Test Data Services
IRI Test Data as a Service (TDaaS) options deliver safe, referentially correct test sets for relational databases of any size, as well as custom-formatted structured and semi-structured files with test data.
We define TDaaS in a couple ways:
1) As a professional service, where you send us the DDLor layout information for the data you need, along with any complex business rules, and we will engage in a service by the hour to synthesize that data for your:
- DB, Data Warehouse, and Data Vault prototypes
- Application stress-testing and DevOps
- Software or hardware benchmarks
- Product demonstrations
- Outsourced developers
Data targets can be created at IRI and sent through email or S/FTP, staged in a cloud service, or populated directly into your targets through remote log-in to your monitored system(s) under NDA.
2) As a self-service test data portal, where IRI test data is created alongside, within, and/or for third-party software enviromentslike CI/CD paltformsand fit-for-purpose test data service portals from Cigniti, ValueLabs, Windocks, and Capgemini. These companies all specialize in DevOps and have integrated IR Idata synthesis, subsetting, and masking capabilities into their platforms for your use (usually via a web application). Their worldwide presence and knowledge of IRI software allow you to combine robust test data generation and state-of-the-art ergonomics to provide the test sets your developers need in a managed way.
Please use the information request form below, or write to firstname.lastname@example.org to discuss your use case and obtain a cost quote.
Data Masking as a Service (DMaaS)
IRI also provides remote or cloud based data masking services for production data you prefer to work with. This data can be in databases, files, spreadsheets, Hadoop, and cloud platforms. Sources can be structured, semi-structured, or unstructured, big and small.
IRI experts in this case classify, discover, and consistently de-identify personally identifiable information (PII) and other sensitive data to preserve referential integrity. The masked targets in lower development environments are realistic, ready for testing, and compliant with data privacy laws.