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 af dags list to get all DAGs
   • Use af dags source <dag_id> to search for table references
   • Look for: FROM target_table, JOIN target_table

2. Check for dependent views:

   sqlCopy code
   -- 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_)

On Astro

If you're running on Astro, the Lineage tab in the Astro UI provides visual dependency graphs across DAGs and datasets, making downstream impact analysis faster. It shows which DAGs consume a given dataset and their current status, reducing the need for manual source code searches.

For DAGs:

1. Check what the DAG produces: Use af dags source <dag_id> to find output tables
2. Then trace those tables' consumers (recursive)

Step 2: Build Dependency Tree

Map the full downstream impact:

Copy code
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:

1. DAG owners: Check owners field in DAG definitions
2. Dashboard owners: Usually in BI tool metadata
3. 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

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

Detailed Impacts

Risk Assessment

Recommended Actions

Before making changes:

1. Notify owners: @data-eng, @analytics, @ml-team
2. Update downstream DAG: transform_daily_sales
3. Test dashboard: Executive KPIs
4. 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