Telemetry
For data and platform teams validating event contracts before dashboards and alerts depend on them

Telemetry Data Quality Monitoring with SQL

Monitor event freshness, required fields, types, duplicates, schema versions, and volume so broken instrumentation is found before it becomes a misleading business or reliability metric.

Reviewed by the Telemetry product team on . Event contract, recommended analysis, and privacy boundaries. Review standards and ownership

Start with sample dataCopy the prompt
Why this works
  • Test the event grain and required fields with known fixtures instead of treating successful ingestion as correctness.
  • Track freshness, late arrivals, duplicate identifiers, schema-version adoption, and unexpected volume changes separately.
  • Attach owners and contract versions to important event tables so failures have an explicit remediation path.
Use-case evidence path

Telemetry Data Quality Monitoring with SQL: from implementation to decision

A complete telemetry data quality monitoring with sql measurement loop connects one owned workflow, a bounded event contract, a controlled fixture, and a question someone can act on.

  1. 1

    Set the boundary

    Inventory the event tables that power customer-facing, revenue, reliability, or compliance decisions.

  2. 2

    Capture the outcome

    Begin with event_contract_checked, ingestion_freshness_sampled, duplicate_event_observed and document the grain of each event.

  3. 3

    Prove the rows

    Publish quality queries and alerts only after owners review thresholds, exclusions, and expected recovery behavior.

  4. 4

    Make the decision

    Which critical event streams are stale, incomplete, duplicated, or changing type?

Scope and neighboring use cases

Choose this page for the right measurement boundary

  • Use this page to validate the telemetry itself: freshness, required fields, types, duplicates, versions, and volume.
  • Use an operational monitoring page when the source events are trustworthy and the application workflow is what may be failing.
  • Use the event tracking plan template for a concise implementation brief; this guide covers the ongoing quality-control program.

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

  1. 1Inventory the event tables that power customer-facing, revenue, reliability, or compliance decisions.
  2. 2Define grain, required fields, types, units, allowed values, lateness, and duplicate policy for each contract.
  3. 3Send deterministic fixtures for valid, missing, duplicate, late, and incompatible events.
  4. 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 unlocked

  • 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?

Event schema starting points

Event contracts for this workflow

Review the row grain, emit boundary, required types, privacy classes, example payload, and validation checklist before adapting a query or snippet to production.

Data quality11 fields

telemetry_delivery_observed

One sampled or aggregated observation per delivery attempt or delivery window.

Inspect contract

Related product capability

Continue this workflow in Structured events

Capture stable event names, typed fields, and privacy-reviewed operational context.

Explore Structured eventsReview customer evidence

Related SQL recipes

Answer the next question with SQL

Run the query against the structured fields from this workflow, inspect the example result, and turn a useful answer into a dashboard or alert.

Browse all recipes
Data qualityBeginner

Measure Event Ingestion Freshness

Which production event sources are stale or delayed right now?

Open recipe
Data qualityBeginner

Measure Required-Field Null Rate

Which event names have an unacceptable missing account identifier rate?

Open recipe
Data qualityBeginner

Find Duplicate Event IDs

Which event IDs were received more than once?

Open recipe
Data qualityBeginner

Track Event Schema-Version Adoption

Which producers still emit old versions of a critical event?

Open recipe
Data qualityIntermediate

Measure Late-Arriving Events

Which event producers deliver data late enough to distort analysis?

Open recipe
Data qualityBeginner

Measure Telemetry Volume by Event Name

Which event contracts create the most ingestion volume?

Open recipe
Complete collectionsData quality SQL

Customer evidence

Related workflows described by customers

“Telemetry is one of the easiest ways to go from dumping in your data to actually understanding it.”

Shayan Taslim, LogSnag

Review the documented context
“Telemetry is my go-to for dumping logs and tracking events. It's straightforward, the SQL workbench is fast, and I don't have to worry about the schema upfront. Running SQL over a REST API lets me fetch data and dump reports straight to Slack. It's the essentials of what Sentry did for me.”

Prem Viswanathan, SwiftCX

Review the documented context

Next step

Create the API key your agent will use

The free plan is enough to run the prompt, send test events, and review the first dashboard.

Prompt packStart with sample data

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