Data Virtualization

by Alyssa Towns
Data virtualization gives users access to disparate data systems. Learn the use cases,best practices for success, and how it differs from data federation.

What is data virtualization?

Data virtualization lets users access and use data without worrying about technical details, such as the format of the data at its source or where it’s physically located. Unlike some other forms of data management, data virtualization doesn’t require replicating or storing data anywhere. Instead, users connect to datasets in real time without running the risk of mistakenly manipulating the source.

Data administrators, analysts, and engineers use data virtualization software to facilitate data usage through virtual data layers, integrate data across sources, and simplify data retrieval. 

Types of data virtualization features

Most data virtualization software systems provide a variety of capabilities and functionalities, such as the ones below.

  • Data administration: Database management, access control, and data security are all administrative features that many data virtualization software programs possess. Data administrators should have control over data privileges and accessibility through these systems.
  • Data federation: This feature enables users to access multiple autonomous data types through a single interface or data view. Data federation allows businesses to manage and organize data centers and integrate their numerous data sources into other systems.
  • Data transformation: Data virtualization software helps businesses analyze and comb through their datasets to identify trends. Data transformation features generally offer quick insights and visual representations of data in various formats.

Data virtualization use cases

Companies employ data virtualization for various use cases according to their specific needs. Common use cases include:

  • Data integration: Data virtualization is most commonly used to integrate disparate datasets across sources. Even though the data sources are in different formats, data virtualization makes it easy for data consumers to connect with the data they need without manipulating it. 
  • Big data and predictive analytics: Big data comes from different sources, including machine data, social media platforms, and transactional data. Data virtualization simplifies how users access these varying datasets from a centralized location. 
  • Self-service reporting and analytics: Data virtualization helps business users across departments reap the benefits of easy-to-use self-service reporting. Instead of trying to locate various data sources and formats, data virtualization platforms give users the data and information they need to create reports and review analytics.

Benefits of data virtualization 

Data virtualization offers many benefits to businesses and their data management, including:

  • Faster and more accurate delivery. Since users don’t have to replicate data sources to achieve their end goals, they often get what they need more quickly. Data virtualization also provides data in real time, so users can access the most recent dataset and gain more accurate results.
  • Better data protection. Data virtualization enables businesses to protect critical systems and data sources. Users can find and utilize the data they need without the risk of extracting it directly from a critical system and unintentionally changing or manipulating it.
  • Enhanced simplicity and flexibility. Data virtualization centralizes data and makes it simple and easy for business users to access. All teams, no matter how technical or non-technical, can benefit from the simple usability of data virtualization. 
  • Data-driven decisions. Businesses can take advantage of the outcomes of data virtualization to make decisions about business direction based on accurate data. 
  • Cost-effectiveness. Data virtualization is more cost-effective than other data management solutions because it doesn’t require maintenance resources and tools. Businesses often don’t need as many developers since this approach doesn’t require restructuring front-end solutions. 

Data virtualization best practices

Undertaking a data virtualization effort or implementing a new data department is challenging. Businesses should contemplate the following best practices when launching and maintaining a data virtualization practice to maximize the chances of success.

  • Establish a data governance approach: Data virtualization uses real-time data, but the sources are only accurate if someone governs the data and monitors it accordingly. Business leaders should prioritize implementing a data governance process before or alongside a data virtualization approach to ensure what they need is available, usable, secure, and honest.
  • Centralize data virtualization responsibilities. Businesses should centralize data virtualization responsibilities, so all team members know whom to ask for data assistance. Consolidating data oversight can help eliminate confusion.
  • Prioritize educating the organization about data virtualization: Business users may need help understanding its benefits upfront. Data virtualization leads should train other team members and consult with them regularly to ensure they understand the data and how it’s meeting their needs.
  • Develop a phased implementation approach: When establishing data virtualization, businesses must think about taking a phased approach because it’s a process that requires iteration. As a first step, data teams can first abstract the data sources and develop data governance policies and procedures.

