Data Pipelines

Monitoring dbt, Airflow, and other data pipelines for the failures that leave a clean exit code and a wrong answer: stale tables, empty loads, and creeping slowdowns.

• September 27, 2026 · 6 min read

  The pipeline that got slower every week

  Four minutes in January, forty in April, a clean exit every night in between. Why gradual slowdown hides from monitoring, how to record run duration where a trend is visible, and how to alert on slower-than-usual.

• September 1, 2026 · 7 min read

  Watching dbt models without a data-observability platform

  dbt already has tests and source freshness. What they cover, where they stop, and how to get a scheduled dbt build to page you when a test fails or the run goes missing, without adopting a profiling platform.

• August 31, 2026 · 6 min read

  The Airflow DAG succeeded and the table is three days stale

  Every task in the DAG is green, and the model it builds hasn't updated since Tuesday. Why Airflow's success is orthogonal to data freshness, and how to check freshness without a data-observability platform.

• August 28, 2026 · 7 min read

  Catching the pipeline run that processed zero rows

  A dbt build, an Airflow DAG, or a plain cron ETL can finish green and load nothing. How to make a zero-row run a failed run, without a data-observability platform.