What is Data Virtualization

Data virtualization is a method of managing data that enables applications to retrieve and manipulate data without needing specific technical information about the data, such as how it was formatted at the source or where it is physically located. 

Data virtualization is one of the latest approaches to data integration. Earlier, ETL was used to solve these complexities. But this method retained the data in the source system, giving consumers a holistic view of all the data. While that was one issue handling Unstructured or Semi-structured data, was also a problem for ETL. 

Data virtualization is an agile, economical, and flexible alternative to the ETL method. This approach allows real-time access to the source system while keeping the data in the same location. 

Data virtualization uses the concept of data cloning; the idea behind it is to snapshot data and create a functional copy, the same as the physical copy. In a nutshell, data virtualization uses these virtual clones to allow users to gather information from diverse systems before transforming and delivering it in real time.

Benefits and Drawbacks of Data Virtualization

Data Virtualization is versatile and applicable in several domains. The following are some benefits and drawbacks of implementing data visualization as a solution.

Benefits of Data Virtualization

1. The virtual clones use minimal storage in comparison to the source data.
2. Snapshotting the source data is a very fast process that can be done in seconds.
3. Connectivity with various types of data sources is possible with data virtualization. Data virtualization can be used with structured sources such as relational database management systems, semi-structured sources such as web services, and unstructured data sources such as file documents. 
4. Simplifies data management, and reduces system workload by creating virtual clones.
5. Usually only requires a single pass data cleansing operation. Integrating external data cleaning and data quality solutions can also be supported using APIs.
6. Data Virtualization is highly adept at reading and transforming unstructured or semi-structured data. 
7. Depending on the source and type of data, data virtualization solutions have built-in mechanisms for accessing data. This provides transparency for users who are generating analytics or reports from these data sources.
8. Since users only have access to virtual clones, the source data is secure, and no unintentional changes can be made to it.
9. Data virtualization allows the user access to near real-time data, which means any downstream analytics done with the data are able to illustrate information accurately at the time of generation.

Drawbacks of Data Virtualization

1. Badly designed data virtualization platforms cannot cope with very large or unanticipated queries.
2. Setting up a data virtualization platform can be very difficult, and the initial investment costs can be high. 
3. Searching for appropriate data sources for analytics purposes, for example, can be very time-consuming.
4. Data virtualization isn’t suitable for keeping track of historical data; for such purposes, a Data Warehouse is more suitable.
5. Direct access to systems for reporting and analytics via a data virtualization platform may cause too much disruption and lead to performance deterioration.

Data Virtualization Use Cases

Real-time analytics and reporting

Data virtualization can be used to gain real-time access to systems and gather data from various sources to create sophisticated dashboards and analytics for purposes such as sales reports. These analytics improve business insight since they access real-time data, integrate it, and are able to generate intuitive infographics. 

Virtual Copies for Projects

Most development projects wind up taking more time than anticipated. By utilizing virtual clones, the complexity of the project is greatly reduced, and teams are able to use the clones to fast-track projects.

Identifying Business or Production Issues

Virtual data clones can be used to carry out Root Cause Analysis (RCA) for issues. Changes can also be implemented in these virtual copies in order to validate and ensure that they don’t have any adverse effects prior to implementing changes in the data source.

Mask volatility in Data Sources

During volatile times such as mergers or acquisitions or even when a business is trying to begin outsourcing initiatives, they may use data virtualization as an abstraction layer to mask the changes being undertaken in data sources and applications.

Test Data Management with Data Virtualization

Another important use case for data virtualization is Test Data Management(TDM). TDM involves various methods to create and manage test data which can be used for purposes such as testing applications, training, or software development. Test data management techniques are required to determine the best possible approach to oversee the use of test data. 

In a database environment where the data source is directly shared with various different teams, conflicts can arise. Additionally, directly sharing a data source also opens a door for potential security breaches. Another issue commonly faced when attempting to devise a sufficient TDM technique is an over-abundance of testing on irrelevant data, or sometimes even outdated data.

Data Virtualization offers a highly efficient solution to the issues test data management techniques aim to assess. Data Virtualization is highly adept at reading and transforming unstructured or semi-structured data. Test data is normally found in such formats making it highly suitable for use via data virtualization. Therefore conflicts and potential data pollution can be avoided by creating virtual copies of the data for different teams as needed. These teams can then work with the data through an intuitive UI, performing functions such as data masking to secure sensitive data, generate new test datasets, and use the data to perform analytics or automated testing.

To Sum it Up

Data virtualization offers substantial advantages over the conventional ETL method in terms of cost reduction and productivity gains. Data virtualization platforms provide faster data preparation, use snapshots of data that require lower disc space costs and enable the use of data from various sources for analytics or other purposes. 

Moreover, data virtualization systems can be highly sophisticated with features such as data cleaning solutions which can help the system self-manage, integrated governance, security, and the ability to roll back any changes made. Data virtualization is very diverse, with several practical applications, and businesses should make sure to explore the possible solutions it could provide for their needs.