Real-time data is now required by all organizations to make instant business decisions and bring value to customers faster. But this data is all over the place: It lives in the cloud, on social media platforms, operational systems, and websites, to name a few. Not to mention that additional sources are constantly being added through new initiatives like big data analytics, cloud-first, and legacy app modernization. To break data silos and speed up access to all enterprise information, organizations can opt for an advanced data integration technique known as data virtualization.
What is Data Virtualization?
Data virtualization is a method for handling data that involves adding a layer of extraction on the logical level. As a result, users can access and alter disparate data sets without worrying about technical details like the data’s original format or storage location.
Users can get to all of their data through a single interface. It eliminates the need to move large data blocks physically; instead, it uses pointers to the real data. This makes it easier to store data and faster to get to it.
Why virtualize data?
The speed of innovation and ability to adapt to rapidly changing market trends rests on the agility of your release cycle and the ability to quickly diagnose, triage, and fix errors. Data virtualization is the critical lever used by forward-thinking enterprises to provision production-quality data to dev and test environments on demand or via APIs.
Virtual data copies are fully readable/writeable and can be provisioned or torn down in just minutes, eliminating development’s reliance on slow serial ticketing systems and DBA involvement for initial data delivery as well as data refreshes after destructive testing.
Data virtualization technology facilitates data delivery across all phases of application development, including testing, release, and production fix. Traditionally, IT organizations rely on a request-fulfill model, in which developers and testers often find their requests queuing behind others. Because it takes significant time and effort to create a copy of test data, it can take days, or even weeks to provision or refresh data for a test environment. This creates massive wait states in the software delivery life cycle, slowing the pace of application delivery.
To keep pace with a faster release cadence, dev and test teams are forced to work with a stale copy of data because refreshing test data takes too long. This can result in missed test cases and ultimately data-related defects escaping into production.
How data virtualization works
Essentially, data virtualization software is middleware that allows data stored in different types of data models to be integrated virtually. This type of platform allows authorized consumers to access an organization’s entire range of data from a single point of access without knowing (or caring) whether the data resides in a glass house mainframe, on-premises in a data warehouse, or a data lake in the cloud.
Because data virtualization software platforms view data sources in such an agnostic manner, they have a wide range of use cases. For example, the centralized management aspect can be used to support data governance initiatives or make it easier to test and deploy data-driven business analytics apps.
Data virtualization software can also play a role in managing who can access certain data sources and who is not. Perhaps one of the most important reasons for deploying data virtualization software, however, is to support business objectives that require stakeholders to view a single source of truth (SSOT) in the most cost-efficient manner possible.
- Speed: Data virtualization enables data consumers to access data, wherever it resides (including traditional databases, the cloud, or IoT systems) in seconds.
- Efficiency: It doesn’t replicate data, so enterprises can save on governance and hardware while enhancing the utilization of server and storage resources.
- Cost savings: Data virtualization software requires fewer resources and costs a lot less than building a separate repository for consolidating and storing data.
- Security and governance: It enables a centralized approach to data security and governance, ensuring that all data is consistent, protected, and high-quality.
- Access: It allows for a self-service approach to accessing data, enabling quick access to data by any authorized data consumer
- Analytics: Data virtualization lets business users apply visualized, predictive, and streaming analytics across many different data sources.
Drawbacks of Data Virtualization
- Badly designed data virtualization platforms cannot cope with very large or unanticipated queries.
- Setting up a data virtualization platform can be very difficult, and the initial investment costs can be high.
- Searching for appropriate data sources for analytics purposes, for example, can be very time-consuming.
- Data virtualization isn’t suitable for keeping track of historical data; for such purposes, a data warehouse is more suitable.
- Direct access to systems for reporting and analytics via a data virtualization platform may cause too much disruption and lead to performance deterioration.
What are some data virtualization use cases?
Data virtualization supports 4 key use cases:
Data consumers need access to data that is spread out across disparate data sources. With data virtualization, data consumers gain a holistic view of this data, regardless of its format or location.
Business domains require data analytics and business intelligence to support decision-making. Data virtualization gives business users on-demand access to data through one centralized, virtual layer.
Although many elements of application development are automated, data is not. Using data virtualization, DataOps teams can eliminate bottlenecks in data provisioning by giving users direct access to high-quality data, and the ability to collaborate cross-functionally.
Backup and production support
If a production issue occurs, development teams can create complete virtual data environments in which they can identify the cause. They can also validate that any changes made will not lead to unanticipated regressions.
Data Virtualization: The best way to access and manage data across your business
Being able to allow easy, rapid, and secure data access to your team whenever and wherever they need is paramount if you want to run efficient and productive business operations. With data virtualization, you can achieve just that.
Thanks to its centralized and real-time access to all types of data from several different sources, data virtualization allows business users to cut costs, simplify processes, and run better and more timely analytics.