Data Masking/Protection
Usually static data masking is performed on production data at rest so it is stored safely, or when replicated to non-production environments for testing or development purposes.
Big Data
An operational data store (or “ODS”) is another paradigm for integrating enterprise data that is relatively simpler than a data warehouse (DW).
Data Masking/Protection
There are times when it is necessary to test with or share data that has elements of personally identifiable information (PII). To comply with data privacy laws and prevent a data breach, you may need to provide data that reflects, and sometimes imparts, critical information, but still protects the PII.
Business Intelligence (BI)
IRI Workbench not only has several ways to create jobs, but also several ways to execute them.
This article focuses on IRI Workbench execution options for scripts based on the SortCL program language, which covers IRI Voracity ETL, CDC, SDC, pivoting and subsetting jobs, as well as its constituent product jobs; i.e.,
ETL
Just as production data processing tools like IRI CoSort must handle big data in NoSQL DB environments, so too must a big test data generation tool like IRI RowGen.
Test Data
Unlike the New DB Test Data Job wizard for creating multiple, related test tables, the New Test Data Job wizard in the IRI Workbench GUI for RowGen generates individual test sets.
Big Data
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.
Test Data
This article explains how to use IRI RowGen to create test files for prototyping COBOL applications running on, or off, the mainframe.
RowGen generates synthetic test data for file, database, and custom report targets.
Test Data
One of the ways IRI RowGen builds realistic test data is through the formation and population of custom field values, such as phone numbers. In this article, we explain how to use the Compound Data Value (CDV) wizard in the RowGen GUI to build a set file containing real-looking, US phone numbers based on the North American Numbering Plan (NANP).
Big Data
Introduction: This example demonstrates an older method of using IRI RowGen to generate and populate large or complex collection prototypes for testing or system capacity using flat files.