Surface Data Eraser

The surface data eraser tool provides a schema-aware masking engine designed to sanitize datasets and logs by erasing or obfuscating surface-level data. It targets common fields such as names, emails, phone numbers, addresses, and identifiers, with per-field rules that preserve the data shape for downstream analytics. The tool is aimed at data engineers, privacy officers, security teams, and developers who need repeatable, auditable masking in test environments, data sharing pipelines, and compliance workflows. It supports CSV, JSON, and parquet exports and offers deterministic masking for repeatable test runs. Example: a customer table can be masked so that real identities are replaced by pseudonyms while still enabling join keys and aggregations.
The engine reads a defined schema and a set of masking rules, then applies field-level operations such as full redaction, partial masking (preserve length), or tokenization. It can utilize regex patterns to detect sensitive formats, preserve data formats (e.g., date-like strings retain structure), and generate deterministic outputs with a salt. Output maintains the original schema, making integration with analytics pipelines straightforward. The tool also produces an audit log with masking actions and applied rules.
It reduces leakage risk during sharing, accelerates compliance with GDPR/CCPA, and improves data governance. Target audiences include data lake managers, QA teams, and consultants who need repeatable, policy-driven data sanitization. Unique differentiators include schema-awareness, per-field customization, deterministic masking, and built-in export formats, plus optional policy modules and audit trails that support governance processes.
Core features include masked test data sets for development, redacted production-like data for demos, and anonymized analytics for customer insights with preserved distributions.