Best 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...

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(1-25 of 214)

1
TIBCO Data Virtualization

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.

2
Spotfire

Spotfire, formerly known as TIBCO Spotfire, is a visual data science platform that combines visual analytics, data science, and data wrangling, so users can analyze data at-rest and at-scale to solve complex industry-specific problems.

3
SAP HANA Cloud

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…

4
Denodo

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

5
CData Connect Cloud

CData Connect Cloud provides governed access to hundreds of cloud applications, databases, and warehouses for live data consumption and analysis with any preferred tools.

6
AtScale
0 reviews

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.

7
MOSTLY AI
0 reviews

MOSTLY AI offers synthetic data for use cases ranging from AI and machine learning development to generating realistic test data. The platform boasts many database connections and features to automate data generation pipelines.

8
IBM Cloud Pak for Data

IBM Cloud Pak for Data (formerly IBM Cloud Private for Data) provides data management, data governance, and automated data discovery and classification.

18
Synthesized.io

The Synthesized.io platform, from the company of the same name in London, is optimized for unlimited synthetic volumes of high-quality test data in the Cloud, ensuring coverage of test cases for functional and non-functional requirements, while complying with regulatory data governance…

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GenRocket
0 reviews

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…

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.

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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.

What are the best data virtualization tools?

Popular data virtualization tools include: