Data Relations

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 is similar to technologies implemented in most RDBMS applications, but the difference is Arbutus directly accesses the original files, without importing. Where the keys in two files do not match exactly, you can employ virtual columns to dynamically transform and harmonize the keys.

Where 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 indexes 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 are able to 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 files are processed, the related dimension files are automatically synchronized