Data virtualization tools enjoyed a moment in the IT sun in the early 2010s, then receded into the shadows as other data management technologies -- Hadoop and NoSQL databases, for example -- grabbed the attention of users. But virtualization software is on the upswing again, according to Gartner.
In a November 2018 report on the data virtualization market, Gartner said its surveys showed that more than 35% of organizations now use the technology in production deployments. Virtualization has become "a real option for data integration" at more companies, a group of Gartner analysts wrote in the report. They said that's partly because data virtualization vendors have eased earlier adoption inhibitors by improving integration performance and expanding connectivity to data sources.
As organizations augment data warehouses with more-expansive data lakes that feed advanced analytics, there also are practical reasons to give data virtualization another look, noted Mark Beyer, one of the Gartner analysts. "You can't change the data warehouse every time a data scientist comes up with a new data model," Beyer said in an interview.
This handbook looks at the current state of data virtualization and its potential applications. First, we detail advice on how to deploy and manage data virtualization tools. Next, Beyer offers more insight on suitable uses in a Q&A. We close with a case study on a data virtualization project at Indiana University.