Capabilities

Telemetry

From edge to cloud: collecting, buffering, and querying telemetry so engineers can trust what the system is doing.

Design intent

Use this lens when adopting Telemetry: define success criteria, start narrow, and scale with safe rollouts and observability.

Telemetry must be version-aware to make regressions attributable
Volume control keeps cost, UX, and query performance predictable
End-to-end latency and gaps are the first operational signals to watch

What it is

Edge components forward telemetry and events upstream; the platform stores and exposes them for operations and engineering workflows.

Design constraints

Telemetry must be version-aware to make regressions attributable
Volume control keeps cost, UX, and query performance predictable
End-to-end latency and gaps are the first operational signals to watch

Architecture at a glance

Edge emits health/events/telemetry with stable IDs (device/resource/app/deploy)
Store-and-forward buffers protect evidence during outages
Backend correlates timelines to snapshots so debugging becomes “find the diff”
This is a capability surface concern: visibility is required to scale safely

Typical workflow

Define the minimal signal set: deployment state + runtime health + adapter health + telemetry
Create dashboards that answer: what is running? is it healthy? is it correct?
Use correlated timelines (events + telemetry + rollout state) to debug
Codify alerts for restart bursts, buffer saturation, flapping, and drift

System boundary

Treat Telemetry as a capability boundary: define what success means, what is configurable per site, and how you will validate behavior under rollout.

How it relates to snapshots

It lets you attribute regressions to releases and roll back deterministically. Without version IDs, you end up guessing what changed.

Example artifact

Event timeline (conceptual)

t=00:00 deploy:start snapshot=snap_... device=edge-12
t=00:12 runtime:healthy resource=forte-1
t=00:20 adapter:connected protocol=modbus endpoint=pump-1
t=01:10 telemetry:ok points=128 gap=0s
t=03:40 alert:staleness point=pump_speed > 5s

What it enables

Report-by-exception and efficient streams
Historical traces for incidents and audits
Health signals for rollouts and diagnostics

Engineering outcomes

Telemetry must be version-aware to make regressions attributable
Volume control keeps cost, UX, and query performance predictable
End-to-end latency and gaps are the first operational signals to watch

Quick acceptance checks

Use report-by-exception and sampling to control volume
Tie telemetry/events to snapshot/deployment IDs for correlation

Common failure modes

Telemetry gaps from buffer saturation or upstream auth/network issues
Clock skew and batching causing out-of-order timelines
“Everything is green” while behavior is wrong (mapping/data-quality issue)
Over-alerting: noisy signals that hide real regressions

Acceptance tests

Verify the deployed snapshot/version matches intent (no drift)
Run a canary validation: behavior, health, and telemetry align with expectations
Verify rollback works and restores known-good behavior

Deep dive

Practical next steps

How teams typically turn this capability into outcomes.

Key takeaways

Telemetry must be version-aware to make regressions attributable
Volume control keeps cost, UX, and query performance predictable
End-to-end latency and gaps are the first operational signals to watch

Checklist

Use report-by-exception and sampling to control volume
Tie telemetry/events to snapshot/deployment IDs for correlation
Monitor buffer depth, drop counters, and end-to-end latency
Create dashboards for rollout comparisons and incident timelines

Next steps

Common questions

Quick answers that help align engineering and operations.

Why does correlation to versions matter so much?