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Data virtualization using JBoss and Teiid

Uniformity Drive

Article from ADMIN 26/2015

By Thorsten Scherf

JBoss Data Virtualization software applications receive a uniform interface to all data – regardless of its source.

Agile methods are increasingly used in today's IT environments, particularly for provisioning large amounts of data. Legacy access to data sources is a thing of the past. In this article, I outline the advantages of the underlying methods of JBoss Data Virtualization software, explain how to install it, and conclude with an initial data integration project.

Business Intelligence (BI) applications are responsible for analyzing, evaluating, and presenting data sets. The requirements for these applications have become more varied and complex over time. For example, users need to be able to view and analyze data in real time, rather than relying on historic data. This data is already outdated in many scenarios and no longer of any value. Additionally, users of BI applications have varying requirements and therefore sometimes need to create completely different reports on the basis of present data. The data itself is, of course, distributed across several sources, and each of these sources is a kind of isolated repository. Applications therefore need to be able to query several of these sources and do so using various interfaces. Employee data might exist in one SQL database and the employees' expense reports in Excel files. Different data sources therefore need to be enabled to process an employee expense report and store the results in a database. Depending on the desired report, this could involve a large amount of manual work.

The Limits of Proven BI Methods

In the past, such problems were solved by the extract, transform, and load (ETL) process. The relevant data were extracted from different sources, adapted, and transformed before being imported into a target database. The process, however, is quite complex, because all the data must be replicated and the transformation process is prone to error. Changes to the data models in the source systems can necessitate major adjustments to the

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