Data AnalyticsData EngineeringLineagedbtAirflowImpact AnalysisData Quality

Data Pipeline Lineage & Impact Analysis Platform

Data teams running dbt, Airflow, and Spark lack real-time data lineage that shows how upstream changes propagate to downstream dashboards and ML models. When a source table schema changes, teams cannot quickly identify all affected assets without manual investigation.

Overall

Problem Statement

When a source system changes a column name or adds a field, data engineers spend hours tracing which dbt models, Airflow tasks, and dashboards are affected. Broken dashboards are discovered by business users, not proactively. ML models silently receive incorrect has without anyone knowing.

The Idea

A data lineage platform that automatically maps dependencies across dbt models, Airflow DAGs, and BI tools, providing real-time impact analysis when any upstream source changes - preventing broken dashboards and incorrect ML model inputs.

Why Now

dbt reached mainstream adoption in 2025-2026 but its built-in lineage is limited to the dbt project boundary. Real data ecosystems span dbt, Airflow, Spark, Fivetran ingestion, and Looker/Tableau dashboards. Cross-tool lineage remains a manual documentation exercise.

Target User

Data engineers, analytics engineers, and data platform teams managing complex data pipelines spanning multiple tools

Target Market

Companies with 20+ dbt models, 10+ Airflow DAGs, and 5+ BI dashboards that experience frequent upstream changes

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Step-by-step validation plan
Score rationale across 11 dimensions
Monetization model & pricing angle
Competitors with links
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Risks & counter-evidence

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