What is Data Virtualization? Benefits, Use Cases & Tools

Data integration is a vital first step in developing any AI application. This is where data virtualization comes in to help organizations accelerate application development and deployment. By virtualizing data, teams can unlock its full potential by providing real-time AI insights for applications like predictive maintenance, fraud detection, and demand forecasting.

Virtualizing data centralizes and simplifies data management without requiring physical storage on the platform. Thanks to a virtual layer between data sources and consumers, companies can access and manage their data without replication or relocation from its original location.

How does data virtualization work, and which tools can teams use to achieve it? 

What is Data Virtualization?

Data virtualization is a method of integrating data into a data management architecture (such as a data mesh, fabric, or hub). It’s used for querying many data sources and federating query results into virtual data views, which are then consumed by applications, query or reporting tools, message-oriented middleware, or other data management infrastructure components. Maintaining virtual views in memory offers an abstraction layer over the physical data implementation, simplifying query logic.

Data virtualization technology serves as a unified, semantic data layer that unifies company data across various systems. It facilitates centralized data governance and enables data masking through the use of data masking technologies.

Applications and users can access company data through data virtualization, irrespective of its location, format, or protocol. Unlike traditional ETL or ELT solutions, which physically copy data from several sources into a target system, such as a data warehouse or data lake, data virtualization allows business users to access data where it is stored without building a physical replica along the way.

Data Virtualization vs. ETL vs. Data Federation

Data integration options include data virtualization, ETL (Extract, Transform, Load), and data federation, although they differ in how they manage data access and processing. ETL physically transports and transforms data, whereas data virtualization and federation provide virtualized views of data without requiring physical movement. Data federation is a sort of data virtualization that allows users to query various sources as if they were a single logical database.

Explore this table for a more in-depth comparison:

	ETL (Extract, Transform, Load)	Data Federation	Data Virtualization
Definition	Involves physically taking data from source systems, transforming it into a usable format, and putting it into a target system, typically a data warehouse	A sort of data virtualization in which a unified view is created by querying numerous data sources concurrently, as if they were a single database	Offers a unified picture of data from many sources without requiring physical movement or copying.
Advantages	Suitable for complicated transformations, historical analysis, and centralized, consistent data storage	Easy access to data from several sources without ETL operations, resulting in faster insights and fewer data silos	Faster data access, less redundancy, and simplified integration across several systems
Limitations	Time and resource requirements, as well as potential for data delay	Performance may be limited for sophisticated queries or huge datasets. Proper planning and coordination are key to providing a correct representation of the underlying data sources	Potential performance issues with sophisticated queries or huge datasets, as well as the need for solid infrastructure to provide real-time data access
Use Case	The process of transferring data from one system to another for analysis and reporting purposes	A sort of data virtualization that focuses on accessing numerous sources through a single logical database	Used to create a unified view of data from multiple sources without requiring physical movement

If you need a centralized, consistent data repository with complicated transformations, ETL may be the best option. If you require real-time access to data from multiple sources with little data migration, data virtualization or federation may be a better option. In some circumstances, combining these approaches may provide the ideal balance of performance, flexibility, and data consistency.

Key Concepts in Data Virtualization

Unified Data Access

Data virtualization consolidates data from multiple sources into a single view for users and applications, eliminating the need to move or duplicate data.

Abstraction of Data Sources

Data virtualization creates a uniform, virtualized picture of data from multiple sources without physically moving or reproducing it. It serves as an abstraction layer, protecting users and applications from the intricacies of the underlying data sources while letting them access and integrate data as if it were all in one place.

Real-Time Data Integration

This component allows organizations to access data in real time or near real time, enabling fast decision-making.

Flexibility and Agility in Data Management

Virtualization simplifies data management by minimizing the need for sophisticated pipelines and workflows.

Data Virtualization Architecture

The working layers of data virtualization architecture are as follows:

1. Connection Layer

This layer connects the virtualization platform to data sources. It manages both organized and unstructured data, such as databases, files, and APIs. It works with databases like MySQL, Oracle, and MongoDB and cloud storage services like S3 and Azure Blob.

The layer can also handle REST and SOAP APIs, as well as semi-structured or unstructured data such as JSON, XML, and plain files. Essentially, it creates bridges across all of the locations where your data is stored, eliminating the need to physically move or duplicate anything.

