Telemetry data quality monitoring and validation

How to test this use case

Measure Telemetry data quality monitoring with SQL and check the results

To measure telemetry data quality monitoring with sql, choose one workflow and its owner. Define the events, test them with known inputs, and write a query that answers a specific question.

1
Choose when to log
Inventory the event tables that power customer-facing, revenue, reliability, or compliance decisions.
2
Capture the outcome
Begin with event_contract_checked, ingestion_freshness_sampled, duplicate_event_observed and document the grain of each event.
3
Check the stored rows
Publish quality queries and alerts only after owners review thresholds, exclusions, and expected recovery behavior.
4
Make the decision
Which critical event streams are stale, incomplete, duplicated, or changing type?

Related use cases

Check which events this guide covers

Use this guide to check the event data itself, including freshness, required fields, types, duplicates, versions, and volume.
Use an operational monitoring guide to investigate application failures once you've checked the event data.
Use the event tracking plan template to specify events before implementing them. This guide explains how to keep checking their quality afterward.

Agent prompt

Paste this into your coding agent

Replace YOUR_API_KEY after signup, then ask the agent to run the product flow and verify the first events.

agent prompt

Telemetry data quality monitoring with SQL setup prompt

text

Add telemetry data-quality monitoring with Telemetry.

Use /skill.md and this Telemetry API key: YOUR_API_KEY

For every decision-critical event table, document the event grain, owner, required fields, field types, timestamp unit, allowed values, schema_version, duplicate key, expected arrival delay, and expected daily volume range.

Create deterministic fixtures for valid events, missing required fields, duplicate event IDs, late arrivals, and incompatible field types. Build SQL for ingestion freshness, required-field null rate, duplicate-event rate, schema-version adoption, late-arrival rate, and volume change by event name and release.

Add alerts only for sustained breaches with minimum sample sizes and an owner. Include links from each alert to the contract and validating query.

Do not place production payloads, credentials, personal data, or unrestricted error text in quality-test events.

Setup steps

1Inventory the event tables that power customer-facing, revenue, reliability, or compliance decisions.
2Define what one event represents. Specify its required fields, types, units, allowed values, arrival deadline, and duplicate handling.
3Send deterministic fixtures for valid, missing, duplicate, late, and incompatible events.
4Publish quality queries and alerts only after owners review thresholds, exclusions, and expected recovery behavior.

Events to capture

event_contract_checkedingestion_freshness_sampledduplicate_event_observedschema_version_observedevent_volume_sampled

Questions you can answer

Which critical event streams are stale, incomplete, duplicated, or changing type?
What percentage of current traffic uses the approved schema version?
Did a release change event volume or required-field completeness before a dashboard changed?

Example event schemas

Check what each event records, when to send it, and which field types it needs. Review the example payload and privacy checklist before using it in production.

Data quality11 fields