Silent failures are the strongest argument for why alert-based monitoring has hit its ceiling a green DAG tile tells you nothing about a freshness timestamp that's quietly wrong or a schema change that didn't break anything but corrupted meaning. What's worked for us at enterprise scale is treating data-level signals (volume, distribution, freshness, contract validity) as first-class inputs to an AI diagnosis loop, not just dashboard tiles. The catch is that the remediation side needs guardrails as much as the detection side needs coverage. I broke down that loop design here: https://www.nilayshah.me/ai-loops-self-healing-data-pipeline/
Great article, thanks. Lots of things to take back to work and check. Do I get a prize for recognising the largest unsigned 16 bit integer? 😁
Good start, it’s slightly more specific than that!
Silent failures are the strongest argument for why alert-based monitoring has hit its ceiling a green DAG tile tells you nothing about a freshness timestamp that's quietly wrong or a schema change that didn't break anything but corrupted meaning. What's worked for us at enterprise scale is treating data-level signals (volume, distribution, freshness, contract validity) as first-class inputs to an AI diagnosis loop, not just dashboard tiles. The catch is that the remediation side needs guardrails as much as the detection side needs coverage. I broke down that loop design here: https://www.nilayshah.me/ai-loops-self-healing-data-pipeline/
Data quality checks at each layer data ingestion, processing and storage can point out the data problems to the oncall to take actions proactively.
A good data engineer diagnoses and fixes schema drift from CSVs. A great data engineer asks why the pipeline isn’t API driven