This Airflow Pipeline Was Broken. Here's How an AI Agent Helped Me Fix It.
One AI session traced, diagnosed, and fixed a failing e-commerce ETL pipeline with 5 broken dbt models.
OVERVIEW
A pipeline in red in Airflow is one of the most stressful sights in data engineering. The hard part isn't seeing the failure. It's tracing it through layers of staging models, intermediate transformations, and mart dependencies to find the actual root cause.
This demo walks through a broken daily e-commerce ETL pipeline in Airflow. The pipeline runs dbt through staging, intermediate, and mart layers, and supports everything from reporting to analysis. Instead of manually chasing failures through Airflow logs and individual dbt model files, Altimate Code traces the full DAG structure, reads every model in the dependency graph, and surfaces all five root causes in one pass.
The fixes cover Snowflake-to-PostgreSQL dialect translation (replacing :: cast syntax with standard CAST), a missing column reference caused by an upstream rename, a Cartesian join from a missing pre-aggregation step, a duplicate model reference, and a missing mart that was referenced in the README but never built. After the fixes, the pipeline runs to completion and comes back clean.
WHAT YOU'LL LEARN
- →How to trace Airflow pipeline failures through dbt lineage without manually reading every model
- →How to identify dialect incompatibilities between Snowflake and other databases
- →How to fix upstream column renames that silently break downstream models
- →How to go from a red pipeline to a successful build in one session
KEY POINTS
- Agent reads the full DAG and surfaces 5 root causes before writing a single fix
- Fixes Snowflake dialect :: cast → standard CAST for PostgreSQL compatibility
- Rewrites a Cartesian fan-out by adding the missing pre-aggregation CTE
- Creates the missing mart_daily_sales model from scratch based on the README spec
- Reruns the full Airflow pipeline to a successful build
