tracing-downstream-lineage

# Downstream Lineage: Impacts

Answer the critical question: "What breaks if I change this?"

Use this BEFORE making changes to understand the blast radius.

## Impact Analysis

### Step 1: Identify Direct Consumers

Find everything that reads from this target:

**For Tables:**

1. **Search DAG source code**: Look for DAGs that SELECT from this table
   - Use `list_dags` to get all DAGs
   - Use `get_dag_source` to search for table references
   - Look for: `FROM target_table`, `JOIN target_table`

2. **Check for dependent views**:
   ```sql
   -- Snowflake
   SELECT * FROM information_schema.view_table_usage
   WHERE table_name = '<target_table>'

   -- Or check SHOW VIEWS and search definitions
   ```

3. **Look for BI tool connections**:
   - Dashboards often query tables directly
   - Check for common BI patterns in table naming (rpt_, dashboard_)

**For DAGs:**

1. **Check what the DAG produces**: Use `get_dag_source` to find output tables
2. **Then trace those tables' consumers** (recursive)

### Step 2: Build Dependency Tree

Map the full downstream impact:

```
SOURCE: fct.orders
    |
    +-- TABLE: agg.daily_sales --> Dashboard: Executive KPIs
    |       |
    |       +-- TABLE: rpt.monthly_summary --> Email: Monthly Report
    |
    +-- TABLE: ml.order_features --> Model: Demand Forecasting
    |
    +-- DIRECT: Looker Dashboard "Sales Overview"
```

### Step 3: Categorize by Criticality

**Critical** (breaks production):
- Production dashboards
- Customer-facing applications
- Automated reports to executives
- ML models in production
- Regulatory/compliance reports

**High** (causes significant issues):
- Internal operational dashboards
- Analyst workflows
- Data science experiments
- Downstream ETL jobs

**Medium** (inconvenient):
- Ad-hoc analysis tables
- Development/staging copies
- Historical archives

**Low** (minimal impact):
- Deprecated tables
- Unused datasets
- Test data

### Step 4: Assess Change Risk

For the proposed change, evaluate:

**S