In the field of database management there is still a significant distinction between systems
that process millions of transactions and those that extract information from enormous
volumes of data in order to support business analysts. These decision support systems not
only need to operate on vast numbers of data, but also have to satisfy the enterprise requirements
of changeability, auditablity or scalability. Nowadays the remedy for these business
needs –apart from Bill Inmon’s always-deployable normalized concept– tends to be the
dimensional modeling (star schema) toolkit of Ralph Kimball. However, both approaches have
certain drawbacks which a certain Daniel Linstedt tried to eliminate –while maintaining their
respective advantages– by devising and publishing a methodology of his: the Data Vault Modeling.
After its initial theoretical success the Data Vault Modeling, however, has not gained
much popularity in practice.
Beyond the familiarization of the methodology the chief goal of this thesis was to work
out a solution by the support of which relational databases can simply and intuitively be
transformed into Data Vaults. The solution involved two tasks: firstly, a general implementation
independent methodology was established to summarize the functions which are due in
case of transforming relational structures (e.g.: converting schemas, loading data or transforming
queries). Secondly, a prototype application –based on the aforementioned methodology–
was realized using Oracle PL/SQL and Java. The application delegates the transformation
logic into a low-level server-side component thus facilitating portability and the advantages
of loose coupling. The user interface is provided by a Java application that invokes the
transformation functionality through a connector library.
The prototype application provides means of efficiently transforming either legacy systems
or new –but orthodoxically designed– databases/data warehouses into Data Vaults.
Moreover, it enables the qualitative and quantitative comparison of different types of architectures.
Therefore the application can serve as a bridge between conceptions and offers a
basis for familiarization and further analyses of the Data Vault Modeling methodology.