Catalog
concept#Observability#Platform#Data#Security

Telemetry Collection

Concept for systematically collecting and forwarding metrics, logs and traces to support observability and operations.

Telemetry collection describes the systematic capture, aggregation and forwarding of metrics, logs and traces from distributed systems.
Established
Medium

Classification

  • Medium
  • Technical
  • Architectural
  • Intermediate

Technical context

Observability backends (e.g. Prometheus, Jaeger, Grafana)Log storage and SIEM systemsAlerting and incident management tools

Principles & goals

Prioritize signals: focus on actionable metrics and failure cases.Enable end‑to‑end correlation: link metrics, logs and traces.Control the data lifecycle: limit sampling, retention and costs.
Run
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Excessive data collection leads to unnecessary costs.
  • Insecure telemetry can expose sensitive information.
  • Lack of correlation hinders root‑cause analyses.
  • Focus on actionable signals rather than raw data flood.
  • Use standard formats (e.g. OpenTelemetry) for interoperability.
  • Automate retention and sampling for cost control.

I/O & resources

  • Instrumented applications (metrics, traces, logs)
  • Agents or sidecars for data collection
  • Infrastructure metrics (host, network, storage)
  • Aggregated metrics and time series
  • Consolidated logs and correlated traces
  • Alerts, dashboards and SLO reports

Description

Telemetry collection describes the systematic capture, aggregation and forwarding of metrics, logs and traces from distributed systems. It provides the foundation for observability, incident analysis and SLO measurement. Implementations must balance sampling, privacy and cost control to avoid overload and data loss.

✔

  • Improved fault diagnosis through correlated telemetry.
  • Early detection of regressions and performance issues.
  • Foundation for SLO measurement and operational automation.

✖

  • High data volume can increase costs and storage load.
  • Inadequate sampling strategies can miss important signals.
  • Heterogeneous systems complicate unified metric models.

  • Ingestion rate

    Number of telemetry events per second being ingested.

  • Data loss rate

    Proportion of captured events lost before persistence.

  • Query latency

    Time to answer typical diagnostic queries in the backend.

OpenTelemetry collector pipeline

Use of the OpenTelemetry Collector to aggregate and forward telemetry data.

SLO monitoring with metrics and logs

Combined use of metrics and logs to monitor service level objectives.

Forensic investigation using correlated traces

Investigating a security incident by correlating traces and audit logs.

1

Inventory signals and set priorities.

2

Introduce and test agents and collector pipeline.

3

Configure sampling and retention rules, define alerts.

4

Establish monitoring and cost control, plan iterations.

⚠️ Technical debt & bottlenecks

  • Legacy agents sending outdated formats.
  • Missing standardization of metric names.
  • Monolithic collector pipelines lacking scalability.
Ingestion rate and burst handlingStorage cost for long‑term retentionProcessing and query latency
  • Unlimited log retention leading to exploding costs.
  • Sampling so aggressive that error patterns disappear.
  • Storing sensitive user data unencrypted in telemetry.
  • Assuming more telemetry automatically yields better insights.
  • Forgetting privacy requirements when logging.
  • Underestimating costs due to poor retention policies.
Understanding of distributed systems and tracingKnowledge of metric models and time‑series storageExperience with collector and agent configuration
Scalability of ingestion pipelineLow latency for real‑time alertsData integrity and availability
  • • Network bandwidth between agents and collector
  • • Legal requirements for logs and privacy
  • • Cost budget for storage and ingestion