Data Arrow
Business Model → Data Model → dbt → Lineage → Graph Model
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1The gap between business modeling and data engineering is the interesting part. Curious where users feel the friction most: understanding the model, translating it into dbt, or keeping the business and technical representations aligned.
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There are several complexities in modern data modelling, and many of them actually start with the fundamentals.
Even with Kimball, one of the biggest challenges is translating business requirements into a clear, consistent model while staying true to dimensional modelling standards.
That is exactly why we created Data Arrow — to help organize the different pieces of a business requirement and systematically translate them into a structured Kimball-based data model.
At the same time, dbt is sometimes mistaken for a data modelling platform. In reality, dbt is primarily a powerful data transformation engine. The business model and modelling decisions still need to come first.
Data Arrow is intended to bridge that gap:
Business Requirement → Dimensional Model → dbt TransformationMore than happy to have a discussion, walk through the approach, or offer access to the public preview free of cost for anyone interested in trying it.
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1That makes sense. The business requirement → dimensional model → dbt transformation sequence makes the product’s role much clearer.
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