Data Relations allow you to define logical relationships between disparate data sources that share a common key or keys. The implementation of this technology is in a dimensional modeling, or "star schema" format, which matches fact and dimension files based on keys.
This approach is similar to technologies implemented in most RDBMS (relational database management system) applications, but the difference is Arbutus directly accesses the original files, without importing. When the keys in two files do not match precisely, you can employ virtual columns to dynamically transform and harmonize the keys.
When the host system data already contains native key information, Arbutus will automatically utilize this to optimize the Data Relations processing. For QSAM or other flat files, where native keys do not exist, Arbutus can create custom indices to optimize performance.
Any of the many file types that Arbutus can access can be directly related, in any combination, allowing for unlimited flexibility in combining the information from different applications or programming languages. Using this technology, disparate data sources may be easily combined.
Since Data Relations can be defined in any combination, you can model your data architecture regardless of its complexity. Both “Star Schema” and “Snowflake Schema” modeling are supported; therefore, any complexity of model can be implemented. As the fact tables are processed, the related dimension tables are automatically synchronized.
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