Data Masking/Protection
The identity fabrication rule is a deterministic but non-reversible data masking rule that can help you create realistic test PII for a whole record from just one original value. Read More
Data Transformation
We started this series of articles by talking about test design automation and the need to introduce automation throughout your testing processes. In this blog, we come full circle to talk, once again, about testing. Read More
Test Data
Abstract: This article demonstrates how to synthesize realistic data to populate a full database schema with referential integrity in one operation via the IRI RowGen New Set File and New Database Test Data Job wizards in the IRI Workbench GUI for RowGen, built on Eclipse
RowGen builds rows of data in flat files, database tables, and reports, through either random generation of column values in specified data types, ranges, and distributions, or random selection of data from lookup tables or external “set files.” Read More
Test Data
Automating where you can, when you can, is the basis of DevOps testing and test data automation. Allowing IRI job script executions which mask or synthesize test data to be handled in the CI/CD pipeline supports this pursuit. Read More
Data Masking/Protection
IRI Workbench contains a section of field-level Data Generation Rules for use in IRI RowGen test data synthesis, FieldShield data masking, and other SortCL-compatible tasks or Voracity (ETL) work flows. Read More
Data Masking/Protection
Once a database exceeds a certain size, it becomes expensive — and risky from a security perspective — to provide full-size copies for development, testing, and training. Read More
Test Data
XML is a popular file format because of its value in data interchange. For this reason, XML test data generation can be an important part of the Software Development Life Cycle (SDLC), particularly for those applications which need to process XML data. Read More
IRI Workbench
There are a variety of testing requirements for any data warehouse and database — and especially dual platforms like Teradata — where ETL and BI prototypes, application stress testing, and performance benchmarking are essential. Read More
Test Data
The value of good test data to DBAs is well known:
“Testing of database-intensive applications has unique challenges that stem from hidden dependencies, subtle differences in data semantics, target database schemes, and implicit business rules. Read More
Data Quality (DQ)
This article is part of a 4-step series introduced here. Navigation between articles is below.
Step 3: Test Data Generation & ProvisioningIn prior steps outlined in this series, you have determined the purpose and properties of the data, and who will produce and consume it. Read More