Datatrail
Use case

Root Cause Analysis for Data Pipelines, Traced Through Lineage

The dashboard is wrong, but the cause is four models upstream. Datatrail walks the lineage backward and points at the exact table and column that changed.

See how it works
Read-only Never moves your data
Lineage map
Lineage mapped from query history. Read-only connection.
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Read-only connection. Datatrail never moves or mutates your data.

In short

Root cause analysis for a data pipeline means tracing a wrong number or stale report back to the upstream change that caused it. Datatrail connects to your warehouse read-only and parses query logs into column-level lineage, so when a metric breaks you follow the affected column backward through every transformation and model to the precise source table, schema change, or freshness gap that introduced the error, instead of querying tables one at a time to guess.

// THE FIT

Why it fits

Data engineers and analysts who burn hours tracing broken metrics back through the pipeline.

Walk lineage backward

Start at the broken metric and Datatrail traces the column back through each model to the upstream table where it went wrong.

Pinpoint the change

A silent upstream schema drift or a stalled load is surfaced as the cause, with the timestamp it happened.

Stop guessing in SQL

No more opening ten tables to find which one moved. The lineage path names the root cause directly.

Map your lineage, end to end

Connect your warehouse read-only and see your lineage map in minutes. Datatrail never moves or mutates your data.