Integrations · Data warehouses

Data warehouse test data management

Cloud data warehouses concentrate data from many systems in one place, which makes them attractive for testing analytics and reporting — and makes an unmasked copy of them a large exposure.

Warehouse test data has an extra constraint: every query costs money, so discovery and sampling must be deliberate and bounded.

What test data management needs here

Warehouse-native metadata discovery that does not scan tables to learn their structure.
Bounded reads with row limits, statement timeouts or byte budgets, so a test-data job cannot become an expensive full scan.
Relationship mappings where the warehouse does not enforce foreign keys, which is common.
Consistent masking with the operational databases the warehouse was loaded from.

How DataNivra approaches it

Metadata comes from the warehouse information schema; profiles and subset reads are explicit, bounded queries.
Where keys are declared but not enforced, relationships can be confirmed or added as explicit, versioned mappings.
Deterministic masking keys stay in your environment, so a masked warehouse table still joins to the masked source systems.
Whatever the system, row-level work happens in the DataNivra agent inside your network: only metadata, aggregates and evidence reach DataNivra Cloud.

Data warehouses: connector status (5)

4 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

Snowflake

Shipped in the standard agent and backed by recorded conformance evidence.

Dedicated Snowflake connector: INFORMATION_SCHEMA discovery, declared keys, row counts from table metadata, bounded aggregate profiles and Arrow result reads processed inside your network.

Available now — details about Snowflake

Available now

Google BigQuery

Shipped in the standard agent and backed by recorded conformance evidence.

BigQuery connector: dataset and table discovery through the tables API, partition and clustering metadata, row counts from table metadata, and reads through tabledata.list, which is not billed as query scanning.

Available now — details about Google BigQuery

Available now

Amazon Redshift

Shipped in the standard agent and backed by recorded conformance evidence.

Dedicated Redshift connector on the PostgreSQL protocol with Redshift's own system views: svv_tables/svv_columns discovery, declared keys, svv_table_info row estimates, bounded aggregate profiles and server-side-cursor reads into the local engine, over verified TLS.

Available now — details about Amazon Redshift

Available now

Azure Synapse Analytics

Shipped in the standard agent and backed by recorded conformance evidence.

Dedicated and serverless SQL pool connector over TDS (the SQL Server connector's driver and TLS enforcement): catalog discovery, declared primary keys, distribution metadata, row estimates from distribution statistics, bounded profiles and reads, labelled for cost attribution.

Available now — details about Azure Synapse Analytics

Coming next

Teradata

Active implementation; not yet available for production use.

Teradata Vantage connector using Teradata's driver installed by the customer: restricted dictionary-view discovery, declared primary keys (kept distinct from primary indexes), referential constraints, statistics-based estimates, bounded profiles and reads over verified TLS.

Coming next — details about Teradata

Learn, try, then start

See the whole workflow — discovery, classification, masking, subsetting and certification — in the synthetic playground, then start a free trial. The product overview explains how the customer-resident agent and DataNivra Cloud divide the work.

Other categories: Databases · Lakehouses · Object storage · Files · NoSQL · SaaS applications · Mainframe · Streaming · APIs.