How does it identify stakeholders for downstream assets?

The skill parses the 'owners' field in DAG definitions and searches BI tool metadata to find the specific teams or individuals responsible for affected assets.

Why should I use this before making a production change?

It prevents 'silent breaks' by showing exactly which production dashboards, ML models, or regulatory reports rely on the data you are about to modify.

What types of data assets can this skill trace?

It traces SQL tables (such as Snowflake), Airflow DAGs, view definitions, and BI tool connections like dashboards and reports.

Can it assess specific types of schema changes?

Yes, it evaluates risks associated with adding, renaming, or deleting columns, as well as changes to data types, volumes, or timing.

Does it support recursive tracing?

Yes, it can identify what a DAG produces and then recursively trace those output tables' consumers to build a complete dependency map.

Data Impact & Downstream Lineage

Name: Data Impact & Downstream Lineage
Author: astronomer

byastronomer

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Gestión de Bases de Datos

Analyzes data dependencies to determine the impact and blast radius of proposed schema or pipeline changes.

Acerca de

This skill empowers data engineers and developers to perform comprehensive impact analysis before modifying data assets. By tracing downstream lineage across tables, Airflow DAGs, and BI dashboards, it identifies direct and indirect consumers to assess risk levels. It facilitates safer deployments by mapping dependency trees, categorizing asset criticality, and identifying relevant stakeholders, ensuring that structural changes do not silently break production reports, ML models, or automated workflows.

Características Principales

77 GitHub stars
Detailed impact reporting with visual dependency tree representations
Stakeholder identification through DAG ownership and BI tool metadata
Automated downstream dependency mapping for SQL tables and Airflow DAGs
Categorization of impact by criticality levels (Production, High, Medium, Low)
Schema change risk assessment for column modifications or deletions

Casos de Uso

Assessing the 'blast radius' before renaming or deleting a core database column
Identifying which executive dashboards will be affected if a specific data pipeline is modified
Mapping the full dependency tree of a warehouse table to plan a large-scale data migration

Acerca de

Características Principales

77 GitHub stars
Detailed impact reporting with visual dependency tree representations
Stakeholder identification through DAG ownership and BI tool metadata
Automated downstream dependency mapping for SQL tables and Airflow DAGs
Categorization of impact by criticality levels (Production, High, Medium, Low)
Schema change risk assessment for column modifications or deletions

Casos de Uso

Assessing the 'blast radius' before renaming or deleting a core database column
Identifying which executive dashboards will be affected if a specific data pipeline is modified
Mapping the full dependency tree of a warehouse table to plan a large-scale data migration