Data Virtualization: What It is, Work and Software

data virtualization

Enterprises are looking beyond traditional data integration systems like Extract Transform Load, (ETL) or Data warehouse tools to manage large amounts of data from a growing number of sources. This is a complete guide on data virtualization for businesses.

Businesses are using data virtualization technology to meet growing data demand. This includes faster provisioning of new data and allowing clients to access their data via self-service. It’s proving to be extremely helpful for data consumers, and IT and technical teams.

Data Virtualization is a mature technology

Data virtualization is an established technology that companies use as part of their data integration strategy. According to MarketsandMarkets, the market for data virtualization is expected to reach USD 1.58 Billion in 2017. It is expected to grow to USD 4.12 billion by 2022 at a Compound Annual Growth Rate (CAGR of 21.1%) during the forecast period (2017 – 2022).

Data Virtualization Technology creates a Logical Extract Layer

Data virtualization technology is a layer of data management that creates a logical extraction layer. It allows users to view data in any format or heterogeneous source ( data warehouse or data lake ), in a standardized manner.

Users of the data don’t have to worry about the technical aspects, such as the location and structure of the data, its type, and storage structure, or the interface with the original source of data storage.

Additionally, these data are consumed via virtual views by applications, query/reporting software, message-oriented middleware, or other components of the data management infrastructure.

Also read: Data Anonymization: What it is, Techniques and Practices

What is Data Virtualization and How Does it Work in an Enterprise?

Data virtualization allows enterprises to access the data they need quickly and easily. Implementing data virtualization is a three-step process.

  • Connect Data: virtualization allows you to connect to a variety of data sources such as databases, data warehouses, and cloud applications.
  • Combine Data: virtualization transforms and combines information from any format into business views and insights.
  • Deliver Data: virtualization allows enterprises to access and deliver real-time data via dashboards, portals, and mobile apps.

Data virtualization technology allows different data sources to be combined in one user interface. However, the core of the technology is the virtual layer or semantic layer. It allows business and data users to arrange their data in multiple virtual schemas and view it in any format.

Through the virtual layer, users can access all unification data from multiple systems. This creates a single consolidated source of data. This information is secure and compliant with all industry requirements.

This virtualized data can be easily enhanced by users to prepare it for analysis, reporting, and automation.

Why do you need to data virtualize?

These are the factors that drive data virtualization’s increasing importance:

  • Meet Data Demands:  As organizations continue to analyze and use self-service analytical tools, the data requirements of businesses, data analysts, scientists, and engineers might become overwhelming. These findings help businesses make better decisions and delight their customers. Data virtualization makes it possible to see all of your data from one central location. This allows you to complete analytics much faster than before.
  • Manages Data Complexity and Volume: With the quest for rapid expansion, there has been an increase in unconnected physical databases as well as complex data within businesses. Data virtualization is the fastest way to combine them all for analytics. It is becoming more difficult to maintain a physical warehouse of data because of the rapid pace at which data is generated. Data virtualization, a newer method for transferring data from multiple locations, is also an option.
  • Provides Data Agility: Although giving business users self-service options may be a priority for some, enterprises strive to find the right balance between security and business agility. Data virtualization allows all enterprise data to be accessible through one virtual layer, making it available for different users and use cases. Data virtualization technology also includes prototyping capabilities that allow users to quickly test their strategy before they deploy it on a larger scale.
  • Secure Governance: With increasing data volume and complexity, compliance, asset protection, and risk mitigation are becoming more important aspects of every data-management strategy.

Data virtualization creates access rules that determine who has access to which data. This makes data more secure. It also facilitates security management, data governance, and performance monitoring by providing a central point of access to all information within the company.

Most Popular Data Virtualization Tools

Companies have been collecting data at multiple sources and destinations to create data warehouses, data lakes, or data lakes in order to consolidate the data for analysis and decision-making.

As we have discussed, data integration can be time-consuming and costly due to the increasing volume and variety of data. Many businesses turn to data virtualization software to view, access, and analyze data, without having to worry about its lifecycle. These are some of the most popular tools you should consider:


TIBCO Software is a well-known company for its data and analytics software. However, it also offers increasing integration options. TIBCO Data Virtualization, for example, allows you to access multiple data sources. The tool also includes centralized metadata management and an orchestrated data layer. Advanced query engines are available to aid in data delivery on-demand.

The studio design tool, service interface, and business directory are just a few of the key features that allow users to search and select virtualized business data from a self-service directory. Users can then use their favorite analytics tools to analyze the results. Web Services Description Language allows abstracted data to be made available in TIBCO as a data service. Users are also protected by the built-in security and governance.

Also read: What is Visual Analytics? Benefits and Difference

2. K2View

K2View is a prominent vendor in the market. Dynamic Data Virtualization technology allows for agile data integration. It removes the difficulties of accessing data from different underlying formats and data sources. It can ingest data from any source, combine it using a semantic layer, store it in physical or digital memory, process it, and finally make it available to data analysts as well as consuming applications.

This tool makes it possible to access real data by using a logical abstraction layer known as data product schema. This schema unites all fields and tables to create a single business entity.

It makes it easy to store or virtualize data. Businesses can opt to keep data they don’t need, rather than virtualizing it. It allows data access using any method, including SQL or web service APIs. Data delivery (“pushing”) to data users via data streaming and messaging protocols is also possible.

3. Denodo

Denodo provides enterprise-grade virtualization capabilities and an intuitive interface. It also includes a data catalog that simplifies data discovery and searches. You can use this tool on-premises or in the cloud or in a hybrid environment.

The query optimization feature is a key capability that improves query performance while reducing response times. It also offers integrated data governance solutions to enterprises that are concerned about data compliance and protection.

This tool contains an active data catalog for semantic search, data governance, AI-powered smart query acceleration, automated Cloud Infrastructure Management for multi-cloud, hybrid deployments, and embedded data preparation capabilities to enable self-service, well-governed, secure analytics.

Denodo also offers unified enterprise data access and business intelligence as well as single-view apps.

Conclusion — Data virtualization

Businesses continue to employ various data management tools due to the complexity of their corporate operations. Data architecture is becoming more complex.

Data virtualization can be used as a middleware to allow companies to manage their data across hybrid infrastructures (cloud, on-premises, or cloud). It is very simple to set up. It allows real-time synchronization between disparate data sources, without the need for data replication, and lowers infrastructure costs.

Data virtualization software’s extensive analytics, design, development, and design features will allow your data engineering team to create clear data views. Data virtualization software allows your data engineering team to create clear and concise data views by using rich analytics, design, and development features.

A thorough evaluation of the technological capabilities is necessary to determine which data virtualization tool or solution is best.

Written by
Albert Lukmanov

Albert Lukmanov is a Junior Content Writer at The Next Trends. He covers all known and unknown facts related to security and research information and puts them together to create trending articles on websites.

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