Using the RowGen Test Data Job Wizard
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. Read More
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. Read More
IRI RowGen software creates test data you can customize to meet specific needs. It supports the formats and techniques that make your test sets as realistic as you want them to be. Read More
IRI CoSort continues to be a low-cost way to accelerate Informatica ETL via pushdown optimization, and IRI RowGen can generate safe, referentially correct test data for any EDW. Read More
IRI has completed its first Software Development Kit (SDK) for RowGen that Java programmers can call to generate test data dynamically in applications or across Hadoop nodes. Read More
In addition to running IRI software from the command line, in batch scripts, DBMS schedulers like Oracle’s, or through third-party workflow automation suites like Stonebranch Universal Controller, the IRI Workbench development environment now has a built-in job scheduler. Read More
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
During the design of IRI Voracity workflows in the IRI Workbench (Eclipse) GUI, you can preview the results of one or more transforms before saving or running the project. Read More
Note: This article showcases the migration of a relational database (RDB) model to star schema using the Eclipse IDE for Voracity (and its included products), IRI Workbench, following an introduction to both architectures. Read More
IRI RowGen users can generate structurally and referentially correct synthetic test data for an entire database in a single operation. The test data reflects production characteristics (such as values ranges and frequencies) normally encountered in database or ETL operations, but does not require access to, or the masking of, real data. Read More
In data warehouses, it is common to map discrete data values to a set of ranges. This makes it easier to write queries that involve a range of discrete values. Read More