Data virtualization vs. data federation 

It’s not uncommon to see data virtualization and data federation used interchangeably. However, data federation is a type of data virtualization. 

Data virtualization allows users to access disparate data across various systems without following strict data models. On the contrary, data federation uses virtual databases with strict data models so users can access distributed data types. The virtual database converts data sources into a common model in the data federation approach.

With the basics of visualization down, learn about database software and how businesses can use it to store customer data and other business details.

Alyssa Towns
AT

Alyssa Towns

Alyssa Towns works in communications and change management and is a freelance writer for G2. She mainly writes SaaS, productivity, and career-adjacent content. In her spare time, Alyssa is either enjoying a new restaurant with her husband, playing with her Bengal cats Yeti and Yowie, adventuring outdoors, or reading a book from her TBR list.

Data Virtualization Software

This list shows the top software that mention data virtualization most on G2.

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

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.

Denodo provides performance and unified access to the broadest range of enterprise, Big Data, cloud and unstructured sources.

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.

Your AI is only as good as the data that feeds it. With IBM Cloud Pak for Data, you can make your data ready for an AI and multi-cloud world and access an array of IBM Watson technologies at your fingertips. Rapidly provision services for data scientists, data engineers and developers so they can work faster than ever. Simplify hybrid data management, unified data governance and integration, data science and business analytics with a single solution.

Dremio is a data analysis software. It is self-service data platform provided that users discover, accelerate and share data at any time.

IBM App Connect is a multi-tenant, cloud-based platform for rapidly integrating cloud applications, on-premises applications and enterprise systems in a hybrid environment using a “configuration, not coding” approach.

SAP HANA Cloud is the cloud-native data foundation of SAP Business Technology Platform, it stores, processes and analyzes data in real time at petabyte scale and converges multiple data types in a single system while managing it more efficiently with integrated multitier storage.

Data Virtuality is a data integration solution that allows its users to instantly access and model data from any database and API with analysis tools.

IBM® Db2® is the database that offers enterprise-wide solutions handling high-volume workloads. It is optimized to deliver industry-leading performance while lowering costs.

Parallel Data Warehouse offers scalability to hundreds of terabytes and high performance through a massively parallel processing architecture.

Snowflake’s platform eliminates data silos and simplifies architectures, so organizations can get more value from their data. The platform is designed as a single, unified product with automations that reduce complexity and help ensure everything “just works”. To support a wide range of workloads, it’s optimized for performance at scale no matter whether someone’s working with SQL, Python, or other languages. And it’s globally connected so organizations can securely access the most relevant content across clouds and regions, with one consistent experience.

Informatica PowerCenter is an ETL tool that is used to enterprise extract, transform, and load the data from the sources. We can build enterprise data warehouses with the help of the Informatica PowerCenter. The Informatica PowerCenter produces the Informatica Crop.

Starburst provides an enterprise ready distribution and support of Presto. Starburst offers a full-featured data lake analytics platform that allows you to discover, manage, and consume the data in and around your data lake.

SAP Datasphere is an out-of-the-box enterprise-ready data warehouse that brings people and information together.

Varada offers a big data infrastructure solution for fast analytics on thousands of dimensions.

JS Charts is a JavaScript based chart generator.

Percona Server for MongoDB is a free and open-source drop-in replacement for MongoDB Community Edition. It combines all the features and benefits of MongoDB Community Edition with enterprise-class features from Percona. Built on the MongoDB Community Edition, Percona Server for MongoDB provides flexible data structure, native high availability, easy scalability, and developer-friendly syntax. It also includes an in-memory engine, hot backups, LDAP authentication, database auditing, and log redaction.

Design, build and run automation applications and services on any cloud, using pre-integrated automation technologies and low-code tools. IBM Cloud Pak™ is the latest deployment option of the IBM Automation Platform for Digital Business, available on Red Hat® OpenShift®.