The traditional or enterprise data warehouse (EDW) has been at the center of data’s transformation to business intelligence (BI) for years. An EDW involves a centralized data repository (traditionally, a relational database) from which data marts and reports are built.
Update Q3’2019: Subsequent to the development of the IRI Voracity Add-On for Splunk described below, there is now also a Splunkbase-registered IRI Voracity App for Splunk available for Seamless Data Preparation, Indexing, and Visualization…
After our first examples of external unstructured data preparation and PII data masking for Splunk generated interest in these capabilities, IRI wanted to develop a direct integration from the Splunk user interface (UI).
Linear regression is a staple data analysis function for financial, economic, research, and many other disciplines, that helps discover new data correlations. Users of the IRI Voracity platform can now simultaneously process big data from any number of sources and present customized trend lines to help business users make predictions.
Note: This example demonstrates a more direct method of using IRI FieldShield or IRI Voracity to statically mask PII within structured MongoDB collections. Our older how-to-article on indirect data masking of MongoDB through export/mask/import from 2015 is here, and a newer method through MongoDB’s native driver support in CoSort v10 from 2018 is here.
In ancient times, the ability to process and analyze information was invaluable. Greek knowledge of astronomy gave rise to the Antikythera Mechanism, an analog computer with sophisticated bronze gears that predicted astronomical events like lunar phases and eclipses.