** In retail, especially e-commerce, personalization is important. Top retailers are recognizing the importance of data virtualization in order to access customer-related data from vast and disparate systems. With the proper software in place, these businesses can begin to get their data in order, and port this data into data science and machine learning platforms, as well as analytics platforms.

**Finance:** The use of data in financial services can yield significant gains, such as for banks, which can use it for everything from processing credit score related data to distributing identification data. With this software, data teams can access and process company data and deploy it to both internal and external applications.

### How to Buy Data Virtualization Software

#### Requirements Gathering (RFI/RFP) for Data Virtualization Software

When assessing tools, buyers must keep in mind that as the company and its data scale, it may be necessary to reevaluate software options down the line. Therefore, when possible it is best to consider solutions that are scalable and offer different options or tiers depending on the amount of data and usage. Also, one should be sure to consider the heterogeneous data sources at their organization, to ensure that the product integrates with them.

In addition, it is important to consider the use case. If a business is considering an operational use case, a less full-featured traditional platform might suffice. However, if one is looking to use the software for analytics workloads, which can be more varied data wise, it might be wise to consider more robust solutions that can support autonomous performance management.

#### Compare Data Virtualization Software Products

**Create a long list**

To evaluate the software, buyers can start by jotting down all relevant data sources, systems, and data uses. With this at hand, it will be easier to assess whether or not they are supported by a given product. Businesses must take note of whether or not a seller supports various data types, such as file-based, relational, API-based, etc. Also, considering the development environment is crucial—whether or not the product allows for one to design virtual data views or semantic models and if they support web-based design environments, for example.

**Create a short list**

With a matrix of the business’ data ecosystem and requirements in comparison to the capabilities of the products, which can be facilitated by G2’s verified features, a business can determine where the greatest amount of overlap is. The ideal is for there to be complete overlap (i.e., the software can support all that the business is looking to accomplish). If there is no complete overlap, it is recommended to try to find a solution that is the closest fit and is within budget.

**Conduct demos**

Trying before buying is critical. Buyers must test out data virtualization products and see how it looks and feels. One should note down how fast it works, whether data queries are working as expected, and more. It is also important to ask questions and request features if they do not already have them.

#### Selection of Data Virtualization Software

**Choose a selection team**

Multiple stakeholders should be involved in the purchasing process, including those who interact with the business data, as well as data analysts and database administrators who are tasked with data organization and deriving insight from data. These individuals will all have different perspectives and will provide useful insight into the buying process. 

**Negotiation**

As with any software category, the price is often flexible and should be questioned. Buyers may mention other prices and offerings in order to get a fair price. Negotiations can happen around factors such as contract length, the number of users, and more. It is recommended to dig into the impact of these factors in order to get a fair price.

**Final decision**

In larger organizations, the final decision would likely be made by the chief information officer (CIO). In smaller organizations, it may be the chief technology officer (CTO), or even the data analytics team, depending on the use case.

### Data Virtualization Software Trends

**Cloud computing**

With the ability to store data in remote servers and easily access them, businesses can focus less on building infrastructure and more on their data, both in terms of how to derive insight from it, as well as to ensure its quality. With the move to the cloud, businesses have easier access to their data, but also more places their data can be. This makes data management even more important.

**Volume, velocity, and variety of data**

As previously mentioned, data is being produced at a rapid rate. In addition, the data types are not all of one flavor. Individual businesses might be producing a range of data types, from sensor data and IoT devices to event logs and clickstreams. As such, the tools needed to process and distribute this data need to be able to handle this load in a way that is scalable, cost efficient, and effective. Advances in AI techniques, such as machine learning, are helping to make this more manageable.