Table of contents
1.
Introduction
2.
Data Virtualization Architecture
3.
Data Virtualization Capabilities
4.
Use Cases of Data Virtualization
4.1.
Data Integration
4.2.
Logical Data Warehouse
4.3.
Big Data and Predictive Analysis
4.4.
Operational Uses
4.5.
Abstraction and Decoupling
5.
FAQs
6.
Conclusion
Last Updated: Mar 27, 2024
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Data Virtualization Capabilities

Author soham Medewar
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Introduction

Before going onto the capabilities of data virtualization, let us briefly discuss the concept of data virtualization. Enterprise data in the current day comes in a variety of forms and is kept in a variety of locations. There are structured and unstructured data, which includes rows and columns of data in a standard database as well as data in forms such as logs, email, and social media material. Big Data is saved in databases, log files, CRM, SaaS, and other apps in various ways.

So, how can you get a handle on your dispersed data and manage it in all of its various forms? You employ data virtualization, which is an umbrella phrase for any solution to master data management that allows you to get and manipulate data without knowing where it is kept or how it is formatted.

Data virtualization combines data from several sources without replicating or relocating the data, providing users with a single virtual layer that spans numerous applications, formats, and physical locations. This implies that data can be accessed more quickly and easily.

Data Virtualization Architecture

Data virtualization solutions must be dynamic in order to react to changing organizational needs. New data sources will be added on a regular basis, and some will be withdrawn. As additional sources are added, the danger of complexity and delayed scalability increases. Furthermore, you may have overlapping code, which adds extra complexity. Keep the following in mind to avoid all of this:

  • Build your apps in layers to separate business logic and transformation components.
  • Maintain tight criteria for reusability, naming, and layer separation.
  • Cisco Data Virtualization, TIBCO Data Virtualization, PowerDesigner, and Oracle Data Service Integrator are examples of data virtualization modelling tools.
  • Involve the data architecture, data governance, and data security teams from the beginning to ensure complete regulatory compliance with the data connections.
  • Determine who is responsible for what aspects of the data virtualization platform.

Data Virtualization Capabilities

Data virtualization is ideal for use with Big Data because of its abstraction and federation. It hides the complexities of Big Data stores, whether Hadoop or NoSQL, it makes it simple to integrate data from these stores with data from other parts of the organization. After all, data virtualization serves this purpose, and Big Data is inherently heterogeneous.

  • Cost savings: Storing and maintaining data is less expensive than replicating and changing it to other forms and places.
  • Logical abstraction and decoupling: As heterogeneous data sources can now interlink more easily through data virtualization.
  • Data governance: By centralizing administration, data governance issues can be alleviated, and rules can be applied to all data from a single location.
  • Bridging unstructured and structured data: Data virtualization helps in bridging the semantic differences between structured and unstructured data, making integration easier and improving data quality across the board.
  • Increased productivity: In addition to the aforementioned data bridging, virtualization makes it easier to test and deliver data-driven products because linking data sources takes less time.

Use Cases of Data Virtualization

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 usage cases:

Data Integration

Practically every firm has data from a variety of sources, this is the most typical scenario you will experience. This entails connecting an old data source, which is kept in a client/server configuration, with new digital systems, such as social media. You search your data using the data catalogue and connections like Java DAO, ODBC, SOAP, or other APIs. Even with data virtualization, the most difficult component is going to be establishing connections.

Logical Data Warehouse

With a few exceptions, the logical data warehouse performs similarly to a typical data warehouse. 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, Odata, SharePoint, and ADO.Net.

Big Data and Predictive Analysis

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.

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 for house loans, for example. Everyone from a call center to a database manager can see the whole breadth of data repositories from a single point of access.

Abstraction and Decoupling

This is the complete antithesis of all of the above-mentioned unifying aspects. You may want to isolate certain 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.

FAQs

1. What is data virtualization, and why is it important?

Data virtualization is a data access platform that brings together diverse data sources to generate a single, consumable version of the data set. While the data stays in source systems, it provides a unified, abstracted, structured, and encapsulated view of the data originating from similar or heterogeneous data sources.

 

2. What is data virtualization Tibco?

TIBCO Data Virtualization is a corporate data virtualization system that curates access to a variety of data sources and generates standard, federated views, which serve as the data basis for almost any analytics solution. With maturing analytic ecosystems, you can meet expanding data needs.
 

3. What is the difference between data federation and data virtualization?

Data federation systems are often limited to linking relational data stores, but data virtualization broadens connectivity to encompass any RDBMS, data appliance, NoSQL, Web Services, SaaS, and corporate applications.

Conclusion

In this article, we have discussed the following topics:

  • Data virtualization
  • The architecture of data virtualization
  • Capabilities of data virtualization
  • Use cases of data virtualization

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