Intelligent and secure test data
Fast, realistic line generation
Test data for all
RowGen automatically creates and populates massive DB, file and report targets with structurally and referentially correct test data - in minutes, not hours!
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Secure test data that looks real
Stop relying on confidential production data. Masked data may not be realistic or robust enough. RowGen uses your metadata and business rules to create better test data.
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Suitable for every scenario
Test data created with RowGen improves DB / ETL prototypes and applications. Use the high-quality, high-volume targets to test and future-proof your platforms and solutions.
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References
RowGen use cases
High volume, referentially correct
„RowGen generates 20 GB tables with referential integrity for query testing. It eliminates problems accessing production data and generates the volumes that reflect our growth.“
Simultaneous function test
„RowGen is the only tool that provides large amounts 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 is the only tool that generates test files in the formats and sizes we need.“
Superior test data management
Use RowGen for:
Load accurate, secure test databases
Prototype Data Warehouse ETL Ops
Outsource development
Stress test of applications
Benchmarking new platforms
Compliance with data protection laws
Virtualize test data
Preview of Voracity ETL mappings
Create test data directly in:
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
Detailed and summarized reports
Learn more about RowGen
What others read
Test data management Intro
According to healthcare.gov, complex application development requires an appropriate needs analysis 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 as well as support for special constraints, nulls, etc.
Realistic data from the ground up
Can you quickly provide your prototypes with good and bad anonymous data? Will it match production ranges, distributions and appearances?