Recce - Trust, Verify, Ship

@datarecce.bsky.social

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Helping data teams preview, validate, and ship data changes with confidence. https://datarecce.io

A partial breaking change can have full impact on downstream models.
Impact Radius for all modified models
What downstream models actually need my attention?
data diffing alerts
setup noise
Data Reviews What Changed
A diff only shows you something changed, but not why, or what to do next.
Understanding these types helps you validate logic, assess risk, and build more transparent models in dbt Recce uses this classification to power column-level lineage and breaking change analysis DataRecce.io
Type 5: Unknown Ambiguous or unsupported logic. Schema needed to resolve DataRecce.io
Type 4: Source Comes from a literal or function, not an upstream model #etl #sql #datapipelines #dbt
Type 3: Derived Created using math, logic, or expressions. DataRecce.io
Type 2: Renamed Same data, new name. DataRecce.io
Type 1: Pass-through Selected directly from upstream without changes. DataRecce.io
Not all columns carry the same weight Some introduce logic, others just move data through. DataRecce.io
Understanding how columns are transformed in a dbt model is critical for impact analysis, QA, and trust in your pipeline. DataRecce.io
Recce's lineage DAG highlights the upstream and downstream model nodes that are connected through the dependencies of their related columns
Recce SOC 2 Type 1 compliance verified by AssuranceLab