de-root-cause-analysis

Root cause analysis of pipeline failures uses an evidence-driven taxonomy and 5-step investigation workflow to diagnose Airflow DAG stalls and Spark job errors like OOM or shuffle issues. It reconstructs incident timelines and eliminates hypotheses to produce a clear RCA statement.

Troubleshooting dbt compilation and runtime test failures uses tech-specific diagnosis patterns to classify issues into logic, configuration, or dependency failures. The workflow assesses blast radius and gathers evidence to isolate the root cause and generate an incident runbook.

Investigating data quality anomalies like volume drops and null explosions uses lineage-aware impact assessment to trace upstream and downstream blast radius. The analysis applies evidence-driven hypothesis elimination to identify the exact pipeline failure source.

Yes, this RCA workflow supports concurrent diagnosis across Airflow, Spark, and dbt environments. It classifies infrastructure, concurrency, and dependency failures while producing a unified runbook-style checklist for deterministic incident documentation.

Starting root cause analysis requires uploading relevant pipeline logs, stating affected tables, and defining the incident time window. The workflow then reconstructs the timeline, assesses blast radius, and generates elimination steps to isolate the failure.

Lineage-aware impact assessment evaluates downstream blast radius to ensure your pipeline RCA captures full incident scope. It maps affected tables and dependencies to prevent blind spots during the failure triage and documentation process.