As Datapeers is purely based on meta-models, it has excellent cross-database features. It provides a simple and easy-to-use interface to your many different DBMS: SQL Server and Oracle (Others under consultation). Datapeers is also certified for the most common Operating Systems such as Microsoft Windows Server™, Linux, AIX™, Solaris™ and HP-UX™. Besides any custom developed software, it is already prepared to be integrated with market leading software packages such as Oracle® Applications and SAP®.
Datapeers automatically detects data dependencies and captures hidden correlations. By taking into consideration the associated dependent data and keeping its original business context in the masking process, Datapeers ensures that the masked value is consistent across the related data and enforces its referential integrity.
Masking all sensitive data to be used in non-production environments (when it is more vulnerable to fraud or theft) is an imperative for every modern organization. The only way to mitigate this risk is to use Datapeers, a toolset that offers a variety of sophisticated scrambling techniques to protect sensitive data, irreversibly replacing it by fictitious yet realistic data, in compliance with all major corporate and government data protection rules and regulations (HIPAA, GLBA, PCI-DSS, PIPEDA and the EU Data Protection Directive) whilst consistently provisioning smaller, rich, meaningful sets of masked data. By applying these state of the art techniques, Datapeers allows organizations to simplify and automate their complex masking requirements while ensuring data integrity.
For testing database-driven application performance, an empty database is useless. Creating test data sets or data generation scripts manually is a dull, time-consuming and resource-wasting task. Datapeers includes a powerful data generator that allows testers, developers and administrators to automatically populate databases with comprehensive, meaningful and realistic test data. Datapeers analyses existing database schemas and, based on a metadata model, automatically resolves master-detail relations for optimal data generation rules.
Datapeers allows organizations to consistently create numerous subsets of data that accurately reflect the original production database and meet the specific requirements of each type of testing.