plan-data-architect-review

A dbt data model architecture review evaluates schema designs across raw sources, staging, intermediate, and marts layers. It verifies key integrity, normalization, join readiness, and SCD handling to ensure naming consistency and evolution safety.

PRE-LOAD, POST-LOAD, and MODEL modes represent distinct architectural review phases for dlt pipelines. PRE-LOAD assesses project structure before data ingestion, POST-LOAD evaluates loaded data integrity, and MODEL mode focuses on schema design and transformation logic.

Checking dbt model layering and grain definitions for governance involves expert critique of schema designs and model architecture. This verifies naming conventions, join readiness, and evolution safety to produce a concise governance artifact with actionable recommendations.

Yes, you can assess dlt architecture and schema quality before loading data by running an architecture review in PRE-LOAD mode. This evaluates the project structure, model designs, and naming conventions to ensure schema quality and key integrity prior to data ingestion.

The best way to evaluate SCD handling and key integrity in data modeling is through an expert-level architecture review. This identifies and evaluates data model designs to verify normalization, join readiness, and evolution safety across staging and intermediate layers.

A data architecture review for dbt projects may be unnecessary for simple, single-source pipelines with no intermediate layers or complex join requirements. It is designed for rigorous governance, schema quality checks, and evolution safety across multi-layered raw, staging, and marts architectures.