IRI Voracity is an affordable data management platform that streamlines information architectures and helps enterprises leverage the intrinsic value of data without the cost or complexity of multiple tools.
This demonstration shows how to use the IRI Workbench to create an aggregation job using sums. Workbench is used to create the job script in the SortCL language.
This video is a demonstration of using IRI Workbench to apply format preserving encryption (FPE) and pseudonymization to select fields in a data file to protect personally identifiable information (PII) and protected health information (PHI).
This demonstration shows how to use the Quick Stats Function in IRI Workbench and our data management platform, Voracity. The Quick Stats Function can be used in any IRI job script to auto-generate a linear regression analysis report in pdf format.
This demonstration shows how to set up a sort job for CoSort using the IRI Workbench. The sort is accomplished using the SortCL language. This video takes a CSV input file, shows how to define the sort keys and options, and demonstrates how to define the targets for output.
This demonstration shows how to execute an existing workflow in IRI Voracity using the built-in job scheduler in its Eclipse GUI, IRI Workbench. The scheduler launches any jobs you’ve queued in the run configuration dialog one or more times.
This demonstration shows how to create a left outer join where the match is based on one field and we use the ONLY parameter to discard matches.
IRI RowGen automatically builds and populates massive database, file, and report targets with structurally and referentially correct test data that’s also fully customizable. Enjoy the same simple 4GL or free Eclipse GUI to: stress-test ETL environments, outsource development, and benchmark new systems.
This demonstration shows how to create an inner join job for CoSort using the IRI Workbench. The join is accomplished using the SortCL tool in CoSort and the Workbench creates the job script using the SortCL language.
BI and analytic tools cannot handle the volume, variety, velocity, and veracity challenges of big data. Today there are many ways to prepare (franchise) that data into the subsets that visualization platforms can ingest, but each typically involves trade-offs between performance, price, and capability.