GCP Data Architect Series - Part V

Let’s compare realistically.

Kimball in BigQuery

Strengths:

• Simple joins

• BI-friendly

• Lower query cost

• Works well with Looker/Power BI

Best for:

• Analytics-focused teams

• Fast reporting

• Small-medium complexity environments

Data Vault in BigQuery

Structure:

• Hubs (business keys)

• Links (relationships)

• Satellites (attributes/history)

Example:

Pros in BigQuery:

• Handles schema evolution well

• Great for multi-source ingestion

• Strong auditability

Cons:

• More joins

• Requires marts layer for BI

• More engineering overhead

My Practical Rule

Use:

• Kimball for analytics layer

• Data Vault only if:

  • You have 10+ source systems

  • Strict audit requirements

  • Constant schema changes

Many enterprises do:

Data Vault → Transform → Kimball Marts