tracing-downstream-lineage

Original：🇺🇸 English

Translated

Trace downstream data lineage and impact analysis. Use when the user asks what depends on this data, what breaks if something changes, downstream dependencies, or needs to assess change risk before modifying a table or DAG.

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Sourceastronomer/agents

Added on2026-02-04

NPX Install

npx skill4agent add astronomer/agents tracing-downstream-lineage

SKILL.md Content

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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:

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
```

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

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

For DAGs:

Check what the DAG produces: Use
```
get_dag_source
```
to find output tables
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:

Schema Changes (adding/removing/renaming columns):

Which downstream queries will break?
Are there SELECT * patterns that will pick up new columns?
Which transformations reference the changing columns?

Data Changes (values, volumes, timing):

Will downstream aggregations still be valid?
Are there NULL handling assumptions that will break?
Will timing changes affect SLAs?

Deletion/Deprecation:

Full dependency tree must be migrated first
Communication needed for all stakeholders

Step 5: Find Stakeholders

Identify who owns downstream assets:

DAG owners: Check
```
owners
```
field in DAG definitions
Dashboard owners: Usually in BI tool metadata
Team ownership: Look for team naming patterns or documentation

Output: Impact Report

Summary

"Changing

fct.orders

will impact X tables, Y DAGs, and Z dashboards"

Impact Diagram

                    +--> [agg.daily_sales] --> [Executive Dashboard]
                    |
[fct.orders] -------+--> [rpt.order_details] --> [Ops Team Email]
                    |
                    +--> [ml.features] --> [Demand Model]

Detailed Impacts

Downstream	Type	Criticality	Owner	Notes
agg.daily_sales	Table	Critical	data-eng	Updated hourly
Executive Dashboard	Dashboard	Critical	analytics	CEO views daily
ml.order_features	Table	High	ml-team	Retraining weekly

Risk Assessment

Change Type	Risk Level	Mitigation
Add column	Low	No action needed
Rename column	High	Update 3 DAGs, 2 dashboards
Delete column	Critical	Full migration plan required
Change data type	Medium	Test downstream aggregations

Recommended Actions

Before making changes:

Notify owners: @data-eng, @analytics, @ml-team
Update downstream DAG:
```
transform_daily_sales
```
Test dashboard: Executive KPIs
Schedule change during low-impact window

Related Skills

Trace where data comes from: tracing-upstream-lineage skill
Check downstream freshness: checking-freshness skill
Debug any broken DAGs: debugging-dags skill
Add manual lineage annotations: annotating-task-lineage skill
Build custom lineage extractors: creating-openlineage-extractors skill