Realistic Test Data Generation
Proven data synthesis, subsetting, and masking
Synthesize intelligent test data and present it in your own way.
Do you need a test data management solution that:
- Fill test databases with realism and referential integrity
- Generate intelligent test data in text files, documents, reports, or images
- Improving Application Quality Through Stress Testing and Automation
- generate the data volume required for hardware and software benchmarking
- Preview of ETL mappings and prototypes of Data Vault models with test data
- Make anonymized datasets for offshore developers available online
- Direct integration with database clones, virtualization, and DevOps pipelines
Use only data models or metadata, but no actual production data?
Then you need a robust tool for generating test data. Table views, index sequences, key relationships, and file and report contents must reflect the characteristics of production data in order to be useful for testing. Generating realistic values and formats with synthetic data within ideal ranges and frequencies—and populating large targets—can be very time-consuming with other tools or programs.
With the IRI RowGen- Tool for the synthesis of test data or the IRI VoracityThe Test Data Management (TDM) platform, into which RowGen is embedded, allows you to create multiple intelligent test data targets for test databases, file structures, and custom report formats. regenerate from scratch – and all of this without access to real data. Or if you have real data wish to use, anonymize, segment, or otherwise mask data from production, Yes, you can do that with the IRI data masking tools in Voracity as well.
The IRI test data software offers four ways to generate anonymous yet intelligent test data for referentially consistent databases, flat files, semi-structured files, formatted reports, and even unstructured files:
Database or file synthesis (via random data generation or selection) in IRI RowGen.
Masking of production or test data in IRI FieldShield, CellShield EE or DarkShield.
RDB table partitioning and masking with RowGen or FieldShield Data (random generation/selection).
Every Combination of the above-mentioned techniques in Voracity (which includes everything).
Data Synthesis Methods
RowGen can generate structurally and referentially correct synthetic test data for any common RDBMS with defined constraints as well as test data in custom report layouts or common file/feed formats such as these:
- Record, row, or variable sequential data
- ASN.1 CDRs
- COBOL Index (MF ISAM, Vision)
- CSV, LDIF, JSON, and XML
- Excel (XLS/X)
- FHIR, HL/7 and X12 EDI
- Text with a fixed position and mainframe locked
- HDFS
- Image files and PDFs (using DarkShield with RowGen)
- MQTT and Kafka Topics
- BIRT (via ODA) or KNIME (Analysis and Visualization Nodes) in Eclipse
RowGen randomly generates field values across more than 100 data types. It can also randomly select data from set files at the field level. Combined with user-defined/composite data values, value ranges, and distributions, this improves the realism of test data.
Support for standard and complex data transformations, seed files, and conditional selection also contribute to RowGen's value in simulating production tables and file formats for a wide variety of applications.
For database users, RowGen uses DDL information for Oracle, DB2 UDB, SQL Server, Sybase, Teradata, and other platforms to create realistic tables with structural and referential integrity. Use RowGen to populate an entire test environment for Enterprise Data Warehouse (EDW) or Data Vault 2.0.
Data masking features
Use one of the static data masking tools that are in the IRI Data Protection are included or are free in the IRI Voracity-The platform includes:
- IRI FieldShield for Structured Files and Databases
- IRI DarkShield for structured sources as well as many semi-structured and unstructured data sources
- IRI CellShield for Excel Spreadsheets
to Recognition (Profiling, searching, and classification), De-identification (Encryption, pseudonymization, fuzzing, redaction, etc.) of data in production systems and its anonymized replication in lower development, test, and QA environments.
When you use IRI Voracity, you can leverage its RowGen synthetic data generation and FieldShield data masking capabilities to find, classify, subset, and mask data, and integrate this data for static development in lower environments or virtual usage in live test environments.
Please note our Consejos para la gestión de datos de prueba, when you have your requirements define and plan your strategy, and read these links for more information on using secure test data for:
Blog links
Other resources
- Bloor Research – IRI TDM InBrief
- The argument for considering data privacy and security in a company's outsourcing strategy Jacqueline Klosek and Andrew Lurie