Integrations · Mainframe

Mainframe test data management

Core banking, insurance and government systems still run on mainframes, and their records rarely stay there: they feed distributed systems through extract files every night.

Testing an end-to-end flow therefore needs mainframe records and modern-system records that describe the same customers — with the same masked identifiers on both sides.

What test data management needs here

Decoding of EBCDIC fixed and variable-length records described by COBOL copybooks, including packed-decimal (COMP-3) fields.
Masking that preserves field lengths and formats so the records still load into the mainframe test region.
Deterministic identity masking shared with the distributed systems the extracts feed.

How DataNivra approaches it

Mainframe support is roadmap work. The design decodes extracts inside your network and plugs them into the same relationship graph and certification model as every other source.
Whatever the system, row-level work happens in the DataNivra agent inside your network: only metadata, aggregates and evidence reach DataNivra Cloud.

Mainframe: connector status (1)

1 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.

Preview

Mainframe files (EBCDIC / copybook)

Real, executable support with the limitations listed on this page.

Built-in connector for mainframe record files described by COBOL copybooks: EBCDIC code pages, fixed and variable-length records, zoned and packed decimal (COMP-3) and binary fields, decoded inside your network into the same masking and certification workflow.

Preview — details about Mainframe files (EBCDIC / copybook)

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 · Files · NoSQL · SaaS applications · Streaming · APIs.