Your data pipeline failed.
We tell you why.

WhyDidItFail provides automatic root-cause analysis for data pipeline failures—so teams can move from alert to action without hours of manual triage.

Early access is now open for a small group of data teams.

Why this exists

When pipelines fail today:

Alerts say something failed—not what or why
Logs are scattered across orchestration, compute, and data systems
Engineers manually trace runs, jobs, retries, and dependencies
Root cause takes hours (or days), even for common failure patterns

This isn’t a monitoring problem.
It’s a diagnosis problem.

What WhyDidItFail does

WhyDidItFail automatically analyzes pipeline failures by correlating signals across the full execution path:

Orchestration — pipeline runs, activities, retries
Compute — jobs, clusters, execution errors
Data — schema drift, volume anomalies, contracts

The result is a clear, actionable diagnosis:

“This failed here, because of this, and here’s what to do next.”

Example diagnosis

Pipeline: daily_customer_load
Failure point: ADF Copy Activity → Databricks Job

Root cause (high confidence):
Schema drift detected in source table customer_events

Evidence:
- New column added at 02:14 UTC
- Sink mapping not updated
- Last successful run at 02:00 UTC

Recommended action:
Update mapping or enable schema evolution

Who this is for

WhyDidItFail is built for data teams that:

Own production pipelines
Are on-call when failures occur
Want faster, more reliable incident resolution

Built by a data platform practitioner with 20+ years of experience designing and operating production data systems.

Request early access

WhyDidItFail is currently in early access. I’m working with a small number of data teams to shape the product and prioritize integrations.