Data & AnalyticsTechnologyPlatinum

Track data flow from source to end-user for audits or troubleshooting.

Data Lineage Tracker

Data Lineage, Metadata Management, Compliance

advancedv5.0

Best for

▸Mapping complete data flows from source systems to downstream dashboards and ML models for regulatory audit trails
▸Performing impact analysis before schema changes to identify all affected downstream systems and consumers
▸Debugging data quality issues by tracing problematic records back through transformation pipelines to their origin
▸Implementing column-level lineage tracking for sensitive data fields to ensure GDPR and CCPA compliance

What you'll get

▸Interactive lineage graph showing table-to-table flows with transformation details and downstream consumer mapping
▸Impact analysis report listing all affected datasets, dashboards, and ML models when a source system changes
▸Column-level lineage documentation tracking PII fields through complex transformations with compliance annotations

Expects

Details about your data ecosystem including source systems, transformation tools (dbt, Airflow, Spark), target systems, and specific lineage tracking requirements (table vs column level).

Returns

A comprehensive lineage map with source-to-target flow documentation, impact analysis reports, and actionable recommendations for lineage governance implementation.

What's inside

“You are a Data Lineage Architect. You hunt for hidden data dependencies, blast radius risks, and classification leaks that standard lineage tools miss. * **Reverse-engineer undocumented transformations** by analyzing query logs, job logs, and git diffs instead of relying on metadata declarations. Mo...”

Covers

What You Do DifferentlyMethodologyWatch For

Not designed for ↓

×Creating the actual data pipelines or ETL jobs themselves
×Performing data quality testing or validation of data values
×Building the underlying data warehouse or lake infrastructure
×Managing user access permissions or data security policies

SupaScore

89.63▼

Research Quality (15%)

9.1

Prompt Engineering (25%)

Practical Utility (15%)

8.65

Completeness (10%)

9.4

User Satisfaction (20%)

8.95

Decision Usefulness (15%)

8.8

Evidence Policy

Standard: no explicit evidence policy.

data-lineagemetadata-managementdata-governanceimpact-analysisdata-qualityopenlineagedbtapache-atlasdata-catalogetl-eltcompliancecolumn-level-lineagedata-observability

Research Foundation: 8 sources (4 official docs, 2 books, 2 industry frameworks)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.