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.
Safe Test Data that Looks Real
Stop relying on confidential production data. Masked data may not be realistic or robust enough either. RowGen uses your metadata and business rules to make better test data.
Cover Every Scenario
Persistent or virtual test sets improve DB/ETL prototypes and speed DevOps. Use these high quality, high volume RowGen targets to stress-test and future-proof your platforms and solutions.
RowGen Use Cases
Big Test Tables, 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 DBs
- 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.
Read NowRealistic Data from Scratch
Can you supply your prototypes with good and bad anonymous data quickly? Will it conform to production ranges, distributions, and appearances?
Read NowAutomate DB Data Generation
Testing DB queries and DW ETL/ELT jobs requires test data with structural and referential integrity, and support for special constraints, nulls, etc.
Read NowConsider RowGen when you're
- forgoing adequate testing and making inaccurate suppositions and extrapolations
- coding custom 3GL or shell programs for test sets valid in only one scenario
- using confidential production data at the risk of breaches, NDA violations, etc.
- scouring a low-end market of test data tools that lack speed or functional breadth
- relying on costly, hard-to-modify platform tools that subset and mask production data
or when production data:
- contains personally identifiable information
- cannot be accessed
- does not yet exist
- does not reflect future application/system scope
