Available now
Parquet files
Shipped in the standard agent and backed by recorded conformance evidence.
Built-in file-set connector for Parquet (or CSV) files on a local or mounted filesystem; each declared file or directory is a table.
Integrations · Files
File-based test data management
Files on a local or mounted file system are the simplest source and the easiest to get wrong: a directory of extracts is a full copy of production that nobody governs.
A read-only landing zone turns those extracts into a controlled input for masking, subsetting and certification.
What test data management needs here
- Declared CSV headers and table names, so nothing is guessed from data.
- A read-only mount that the agent can prove from file-system permissions.
- Clear retention: extracts should be deleted once a certified dataset exists.
How DataNivra approaches it
- Discovery refuses undeclared CSV headers rather than guessing, and reports only structure and aggregates.
- Certified outputs are written to a separate target, so the sensitive extract can be removed.
- Whatever the system, row-level work happens in the DataNivra agent inside your network: only metadata, aggregates and evidence reach DataNivra Cloud.
Files: connector status (2)
2 of these connectors can be used today (available or in preview); the others are shown with their honest status. Statuses are derived from recorded conformance evidence.
Available now
Local files (Parquet / CSV)
Shipped in the standard agent and backed by recorded conformance evidence.
Built-in file-set connector for Parquet or CSV extracts in a local or mounted directory (for example a read-only landing zone for exports).
Learn, try, then start
See the whole workflow — discovery, classification, masking, subsetting and certification — on synthetic data in the interactive demo, then start a free trial. The product overview explains how the customer-resident agent and DataNivra Cloud divide the work.
Other categories: Databases · Data warehouses · Lakehouses · Object storage · NoSQL · SaaS applications · Mainframe · Streaming · APIs.