Data virtualization: what it is, advantages and benefits

Every business needs real-time data to make quick business decisions and serve customers faster. This data is scattered across the cloud, social media platforms, operating systems and websites. Data virtualization helps eliminate data silos and optimize access to company data.

Data virtualization is a type of data integration technology that enables access to data in real time. In this article, we will learn more about it and discuss the pros and cons based on some use cases.

What is data virtualization?

Data virtualization is a method of data processing that adds an extraction layer at a logical level. This allows users to access and modify different data sets without having to worry about technical details such as the original format or location of the data.

Users can access all their data through a single interface. Large blocks of data no longer need to be physically moved; instead, pointers to the actual data are used. This makes the data easier to store and faster to access.

Advantages and disadvantages of data virtualization

Data virtualization enables faster, more flexible and better business intelligence operations. There are some advantages but also potential disadvantages associated with it.

Benefits of data virtualization

• Real-time access: Data virtualization enables real-time access and manipulation of source data through the virtual or logical layer without the need to physically move the data to a new location.
• cost: Implementing data virtualization requires fewer resources and costs than building a separate consolidated warehouse.
• Improved data management and security: Data virtualization enables administrators to enforce centralized data management and security through the unique virtual data structure. There is no need to move hardware and access levels can be controlled.
• Lower complexity: All company data is exposed through a single virtual layer, making it suitable for a wide range of users and applications.
• Self-service features: Users don't have to worry about the format or location of data to create and run the reports and analysis they need.

Disadvantages of data virtualization

• Time spent finding test results: Virtual databases can save a lot of time setting up test environments, but a lot of time is also wasted in the process. QA engineers are often busy collecting test data. A database with millions or billions of records will only shorten this time.
• High network traffic costs: The complete production files are stored on the data server. However, they are compressed to save space. The number and intensity of these processes create significant network traffic with all sorts of costs.
• Single point of failure: A single point of failure is typically the virtualization server's only access point to all data sources. All operating systems run the risk of losing their data sources if the server fails.
• No batch data support: The integration method does not support batch or bulk data transport, which may be required in various circumstances.

Possible uses of data virtualization

It consists of building a layer between the different data sources and the people who use them. There is more than one application in the business world. Let's explore some use cases:

data integration

The most common application of virtualization is probably data integration. Big data, cloud information, and social media are just a few examples of the many types of enterprise data.

Thanks to data virtualization, users no longer need to know the details of the location or format of each data type in order to access the data they need.

Rapid prototyping

Logical Data Warehouse's data virtualization component enables rapid configuration, iteration, and materialization to move data into production as needed. The integrated engine analyses how the prototype's data is used and provides storage recommendations for production, such as: B. automatic database indexing.

Companies need to make better use of their data assets to make better decisions, delight customers and outperform the competition.

Use in development operations

Most teams automate everything but data in the application development process to transform the way customers interact with applications. Data virtualization makes it easier for these teams to connect, access, and consume data that is ready for production.

It helps development teams eliminate deployment and data management constraints and reduce the resources required for computing and copying data for developers and testers.

Analysis of large amounts of data

Data virtualization is particularly well-suited to the needs of big data and analytics that rely on a variety of data sources.

Email, social media, and cell phone use are just a few examples of large amounts of data being collected that are beyond the scope of a traditional database like Oracle. That's why this solution is suitable for such a wide range of methods.

Conclusion

Data virtualization is a great way to work with data hosted on different platforms. It is a good business plan if you need easy-to-use and well-designed data visualizations for your users.

It helps you quickly get up-to-date information and collect data from numerous sources. IT can quickly deploy a new data set and iterate as customer needs change.

At QuestionPro, we provide researchers with data collection tools such as: B. our survey software – you can create a free account and start exploring.

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KEYWORDS OF THIS BLOG POST

Data virtualization | data | virtualization