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DTSTART;TZID=Europe/Berlin:20261118T080000
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CREATED:20260702
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SUMMARY:Runtime Lineage: End-to-End OpenTelemetry Tracing for AI Data Systems
DESCRIPTION:Most data lineage is static. Catalogs, dbt graphs, and SQL parsers read your code ahead of time and draw a table-to-table, column-to-column map of how data is supposed to flow. That is useful, until it isn’t: it describes the intended design, not what actually happened on a given run. And it stops at the column boundary, not the full path from raw source to the value something finally consumes. That gap turns into a real operational problem once AI agents enter the picture. Agents pick data sources and transformations dynamically, per request, at runtime. There is no static graph for a path that is decided live. So, when an agent on a complex data system returns a wrong or surprising answer, a design-time diagram cannot tell you what it actually did. This talk makes the case for runtime lineage: capturing the real, end-to-end path as the system executes, and treating it as an observability problem rather than a documentation one. OpenTelemetry turns out to be the natural carrier, it is already a runtime, distributed-trace standard. Using mloda (Apache-2.0), whose Extender hook wraps every transformation, a whole run becomes a single trace from source to consumed value, viewable in the Grafana Tempo / Jaeger you already operate. Live demo: watch one end-to-end run as a trace, then change the data path and watch the trace change, because it reflects what ran, not a static model.\n
URL:https://osmc.de/talks/runtime-lineage-end-to-end-opentelemetry-tracing-for-ai-data-systems/
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