What Is Telemetry Data?
Telemetry data refers to the automatic collection and transmission of data from remote systems, services, or devices to a centralized location for monitoring, analysis, and action. In software and infrastructure systems, telemetry data is foundational for observability — enabling teams to track performance, detect issues, and make data-informed decisions.
This data can come from virtually any digital system, including cloud applications, edge devices, web services, and infrastructure. Telemetry helps with real-time monitoring, capacity planning, debugging, and improving system health.
As modern systems become more distributed and complex, telemetry data has grown more essential. It enables DevOps teams to correlate signals, reduce downtime, and build more resilient applications. Whether you're running a Kubernetes cluster, deploying microservices, or monitoring IoT devices, having access to high-quality telemetry data makes all the difference.
Types of Telemetry Data
Below are 9 common examples of telemetry data collected in modern software environments. These types of telemetry each serve different purposes, and when used together, provide full visibility into your systems.
1. Metrics
What it is: Numerical measurements collected over time that reflect the state or performance of a system. Metrics are typically collected at regular intervals and are well-suited for trend analysis.
Use case: Track CPU usage, memory consumption, request rates, error rates, application latency, and other system-level indicators.
Example: http_requests_total showing the number of HTTP requests over time.
Why it matters: Metrics are ideal for building dashboards and alerts because they’re lightweight, easy to query, and give a consistent view of performance trends. You can use metrics to set Service Level Indicators (SLIs) and monitor system health.
Tools: Prometheus, OpenTelemetry Collector, Datadog, AWS CloudWatch
2. Logs
What it is: Timestamped records of events, messages, or errors produced by software systems. Logs can be structured (e.g., JSON) or unstructured (e.g., plain text).
Use case: Debug application behavior, identify issues, audit user actions, and investigate security incidents.
Example: 2024-04-18T10:32:00Z ERROR: Failed to connect to database.
Why it matters: Logs provide rich context and are essential for root cause analysis. They capture the narrative of what the system was doing, making them vital for post-mortems and security forensics.
Tools: ELK Stack (Elasticsearch, Logstash, Kibana), Fluent Bit, Loki, OpenTelemetry filelog receiver
3. Traces
What it is: A trace represents the journey of a request as it moves through a distributed system. It is composed of spans, each representing a single unit of work.
Use case: Identify bottlenecks, diagnose high-latency services, and understand the flow of requests in microservices architectures.
Example: A trace showing a user request hitting an API gateway, calling a backend service, and querying a database — with each segment timed and labeled.
Why it matters: Traces let you see how different components interact, enabling you to optimize performance and reduce latency.
Tools: Jaeger, Zipkin, OpenTelemetry SDKs and Collector, AWS X-Ray
4. Events
What it is: Discrete occurrences within a system that often signal a state change or significant action.
Use case: Track deployments, configuration changes, feature flags, and user interactions.
Example: A "user_logged_in" event or a "pod_scheduled" event in Kubernetes.
Why it matters: Events help correlate telemetry with specific actions. When something breaks, knowing what happened right before can drastically reduce time to resolution.
Tools: Kubernetes Events, GitOps systems, OpenTelemetry semantic conventions
5. Exceptions
What it is: Captured errors or unexpected behavior in application code. Often includes error messages and stack traces.
Use case: Track runtime exceptions, failed transactions, and bugs in production systems.
Example: NullReferenceException at UserController.cs:42
Why it matters: Exceptions often directly impact user experience. Capturing them gives developers visibility into real-world failures.
Tools: Sentry, Rollbar, New Relic, OpenTelemetry error instrumentation
6. Heartbeats
What it is: Periodic signals emitted by services or agents to confirm that they are alive and reachable.
Use case: Monitor uptime, detect system failures, and perform health checks.
Example: A server sends a ping every 30 seconds to indicate it's healthy.
Why it matters: Heartbeats allow you to detect unresponsive services quickly, helping you maintain service availability.
Tools: Consul, Nagios, Kubernetes liveness probes
7. Resource Utilization Data
What it is: Telemetry that shows how much of a given system resource is being used.
Use case: Optimize performance, right-size infrastructure, and manage auto-scaling.
Example: 80% memory utilization on a Kubernetes node, or 95% disk usage on an EC2 instance.
Why it matters: Helps avoid over-provisioning or resource exhaustion. Enables more informed capacity planning.
Tools: Prometheus Node Exporter, CloudWatch Metrics, OpenTelemetry Host Metrics receiver
8. Network Telemetry
What it is: Data about network performance, connectivity, and throughput. This includes latency, packet loss, and bandwidth.
Use case: Diagnose connectivity issues, optimize routing, detect DDoS attacks, and ensure SLAs.
Example: Average latency of 350ms between service A and B, with a spike to 800ms.
Why it matters: Slow networks can make fast services feel broken. Understanding network behavior is essential for distributed systems.
Tools: OpenTelemetry Collector with Prometheus exporters, Packetbeat, Netdata, Cloudflare analytics
9. Custom Business Metrics
What it is: Application-defined metrics that reflect business logic or product usage.
Use case: Measure KPIs such as sign-up rates, transaction counts, customer churn, or revenue per user.
Example: cart_checkout_success_rate = 98% or active_users = 12,340
Why it matters: These metrics provide insight beyond system health. They help product teams and executives understand business performance in real time.
Tools: OpenTelemetry SDKs, StatsD, Datadog, Google Analytics (event tracking)
Bringing It All Together
Each of these telemetry types serves a specific purpose, but the real power lies in combining them. By correlating metrics with logs, traces, and events, teams can:
- Pinpoint the root cause of outages
- Understand user behavior and service dependencies
- Optimize performance and reduce costs
- Create comprehensive dashboards for business and technical stakeholders
Modern observability tools, like the OpenTelemetry Collector, allow developers to gather all these signals into a centralized pipeline. From there, telemetry can be enriched, filtered, and routed to the right storage and visualization tools.
Final Thoughts
Telemetry data is the lifeblood of modern DevOps and observability. Whether you’re running a small app or a massive multi-region deployment, understanding and properly managing telemetry is critical for reliability, performance, and growth.
Want to reduce noise, optimize telemetry pipelines, and make your observability stack more efficient?
Schedule a demo with Sawmills →
