dv-discover

To propose a Data Vault model from source descriptions, analyze the DDL, CSV headers, or plain-language narratives to identify candidate hubs, links, and satellites capturing business keys and relationships. This generates a structured vault model proposal with open questions for governance.

A Pragmatic Data Vault model structures data into hubs, links, and satellites to capture business keys and relationships. It maps domain concepts to DDD contexts by profiling sources and recommending loading patterns to produce a structured model proposal.

Yes, you can generate hubs, links, and satellites from multiple source representations simultaneously. Analyzing various input formats like DDL or table columns surfaces candidate vault structures and maps domain concepts across the provided sources.

The best way to scope a Data Vault structure for a new vendor integration is to analyze the vendor's source descriptions. Profiling the source data surfaces candidate hubs, links, and satellites, yielding a structured model proposal with open questions for confirmation.

Data Vault model proposals handle missing relationship details by generating open questions for governance and confirmation. The analysis surfaces candidate structures from available source descriptions while explicitly flagging ambiguities for user validation.

You do not need DDL to generate a Data Vault model proposal; CSV headers or plain-language descriptions are sufficient. The analysis accepts various input formats to surface candidate hubs, links, and satellites from the provided source data.