2. Abstraction Layer

The abstraction layer generates a virtual version of your data that appears clean and coherent, regardless of how chaotic or complex the sources are. Instead of displaying the raw data tables or formats, it generates virtual views.

Imagine that you have sales data in one database and customer data in another; this layer can create a virtual table that merges the two and makes them appear to be from the same source. It does not move or store data; it creates a seamless virtual representation.

3. Consumption Layer

This is the user-facing layer that allows access to the unified data. It’s intended to make it simple for tools, programs, and humans to interact with the data. The layer makes virtualized data accessible using tools and procedures that users are already familiar with.

For example, you can query the data using SQL or access it programmatically via APIs such as REST or SOAP. It also supports connectivity with tools such as Tableau, Power BI, and Excel, allowing you to use the data in dashboards, reports, and analytics.

Benefits of Data Virtualization

Cost Efficiency

Traditional data integration often entails replicating data across different systems, including data warehouses, data lakes, and application-specific storage. This raises storage requirements and calls for significant hardware investments to accommodate expanding data volumes. 

Data virtualization minimizes the need for physical duplication by providing real-time access to data straight from the source. It also minimizes data duplication redundancies by providing a unified view of data that avoids physical duplication.

Simplified Data Management

Data virtualization makes data integration and access easier by creating a single, virtualized layer that connects diverse data sources. This reduces the need for complex ETL (Extract, Transform, Load) operations, allowing for real-time data access while saving time and money.

Virtualization minimizes the complexity and time necessary to integrate new data sources, allowing businesses to respond more swiftly to changing requirements. As a result, IT teams spend less time maintaining data pipelines and integration projects, freeing up time for higher-value operations.

Lower Maintenance and Administrative Overhead

Managing complicated data systems requires a significant amount of time and resources. Traditional systems, from database management to integration software maintenance, can be expensive to run. Data virtualization simplifies this environment by dramatically lowering administrative overhead.

By lowering the number of physical databases, enterprises can reduce the effort of database managers. Data virtualization also reduces the need for costly ETL and integration software. By providing a consolidated virtual data layer, it simplifies governance and regulatory compliance, reducing audit costs.

Accelerating Time-to-Market

In the modern competitive environment, speed is key. Data virtualization’s agility allows enterprises to bring goods and solutions to market more quickly by reducing the time necessary for data integration and analytics.

Teams can access data in real time, accelerating analysis and decision-making processes. This allows entire organizations to adjust to market shifts and client needs faster, giving them a competitive advantage.

Common Use Cases for Data Virtualization

Business Intelligence and Analytics

Data virtualization provides business intelligence (BI) and analytics by delivering a uniform, real-time view of data from several sources that don’t require physical movement or duplication. This strategy simplifies data integration, improves user access, and accelerates insights, resulting in faster and more informed decision-making.

Data virtualization removes the intricacies of data sources, allowing users to focus on analysis rather than management. It provides self-service BI, allowing business users to examine data and generate reports without requiring IT assistance for each query.

Supply Chain Management

Supply Chain Management focuses on streamlining every aspect of production and delivery. The purpose is to efficiently deliver items to the final customer. Data virtualization opens the door to standardizing data from multiple sources. This facilitates collaboration among suppliers and manufacturers, as obtaining data from several vendors and partners would often be challenging. 

Cloud Migration Strategies

Data virtualization can simplify cloud migration by providing a layer of abstraction that separates applications from underlying data sources. This enables gradual data migration to the cloud, reducing disturbance and providing a “migrate as you go” strategy. 

By creating a virtualized data layer, it streamlines data access, integration, and migration across several sources, allowing for a more seamless shift to the cloud. In hybrid cloud deployments, data virtualization allows enterprises to manage and access data in both on-premises and cloud environments.

Data Integration for Mergers and Acquisitions

Data virtualization simplifies data integration during mergers and acquisitions by presenting a unified view of different data sources without needing physical data migration. This strategy enables businesses to quickly integrate data from the acquired firm into their existing systems, which speeds up the integration process and reduces the chance of project delays or failures.

Data virtualization serves as a link between disparate data silos, creating a uniform and easily available data layer for analytics, reporting, and other business processes during and after an M&A.

AI/ML Development and Experimentation

Data virtualization makes machine learning processes easier by giving a consistent, real-time view of data from several sources. This eliminates the need for extensive data migration and duplication, allowing ML models to access and process data more efficiently, resulting in faster model development and deployment.

