DATPROF vs SDSA

DATPROF is a test data management platform for creating, protecting, automating and delivering data for development, testing and QA. Its modules cover privacy, subsetting, runtime, analysis and virtualization. Privacy masks test data and creates synthetic data while retaining representative characteristics, with more than 50 built-in generators supporting different languages. Subset extracts selected production data for test environments while preserving relationships. Runtime supports self-service provisioning, scheduling, monitoring and REST API automation. DATPROF can be deployed on premise or in the cloud, and says data stays in its database domains during masking. Native database support includes Oracle, Microsoft SQL Server, DB2 iSeries, DB2 LUW, PostgreSQL, EDB, MySQL and MariaDB. Supported flat file formats include CSV, Fixed Length, Recursive Fixed Length and XML. DATPROF says its masking can support GDPR, PCI and HIPAA requirements. Its security program aligns with ISO 27001 principles and includes SAST, dependency scanning and DAST in development. Support is available through a service desk portal, email and a documentation knowledge base. Pricing is available on request, and a free trial is listed.

SDSA is a free, AGPL-3.0 self-hostable application for sanitizing tabular data before it leaves a trusted environment. It accepts CSV, delimited TXT, single-table SQL INSERT dumps, JSON records, and XML records through a browser interface or REST API. SDSA infers schemas and flags likely sensitive fields such as emails, phone numbers, card numbers, government IDs, dates of birth, names, and addresses. Transformations include masking, HMAC hashing, tokenization, redaction, dropping, numeric binning, date or string truncation, and bounded Laplace noise. Users can enforce k-anonymity for selected quasi-identifiers and measure or enforce l-diversity for sensitive cleartext attributes. The app exports sanitized CSV plus JSON and Markdown privacy reports with utility summaries. A headless command-line pipeline supports CI/CD and data pipelines. Sessions use in-memory storage for a default 30 minutes. Docker and Python installation are documented.