As more enterprise test data moves into Databricks, exporting sensitive data for masking creates delay, risk and unnecessary complexity. With DATPROF, teams can mask sensitive data directly in Databricks Delta Tables and generate synthetic test data where the data already lives.
Databricks has become a central platform for analytics, data engineering and AI workflows in many organizations. Data that used to live mainly in relational databases now moves through lakehouse architectures, Delta Tables and bronze-silver-gold pipelines.
That is good news for innovation. But it also makes test data management more complex.
Once sensitive production-like data is used in Databricks for development, testing, analytics or AI validation, organizations face a familiar question in a new environment: how do we give teams realistic data without exposing personal or confidential information?
The answer is increasingly simple: mask data where it lives.