Data virtualization offers near-real-time data access, which is critical for predictive maintenance and fraud detection. By bringing centralized control and administration of data access to the table, data virtualization also boosts security and compliance which are critical in ML applications.

Critical Capabilities of Data Virtualization

Data virtualization’s important characteristics focus on data abstraction, access simplification, and complexity elimination. It accomplishes this by logically abstracting data sources, enabling smart query acceleration, and offering enhanced semantic capabilities for data discovery and management.

Let’s dive into the details:

Capability	Description
Logical Data Abstraction	The abstraction layer enables users to access and alter data without having to understand the specifics of each data source. It transforms data into a virtualized view, providing data users with a single and consistent interface.
Smart Query Acceleration	Data virtualization improves query performance by sending requests to the most relevant data source. It uses approaches such as multi-source query optimization, parallel processing, and AI-powered acceleration to reduce query response time.
Advanced Data Semantics	Data virtualization technologies offer data catalogs to facilitate data search and exploration. These catalogs often include AI-powered suggestions and collaboration tools to improve the user experience.
Universal Connectivity and Data Services	Data virtualization offers connectors to several data sources, such as databases, data warehouses, and cloud storage. It offers data publishing via common interfaces like SQL, REST, and GraphQL APIs, allowing for easy data sharing and consumption.
Flexible Data Integration	Data virtualization enables real-time data federation, selective materialization (caching and aggregations), full replication (ETL and ELT), and streaming. This flexibility enables companies to select the best integration solution depending on their individual needs and use cases.
Unified Security and Governance	Data virtualization allows for centralized management of security and governance standards, resulting in uniform data access control and protection. Comprehensive audit logs provide visibility into data access and usage, which aids compliance and risk management.

Challenges in Data Virtualization

Performance Limitations

Data virtualization might struggle with complex data transformations and queries that need several joins, potentially resulting in performance bottlenecks, particularly for large datasets.

Its performance depends on the performance and availability of the underlying data sources. If the source systems are slow or unreliable, the virtualized data suffers as well.

Security and Compliance Concerns

Proper data security is crucial while using virtualization to avoid exposing sensitive information. Robust access controls and encryption mechanisms are key to reducing these security concerns.

Note that data virtualization’s centralized structure may result in a single point of failure. If the virtualization layer or the physical server fails, it might affect all connected computers.

Governance and Data Quality Issues

While data virtualization provides agility and access to various data sources, it also introduces substantial governance and data quality problems. Effective governance is critical for ensuring the accuracy, consistency, and security of virtualized data. 

Data quality concerns, including inconsistencies, errors, and incompleteness, can jeopardize the validity of insights gained from virtualized data.

Implementing Data Virtualization: Best Practices

Start with Clear Objectives and Specific Use Cases

Determine how data virtualization can help solve specific business problems and improve decision-making. Prioritize these use cases in your planning and concentrate on areas where data virtualization can provide the greatest benefit, such as real-time reporting or data exploration.

Understand the Data Sources

Evaluate data quality and structure before virtualizing. You should make sure that your data sources are in the proper format, structure, and quality. Mapping data relationships is another important step – it will help you understand how data from many sources relate to one another.

Establish Strong Data Governance Policies

Data governance is at the core of sustainable data virtualization programs. Make sure to use data encryption and implement mechanisms to protect critical information from unauthorized access.

Define access restrictions to ensure that data is only accessed by authorized people – and have audit and logging capabilities to monitor data access and trace data changes. Create governance policies that ensure data accuracy, consistency, and compliance. 

Invest in Robust Tools That Integrate Seamlessly

Evaluate data virtualization platforms and choose one that meets your exact requirements and works seamlessly with your existing infrastructure.

Monitor and Optimize

Monitor the virtualization layer’s performance and optimize it as needed. This will help you to quickly identify and resolve any performance bottlenecks or data quality issues.

Train Teams to Maximize Benefits

Communicate the benefits of data virtualization to all stakeholders and relevant teams. Involve IT, data scientists, and business users in the implementation process.

Data Virtualization Tools

Data Lake–Focused Solutions Enabling Virtualization

Data lake solutions are adopting data virtualization to improve data accessibility and usefulness. Data virtualization creates a conceptual data lake by providing a single, virtualized interface for accessing data from multiple systems. This reduces the need for ongoing data replication and ETL processes, resulting in lower duplication and storage costs. 

Real-time access allows users to query and evaluate data without the delays caused by batch processing. Data virtualization provides centralized governance, security, and access control, resulting in identical policies across all data sources. 

