Table of contents
1.
Introduction
2.
Data Virtualization Abilities
3.
Data Virtualization Use Cases
3.1.
Data Integration
3.2.
Big Data And Predictive Analytics
3.3.
Logical Data Warehouses
3.4.
Abstraction And Decoupling
3.5.
Operational Uses
4.
Frequently asked questions
4.1.
What is data virtualization?
4.2.
Mention some data virtualization tools.
4.3.
Is data virtualization anyhow related to simple virtualization?
5.
Conclusion
Last Updated: Mar 27, 2024
Easy

Data Virtualization Use Cases

Author Rajat Agrawal
0 upvote
Career growth poll
Do you think IIT Guwahati certified course can help you in your career?

Introduction

In today's world, the Enterprise data now comes in various formats and is kept in a variety of locations. There are structured and unstructured data, such as rows and columns of data in a standard database, and formats such as logs, emails, and social media content. Databases, log files, CRM, and other programs store Big Data in various forms.

Data Virtualization is used to manage all this structured and unstructured data, which allows for retrieval and manipulation of data without knowing where it is stored or how it is formatted.

Data virtualization combines data from several sources without replicating or moving it, offering users a single virtual layer that spans numerous applications, formats, and physical locations. This means that data can be accessed more quickly and easily.

Let’s learn about Data Virtualization and its use cases in-depth.

Data Virtualization Abilities

Data virtualization is appropriate for usage with Big Data because of its abstraction and federation. It hides the intricacies of Big Data repositories, whether Hadoop or NoSQL, and makes it simple to link data from these stores with data from other parts of the organization. After all, data virtualization serves this role, and Big Data is intrinsically heterogeneous.

Data Virtualization has several abilities:-

1.) Logical Abstraction and Decoupling: Data virtualization allows heterogeneous data sources to communicate more efficiently.

2.) Cost Savings: It is less expensive to keep and manage data than duplicate it and invest resources in converting it to other formats and places.

3.) Data Governance: By centralizing administration, data governance issues can be alleviated, and rules can be applied to all data from a single location.

4.) Bridging Structured and Unstructured Data: Data virtualization can bridge the semantic gap between unstructured and structured data, making integration easier and improving data quality overall.

5.) Increases Productivity: Aside from the aforementioned data bridging, virtualization makes testing and launching data-driven programs easier because integrating data sources takes less time.

Now that we have learned data virtualization capabilities. Let’s learn about data virtualization use cases.

Data Virtualization Use Cases

Since data virtualization is essentially the process of introducing a layer of data access between heterogeneous data sources and data consumers, such as dashboards or visualization tools, it has a wide range of applications.

The following are some of the most popular use cases:-

Data Integration

Since practically every firm has data from various sources, data integration is the most typical scenario you will experience. This entails connecting an old data source, kept in a client/server configuration, with new digital systems, such as social media. You search your data using the data catalog and connections like Java DAO, SOAP, or other APIs. Even with data virtualization, the most challenging component is establishing connections.

Big Data And Predictive Analytics

Because Big Data and predictive analytics are built on diverse data sources, data virtualization works effectively here. Big Data doesn't simply come from an Oracle database; it also comes from cell phone usage, social media, and email. As a result, data virtualization lends itself to a wide range of approaches.

Logical Data Warehouses

The logical data warehouse performs similarly to a typical data warehouse with a few exceptions. For starters, unlike a data warehouse, which stores data after it has been prepared, filtered, and saved, an LDW does not store data. Data is stored at the source, which could be a standard data warehouse or something else entirely. As a result, no infrastructure is required; existing data stores are used. A good LDW federates all data sources and provides a single platform for integration via SOAP, REST, and ADO.Net.

Abstraction And Decoupling

This is the polar opposite of all of the above-mentioned unifying aspects. You may want to isolate specific data sources owing to shady sources, privacy standards, or other compliance requirements. Data virtualization allows you to protect a specific data source from those who shouldn't have access to it.

Operational Uses

Siloed data is one of the biggest challenges for call centers and customer support apps, and it's been that way for a long time. For credit cards, a bank would need a different call center than house loans. From a call center to a database manager, everyone can see the whole breadth of data repositories from a single point of access thanks to data virtualization that spans data silos.

Frequently asked questions

What is data virtualization?

Data virtualization combines data from several sources without replicating or moving it, offering users a single virtual layer that spans numerous applications, formats, and physical locations.

Mention some data virtualization tools.

DataCurrent, Denodo, Oracle data service integrator, TIBCO Data Virtualization, etc., are some of the data virtualization products.

Is data virtualization anyhow related to simple virtualization?

Data Virtualization is not anyway related to virtualization. When the phrase "virtualization" is used, it usually refers to the virtualization of server hardware. Apart from the word, there is no link between the two.

Conclusion

In this article, we have extensively discussed Data Virtualization, data virtualization capabilities, and its different use cases. If you want to learn more, check out our articles on Top 100 SQL Problems, Interview Experience,  IAM Security StandardData Warehousing Tools, and Google Deep Dream

Do upvote our blog to help other ninjas grow.

Happy Coding!

Live masterclass