This article is the fourth in our 4-part series on feeding the Datadog cloud analytic platform with different kinds of data from IRI Voracity operations. It focuses on visualizing search logs from the DarkShield unstructured data masking product (also a Voracity component) in Datadog for security analytics.
This article is third in a 4-part series on feeding the Datadog cloud analytic platform with different kinds of data from IRI Voracity operations. It focuses on visualizing Voracity-wrangled in Datadog.
This article is the second in a 4-part series on feeding the Datadog cloud analytic platform with different kinds of data from IRI Voracity operations. It focuses on preparing data in Voracity, and getting Datadog ready to receive it.
In predictive analytics, machine learning involves training a computer to evaluate data sets and create prediction models from trends it finds in the data. Machine learning builds off traditional statistics and creates larger and more advanced models faster than a person ever could.
For the last 30 or so years, the precursor to most large scale business intelligence (BI) environments has been the Enterprise Data Warehouse (EDW). A data warehouse (DW) is usually a central database (DB) for reporting, planning, and analyzing summarized, subject-matter data integrated from disparate, historical transaction sources.
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.
The concept of mashups is a simple one: combining separate, typically heterogeneous elements into a single, compound, and useful system. A stick, some rope, and a sharp rock all have their worthy independent uses; but, when combined, they become a spear to make hunting and survival easier.
This article is third in a 3-part series on using IRI products to expand functionality and improve performance in Pentaho systems. We first demonstrate how to improve sorting performance, and then introduce ways to mask production data, and create test data, in the Pentaho Data Integration (PDI) environment.