Data versioning solutions like lakeFS that operate on top of virtualized data lakes enforce data quality checks and transformations at the point of access, ensuring consistent and reliable data.

Traditional Data Virtualization Platforms

Traditional data virtualization platforms allow you to access and combine data from several sources without physically relocating or recreating it. Denodo, TIBCO Data Virtualization, Informatica, and IBM Cloud Pak for Data are among the industry leaders. These platforms establish a virtual layer that enables users to access and aggregate data from many systems, such as databases, cloud storage, and even APIs, in real time.

Modern Cloud-Native Virtualization Approaches

Modern cloud-native virtualization technologies combine the agility and adaptability of containerization with the resource efficiency and isolation of classical virtualization. Cloud-native emphasizes containerization (Docker), orchestration (Kubernetes), and microservices for faster development, deployment, and scaling.

Cloud-native virtualization leads to shorter development and deployment cycles, helps teams optimize their resource use, and allows flexibility in running both traditional and cloud-native applications on the same platform.

How lakeFS Enhances Data Virtualization

lakeFS manages version control for the data lake and creates and accesses versions using Git-like semantics. The data versioning system allows you to apply ideas to your data lake, such as:

• branching to create an isolated version of the data, 
• committing to create a reproducible point in time, 
• and merging to combine your changes in a single atomic action.

lakeFS works with structured, semi-structured and unstructured data, regardless of format, and it is compatible with various data tools and platforms. Zero-copy branching avoids the requirement for data duplication by preserving existing data without copying.

The impact of lakeFS on data virtualization centers on data quality. lakeFS improves data quality by offering powerful data versioning for data lakes, allowing teams to trace changes, isolate experiments, and maintain data consistency. This improves data integrity, lowers errors in production, and enables faster, more reliable data pipelines.

lakeFS Use Cases in Data Virtualization

Stable Inputs for AI/ML and ETL 

lakeFS improves AI/ML and ETL workflows by incorporating a comprehensive data versioning and branching mechanism that ensures reproducible and isolated environments for data and code. It provides effective testing of ETL operations and ML models against specific data snapshots, reducing data corruption and allowing for shorter development cycles.

Users can test the new ETL code against historical data snapshots and compare outputs between versions using the same input. lakeFS integrates with solutions like Databricks and Airflow for automated ETL testing in isolated environments. It supports reproducible ML experiments by versioning all components, such as data, code, and parameters.

Reproducibility in Machine Learning

Data changes make it difficult to maintain an accurate record of its current state throughout time. lakeFS provides a Git-like interface to data, allowing users to track  the present and past state of data. This makes it easy to reproduce its state at any point in time.

Reproducibility is essential for debugging and replicating ETL pipeline outputs by pinpointing the exact state of data at a specific point in time. lakeFS supports reproducible ML experiments by versioning all components, such as data, code, and parameters.

Data Rollback and Recovery

A revert operation is used to rectify critical data problems right away. Rolling back data returns it to a previous state, before the problem occurred. A rollback is used as a stopgap measure to “put out the fire” as soon as possible, while RCA (root cause analysis) is performed to determine how the problem occurred and what can be done to prevent it from happening again.

lakeFS lets you structure your data lake to make rollbacks easy to perform. This starts with taking a commit over your lakeFS repository whenever its state changes. Using the lakeFS UI or CLI, you can quickly set the current state of a branch to any historical commit, essentially conducting a rollback.

Continuous Integration/Continuous Deployment (CI/CD) for Data Lakes

It’s critical for data teams to guarantee that production data adheres to the governance standards of organizations. These might be as simple as validating file formats, doing schema checks, or completely removing PII (Personally Identifiable Information) from all of the organization’s data.

To maintain quality and reliability throughout the data lifecycle, data quality checks are essential. Teams need to execute Continuous Integration (CI) tests on the data, and only once data governance requirements are met can the data be promoted to production for business use.

lakeFS simplifies the implementation of CI/CD pipelines for data via a feature called hooks, which allows automating data checks and validations on lakeFS branches. Certain data actions, such as committing or merging, can trigger these checks.

Conclusion

The future of data virtualization looks promising, with cloud computing, IoT, and the need for faster, more agile data access all driving increased use. Expect more cloud-native integrations, improved edge computing capabilities, and increasing automation. Organizations will also prioritize data governance and security in line with growing ML application demands.