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Academy/Lab: Repair Missing Intervals Without Losing Trust
Hands-On Lab

Repair missing intervals without losing trust

This lab teaches the discipline around telemetry patching so teams do not treat repaired data as if it were identical to original data.

Lab Guide9 minTelemetry Patching
  1. 1

    Load the telemetry file

    Upload a file that contains at least one known missing-interval pattern. Before running patching, note how many gaps or irregular rows the preview identifies.

  2. 2

    Choose the patching rule deliberately

    Decide whether interpolation or fallback extrapolation is justified for this dataset. The point is not to force a repair, but to choose the least misleading option.

  3. 3

    Run patching and inspect the output

    Look at which intervals were changed and whether the correction pattern makes operational sense. If the patch creates artificial smoothness or implausible values, do not treat it as a valid repair.

  4. 4

    Preserve auditability

    Export the patched dataset and the associated summary so another reviewer can see what was changed. The goal is to retain traceability, not to hide the existence of data repair.

Lab success criteria: You can explain which intervals were repaired, why that method was used, and whether the resulting file is suitable for downstream analysis.