Data virtualization is an essential part of any good data management strategy. It can help to improve data quality, data governance, and data performance while making it easier for businesses to access and analyze data from a variety of sources. In this article, we’ll examine the basics of data virtualization and what it can do for your business. Keep reading to learn more.
What is data virtualization?
Data virtualization is the process of creating a logical, unified view of data that is not tied to any physical location or format. Useful features of data virtualization tools include the ability to access and manage data no matter where it resides, whether it’s on-premises or in the cloud.
Data virtualization tools allow you to break free from the physical limitations of your data infrastructure and improve the performance and agility of your business intelligence (BI) and analytics applications. Data virtualization also allows for the aggregation and de-duplication of data from multiple data sources, which can improve performance and reduce data storage requirements.
How does data virtualization work?
To understand how data virtualization works, let’s take a look at an example. Imagine you have an e-commerce website that sells products from three different suppliers: ABC, DEF, and GHI. The website stores product information in a database table called “products.” The table has columns for the product ID, supplier ID, name, description, price, and quantity on hand. If you wanted to create a report that showed how many products each supplier had in stock, you would need to query the “products” table three times—once for each supplier. This would be slow and inefficient because it would require your application to retrieve data from three different servers.
With data virtualization, however, you can create a single view of all product information regardless of where it is stored. This makes reporting much faster and easier because your application only needs to access one server instead of three.
What are the benefits of data virtualization?
The benefits of data virtualization are vast and include:
- Improved agility: Data virtualization makes it possible to provision new applications more quickly since there is no need to wait for new copies of data to be created. In addition, changes made to the logical model are automatically reflected in all applications that use it.
- Increased performance: Because data virtualization eliminates the need to physically move data around, response times are typically much faster than with traditional methods.
- Greater flexibility: The logical model created by data virtualization can be used to access any type of data, including both structured and unstructured information. It can also accommodate different formats and sources, including both on-premises and cloud-based systems.
- Reduced complexity: By consolidating multiple sources of information into a single logical model, data virtualization makes life easier for IT staff who must manage complex BI and analytics environments.
What should you consider when choosing a data virtualization platform?
When considering a data virtualization platform, you should first evaluate your business and technical needs. What type of data do you need to virtualize? What are your performance requirements? How elaborate is your data architecture?
Once you know what needs to be done, you can start looking for a data virtualization platform that meets those needs. The platform should include features like data federation (the ability to combine data from multiple sources), self-service access (so users can easily find and use the information they need), and support for big data workloads (so you can handle large volumes of data).
The final consideration is cost. Data virtualization platforms can be expensive, so it’s important to make sure you understand all the costs associated with deploying and using the platform.
Overall, data virtualization is an essential tool that can help improve data quality, performance, and agility. It can also help organizations reduce costs and complexity.
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