Highlights
- Observability watches pipelines from the inside, not the outside — the vendor's core pitch is instrumenting data applications at run time (metrics, lineage, schema drift) instead of inspecting datasets after the fact; in a generated-pipeline world this telemetry is itself generatable from the model.
- Detection without context is noise — connecting an anomaly to the producing process and downstream consumers is what makes an alert actionable; that connective tissue is lineage, and lineage is exactly what a model-driven approach gets for free.
- Expectations on data are rules by another name — freshness thresholds, volume ranges, schema contracts: observability re-invents the explicit rules layer at run time, confirming rules as the forgotten third pillar next to data and AI.
Highlights on this page are generated with the help of AI.