The most fragile system I ever built wasn't the model. It was the data pipeline feeding it.

17 data sources. 3 different formats (JSON, CSV, XML). Updated at different frequencies (real-time, hourly, daily). Different quality levels (some clean, some a mess).

The model was fine. The pipeline broke every other day.

Data pipeline lessons learned the hard way:

Validate data at every stage, not just at ingestion. A schema check at the source doesn't catch corruption during transformation.

Build for pipeline recovery, not just pipeline success. When (not if) a stage fails, can it retry? Can it resume from where it stopped? Can it alert you before the model starts training on garbage?

Separate extraction, transformation, and loading. When they're tangled together, debugging is a nightmare.

Monitor data freshness. A pipeline that runs successfully but processes stale data is a silent failure.

Use idempotent operations everywhere. If a step runs twice, the result should be the same. This single principle eliminates an entire class of bugs.

70% of ML project failures trace back to data pipeline issues. Not model issues. Not algorithm issues. Pipeline issues.

Build robust pipelines first. The model will thank you.