In today’s world, data protection and disaster recovery rely heavily on virtualization. Who can forget the days before virtualization when people rushed to repair a down server and restore all the data in the hopes that everything would resume working as it had before the server went down?  The server environment was consolidated by virtualization, which altered this paradigm.

What is Data Virtualization?

Virtualization is the process of manipulating data without the need for technical knowledge of the data. Users can access, integrate, transform, and deliver datasets with unprecedented levels of cost and time efficiency because of data virtualization. Users may access data stored across the company rapidly and at a fraction of the cost and time of physical warehousing and extract, transform, and load (ETL) operations, including data housed in traditional databases, big data sources, the cloud, and IoT devices.

The data is still present, as opposed to the ETL process, and real-time access is granted to the source system for the data. In this approach, the chance of data inaccuracies is lower, and our data is safe. Virtualization is a powerful technology that may assist organizations in safeguarding their vital.

Why Data Virtualization?

It’s crucial to handle data effectively and make use of it when necessary, in today’s competitive business environment, where data demands are growing at a pace that is equal to the quantity of data you maintain. Nowadays, processing data is beyond the capabilities of conventional data integration technologies like Extract, Transform, and Load (ETL) systems and data warehouse software because organizations are gathering so many different types of data. An organization’s ability to respond quickly to evolving market conditions will depend on how flexible it is in a fast-paced corporate environment. Business development, testing, production, and release cycles become more flexible as a result of data virtualization since it enables firms to quickly access and use production-quality data.

Applications of Data Virtualization:

• Real-time analysis: To construct complex dashboards and analytics for uses like sales reporting, data virtualization may be utilized to obtain real-time access to systems and collect data from numerous sources. Since they can acquire real-time data, combine it, and produce understandable visuals, these analytics increase corporate insight.
• Virtual Replica for Projects: Most of the projects end up taking longer than expected. The complicated nature of the project may be significantly decreased by using virtual clones, and teams can use the clones to expedite tasks.
• Addressing Production or Business Issues: Root Cause Analysis, also known as RCA, can be performed on problems using virtual data clones. Before making changes to the data source, adjustments may also be made to these virtual copies to check and make sure they don’t have any negative impacts.
• Mask the Volatility of Data Sources: Data virtualization may be used as an abstraction layer to hide changes being made to data sources and applications during uncertain periods like mergers and acquisitions or even when a company is seeking to start outsourcing activities.

Recommendation:

The data is expanding at an incredible rate right now. Additionally, this data contains private user information. Organizations are unable to communicate this encrypted form of sensitive data with their development teams due to the sheer volume of the data. Also, because this data is always changing, we need a solution that would allow users to access this constantly changing data while also respecting their permission levels.

Infosys offers an in-house data privacy product called iEDPS (Infosys Enterprise Data Privacy Suite) that can give a solution to this, which is Secure Query. Data that must be masked and changes often can be subjected to Secure Query. Whereas other data privacy tools do not have this feature. In addition to Application-level rights, we can provide users with their own permissions. According to the permissions defined in secure query execution, the user can view either the original or masked value in this case.

Data on the stock exchange, for instance, is always changing. What is the best way to mask data that changes this frequently? Secure query is the response. We may disguise that specific data by utilizing a Secure query whenever new data is added or current data is modified.

Conclusion:

Data virtualization makes working with data spread across numerous computers a snap. It’s a wise business move when you want to give your users intuitive, well-designed data presentations. It gives you the ability to effectively federate data from many sources and obtain current information. IT may effortlessly deploy and duplicate a fresh data set with it as customer demands change.

About the Author:

Shivangi Agrawal is a Technology Analyst with over two years of experience in application development. She has worked on developing microservice architecture to meet the business requirements for Infosys Enterprise Data Privacy Suite (iEDPS).