The Benefits of Data Virtualization

Data virtualization may be the enterprise Holy Grail that enables better management of social media and data warehouses. IT pundit Wayne Kernochan provides some background.

Data virtualization may be the enterprise Holy Grail that enables better management of social media and data warehouses. IT pundit Wayne Kernochan provides some background.

When I set out to write this article, I thought I could assume a basic understanding of data virtualization (DV) in readers and focus on the neat new benefits to agile business intelligence from using DV to combine Big Data with the data warehouse dynamically. Then I found, reading what's out there on the 'Net, that there's still a lack of understanding of what data virtualization really is and what it does. For example, as recently as a couple of months ago, Wikipedia was unsure of the relationship between data virtualization and "Enterprise Information Integration," (EII) which it said "failed in the market."

So before I note data virtualization's upcoming benefits, I am going to ask the reader to review the definition, history and existing benefits of DV, briefly, with me. I promise: the review will help.

Data, data everywhere

The basic aim of data virtualization, whose first solutions arrived around 2001, is to allow users to query across differing data sources in real time. That means that any DV solution needs three brand spanking new (in 2001) technologies:

1.       A way to gather data from any data stores accessed by different vendors' databases or file systems or applications in real time;

2.       A global metadata repository that shows not only what data was out there, anywhere, but also the relationships between the data in various data stores;

3.       A common format across any and all data types that allows DV when it combines the data to present to the end user the relationships between the data, not just differing formats side by side.

And that has been the core of DV's value proposition. But not the whole story. Because, by definition, data virtualization aims to be (to stretch a much-abused word) "agile." That is, its ongoing value lies in its ability to keep pace with the proliferating number of data types out there in the world. A data warehouse, or a file system, achieves performance above all by refining its ability to process a particular type of data. DV piggybacks on this performance, but focuses on its own performance improvements in combining data types where the existing database has not done so. Over time, the "rich have become richer": the gap between what's stored in a data warehouse and a zettabyte's worth of a wide array of other data types stored all over the world has become ever wider, and DV continues to bridge that gap and assemble a richer and richer set of combined data and metadata.

Read the rest about data virtualization at Enterprise Apps Today.




Tags: virtualization, Data Virtualization, big data


0 Comments (click to add your comment)
Comment and Contribute

 


(Maximum characters: 1200). You have characters left.