Introduction to Telemetry Systems: The Backbone of Observability
Modern software systems are complex, distributed, and constantly evolving. Whether you’re deploying microservices in Kubernetes or managing legacy systems in the cloud, one truth holds: you need visibility. That’s where telemetry systems come in. They provide the data and structure needed to observe, understand, and operate IT systems with confidence.
In this post, we’ll explore the fundamentals of telemetry systems, how they work, the types of data they handle, and why they’re indispensable for achieving observability.
Why Observability Matters
Observability is the ability to infer the internal state of a system based on its external outputs. It’s what allows engineers to go from “something’s broken” to “here’s exactly what happened” without needing to SSH into production.
In practice, observability relies on three categories of telemetry data: logs, which are textual records of discrete events; metrics, which represent numerical measurements over time; and traces, which capture the path a request takes through multiple components. Each signal type reveals a different angle of system behavior, and together, they enable teams to troubleshoot issues, detect anomalies, and make data-informed decisions.
What Are Telemetry Systems?
Telemetry, in the context of IT, refers to the automated collection and transmission of measurement data from remote systems. A telemetry system manages this process—from capturing data at the source, to transforming and storing it, to presenting it to users in a way that supports monitoring, analysis, and alerting.
Different telemetry systems specialize in handling different types of signals. Centralized logging systems collect logs across your infrastructure and allow you to search and correlate them in one place. Metrics systems focus on time-series numerical data, often visualized through dashboards to give high-level insights. Distributed tracing systems are designed to track requests as they move through services in a distributed architecture. Lastly, SIEM systems are tailored for security and compliance, aggregating logs and events with a focus on threat detection and auditing.
Who Uses Telemetry—and Why?
Telemetry systems serve a broad range of stakeholders within an organization. DevOps engineers and site reliability teams typically rely on centralized logging and metrics systems to monitor system health and respond to incidents. Software developers use logs for debugging, metrics for understanding performance, and traces to analyze complex interactions in microservices.
Security and compliance teams lean on centralized logging and SIEM platforms to detect anomalies and investigate incidents. Even customer support teams benefit from telemetry, using logs and traces to reproduce user issues or validate bug reports.
The Architecture of a Telemetry System
Despite variations in implementation, most telemetry systems share a similar high-level architecture composed of three stages—emitting, shipping, and presentation—and two supporting processes—markup and enrichment.
The emitting stage is where telemetry is generated by applications or infrastructure. This is often the best point to add contextual metadata—like service name, version, environment, or process ID—because it’s closest to the source of truth. Whether logs are being written, metrics are being recorded, or spans are being created, this is the moment telemetry is born.
The shipping stage is responsible for transporting the emitted data to a backend. During this phase, telemetry may be parsed, transformed, enriched with additional context, routed to the appropriate destination (especially in multi-tenant environments), or even duplicated across systems. This is also where buffering, queuing, or streaming can help manage volume, resilience and security.
Finally, the presentation stage is where stored telemetry is visualized, queried, and analyzed. Whether through dashboards, alerts, or ad hoc searches, this stage turns raw data into actionable insights. Enrichment can also occur here, for example by correlating multiple data types or deriving new signals from existing ones.
Supporting this flow are two critical processes. Markup refers to adding context at the point of emission, like including which function or module generated a log line. Enrichment, on the other hand, often occurs during shipping or presentation and involves transforming data formats or adding metadata (like geographic location from an IP address) to make telemetry more searchable and informative—especially important when ingesting data from third-party sources.
Principles of Robust Telemetry Systems
Well-designed telemetry systems follow several important principles. First, they must not interfere with production workloads. That means telemetry should be emitted asynchronously and buffered when possible, especially in environments where direct-to-storage pipelines are infeasible.
Production systems should only write telemetry—they should never read or modify it after the fact. Once emitted, telemetry data should be immutable, and any access or changes must be auditable. Continuous delivery is another key principle: telemetry should be shipped in real time or near-real time, avoiding scheduled batch jobs that introduce delay and risk. Security is also paramount. Telemetry systems must be resistant to tampering, especially by insiders or compromised accounts.
Exploring Telemetry System Types
Centralized logging is often the first telemetry system teams adopt. It aggregates logs from across systems, making it easier to search and correlate events. These systems offer the richest detail but are also the most expensive to operate due to high cardinality and complex indexing. As a result, they typically have the shortest retention periods.
Metrics systems focus on numerical data over time, such as request rates or memory usage. They’re built on time-series databases and offer efficient storage and querying for aggregated data. With low cardinality and long retention, metrics are ideal for monitoring and alerting, but less useful for debugging one-off events.
Distributed tracing systems track the flow of a request through multiple services, making them indispensable in microservice and event-driven architectures. Tracing bridges the gap between metrics and logs by providing both context and timing. While powerful, these systems can be complex to set up and manage, and often require sampling to control data volume.
SIEM systems are specialized platforms used by security teams to collect, analyze, and correlate security-related telemetry. These systems often integrate with compliance workflows and may retain data for years due to regulatory requirements. They’re typically commercial offerings and may overlap with centralized logging, but with a strong focus on auditability and incident response.
Real-World Deployment Patterns
There are several common ways to deploy telemetry pipelines, each with trade-offs in complexity, performance, and flexibility.
In the simplest case, telemetry is sent directly from a client to a SaaS provider. This approach is easy to implement but offers limited visibility and control. Another common model involves writing telemetry to local files and using agents like Fluent Bit or Logstash to ship the data to a central location.
In containerized environments, telemetry is often sent to a sidecar container responsible for buffering and shipping, which keeps application images lightweight and decouples telemetry logic. More advanced setups involve queuing or streaming systems, such as Kafka or Pulsar, which provide durability and backpressure handling. These systems allow telemetry to be ingested, processed, and routed by dedicated worker services at scale.
Challenges and Pitfalls
One of the most common pitfalls in telemetry systems is the unintentional exposure of sensitive data. Exception messages, stack traces, or unfiltered inputs can leak personally identifiable information (PII) into logs and traces. This not only increases the risk of data breaches but can also introduce legal and compliance concerns, especially when data is retained for long periods.
Another frequent issue is high cardinality, which occurs when there are too many unique values in telemetry labels or fields. For example, including a user ID in every metric label can dramatically increase memory usage and slow down queries. Over time, this can overwhelm your storage backend, cause ingestion delays, and lead to unpredictable costs. It’s crucial to apply aggregation and filtering strategies early in the design of your telemetry schema.
Conclusion
Telemetry systems are a critical enabler of modern observability. They provide the signals that help you monitor performance, investigate issues, ensure security, and make informed engineering decisions.
Happy engineering!