Smarter and Safer Test Data
Fast, Realistic Row Generation
Test Data for Everyone
RowGen automatically builds and populates massive DB, file, and report targets with structurally and referentially correct test data - in minutes, not hours!
Learn more
Safe Test Data that Looks Real
Stop relying on confidential production data. And masked data may not be realistic or robust enough. RowGen uses your metadata and business rules to make better test data.
Learn more
Cover Every Scenario
RowGen-created test data improves DB/ETL prototypes and applications. Use its high quality, high volume targets to stress-test and future-proof your platforms and solutions.
Learn more
References
RowGen Use Cases
High Volume, Referentially Correct
"RowGen generates 20GB tables with referential integrity for query testing. It eliminates production data access concerns and generates the volumes that reflect our growth."
Simultaneous Functional Testing
"RowGen is the only tool that supplies high volumes of test data on multiple operating systems and simultaneously manipulates the test data for application compatibility."
Better than Production Data
"RowGen creates realistic PII and PAN data to support our OLTP app development and testing. It's the only tool that generates test files in the formats and sizes we need."
Superior Test Data Management
Use RowGen to:
Load Accurate, Safe Test Databases
Prototype DW ETL Ops
Outsource Development
Stress-Test Applications
Benchmark New Platforms
Comply with Privacy Laws
Virtualize Test Data
Preview Voracity ETL Mappings
Build Test Data directly into:
RDBMS Tables and Excel®
Rec/Line/Var. Sequential Files
CSV, LDIF, Text, and XML
ASN.1 CDRs
Data Vault 2.0 Models
MFVL, ISAM, and Vision Files
Mainframe and V/B Files
Detail and Summary Reports
Learn more about RowGen
What Others Are Reading
Test Data Management Intro
As anyone from healthcare.gov can tell you, complex application development requires adequate needs assessments and sufficiently robust test data.
Automate DB Data Generation
Testing Database queries and DW ETL/ELT jobs requires test data with structural and referential integrity, and support for special constraints, nulls, etc.
Realistic Data from Scratch
Can you supply your prototypes with good and bad anonymous data quickly? Will it conform to production ranges, distributions, and appearances?