Datenfabrik GmbH & Co. KG Releases Beta Version Of Its SSIS Component Datenfabrik.profiler

Online, November 21, 2011 (Newswire.com) - The datenfabrik GmbH & Co. KG, a Bonn-based software manufacturer of data quality components who operates internationally, is releasing the beta version of its SSIS component datenfabrik.profiler on November 21, 2011. Starting on the release date, it will be available for download as a free 30-day trial version via the company's homepage http://www.datenfabrik.com/en/datenfabrik-profiler.

datenfabrik.profiler is an extension of Microsoft's data integration platform (SQL Server Integration Services) which is used to perform data profiling and monitoring for data pools from several different data base systems. According to the company, various data base systems, such as MS-SQL-Server, Oracle and SAP are supported, among many others. Data profiling is generally performed before implementing measures for the improvement of data quality in different data integration projects. Here data from existing data sources is checked according to previously defined rules and criteria. Afterwards, the results are documented in the form of statistics and information. By performing such data quality analysis, it is possible to precisely assess the level of quality of existing data, as well as create measurable indicators for continuous monitoring.

For analyzing data, the beta version of the component already includes nine different types of rules, such as data dictionaries, regular expressions and pattern analysis. According to the producer, it will be possible to subsequently add further rules via a special plug-in system. The information and statistics gathered with these different rules are stored in a central repository and recreated, extended or put under version control with each execution. On this, Tillmann Eitelberg, head of the department for database development in the datenfabrik GmbH & Co. KG, says: "Due to this possibility of central archiving, datenfabrik.profiler enables data profiling for single and incremental updates. This makes it possible for the data management staff to access and analyse all cumulated statistics at any time."