Covers data lineage tracing in dbt, column-change impact assessment, and data catalog standards; use when tracing data origins, assessing the impact of schema changes, or managing the data catalog.
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---
name: lineage-governance
description: "Covers data lineage tracing in dbt, column-change impact assessment, and data catalog standards; use when tracing data origins, assessing the impact of schema changes, or managing the data catalog."
---
# Skill: Data Lineage & Governance
## When to load
When tracing data origins, assessing impact of schema changes, or managing data catalog.
## Lineage in dbt
```
dbt manifest.json contains full lineage graph.
Use: dbt ls --select +model_name (upstream)
dbt ls --select model_name+ (downstream)
```
## Impact Assessment for Column Change
```
Before renaming/dropping a column:
1. dbt ls --select model_name+ → list all downstream models
2. Check dashboards/BI tools connected to those models
3. Check ML feature pipelines consuming the column
4. Estimate blast radius: N models, M dashboards affected
Migration approach for breaking changes:
Phase 1: Add new column alongside old column
Phase 2: Migrate downstream consumers to new column (coordinate with owners)
Phase 3: Mark old column deprecated in YAML docs
Phase 4 (after 30 days): Drop old column
```
## Data Catalog Standards
Every table in the warehouse must have:
- `description`: what the table represents (grain, one sentence)
- `owner`: team or person responsible
- `data_classification`: PUBLIC / INTERNAL / CONFIDENTIAL / RESTRICTED
- `sla_freshness`: expected update frequency
- Column descriptions for all non-obvious fields
