Telemetry

What is Telemetry?

Telemetry is the automated process of collecting, transmitting, and analyzing data from remote or distributed sources to a central system for purposes such as monitoring, analysis, optimization, and decision-making. Telemetry allows organizations to observe the health, performance, and usage of physical devices, software applications, infrastructure, and user interactions—often in real time and across highly distributed environments.

Etymology: The term “telemetry” derives from the Greek tele (remote) and metron (measure), literally signifying “remote measurement.” Originally developed for industrial automation and scientific research, telemetry is now fundamental to IT, AI infrastructure, cloud operations, and cybersecurity.

How Telemetry Works: Step-by-Step

1. Data Collection

Sensors (hardware or software agents) gather raw data from endpoints—these can be servers, IoT devices, applications, or network devices. In IT, software-based collectors (agents or SDKs) are deployed within code or as sidecar processes, capturing metrics such as CPU utilization, API response times, and user interactions.

2. Data Conversion and Formatting

Raw readings are digitized and structured into standardized formats—commonly JSON, Protocol Buffers (protobuf), or the OpenTelemetry Protocol (OTLP). Metadata is attached, including timestamps, source IDs, environment tags, and context.

3. Data Transmission

Data is securely transmitted to a central system using protocols such as HTTP, gRPC, MQTT (for IoT), SNMP (for network devices), or OTLP for observability pipelines. Transmission modes include real-time streaming or batched intervals, depending on latency and resource requirements.

4. Data Storage

Received telemetry is ingested into databases, data lakes, or time-series databases (TSDB). Storage infrastructure often enforces data retention policies, applies encryption, and implements tiered storage to balance cost and performance.

5. Data Analysis and Visualization

Analytical tools and observability platforms (such as Grafana, Splunk, New Relic, or Datadog) process, aggregate, and visualize telemetry data. Teams leverage dashboards and alerting systems to identify trends, anomalies, and optimize system behavior.

Analogy: Telemetry in IT is akin to a medical patient monitor: vital signs are continuously recorded and displayed in real time, allowing clinicians (engineers or admins) to respond rapidly to changes.

Types of Telemetry Data

1. Metrics

Definition: Quantitative, numeric time-series measurements reflecting system health and performance

Examples: CPU/memory usage, request latency, error rates, disk I/O, throughput

Use Case: Triggering an alert when memory usage exceeds 90% for a sustained period

2. Events

Definition: Discrete, timestamped occurrences representing significant system state changes or actions

Examples: User logins, deployments, payment failures, configuration changes

Use Case: Logging all failed authentication attempts for security analysis

3. Logs

Definition: Textual or structured records providing a chronological account of system and application activity

Examples: Application error logs, access logs, system restarts, stack traces

Use Case: Investigating an outage by correlating error logs around the incident time

4. Traces

Definition: End-to-end records of individual transactions or requests as they traverse distributed systems, capturing context and causality

Examples: Tracing a user request through microservices, database queries, API calls

Use Case: Diagnosing latency bottlenecks in a multi-service checkout workflow

5. User Telemetry

Definition: Data on user interactions and engagement with digital products

Examples: Clicks, navigation flows, feature usage, session durations

Use Case: Prioritizing product development based on feature adoption metrics

6. Network Telemetry

Definition: Data from network devices and traffic flows

Examples: Packet loss, bandwidth utilization, port status, device uptime

Use Case: Identifying abnormal traffic spikes indicative of potential DDoS attacks

7. Security Telemetry

Definition: Data focused on the security posture and threat surface of systems

Examples: Firewall logs, intrusion detection events, endpoint alerts, authentication attempts

Use Case: Real-time threat hunting and incident response

8. Application Telemetry

Definition: Metrics and events specific to application operations and lifecycle

Examples: Deployment events, exception rates, database access metrics, DevOps pipeline status

Use Case: Monitoring application health during rollouts to detect regressions early

9. Cloud Telemetry

Definition: Insights into cloud resources, configurations, and operational performance

Examples: VM health, serverless function invocations, storage activity, cost analytics

Use Case: Optimizing cloud resource allocation and spend

10. IoT Telemetry

Definition: Data from Internet of Things devices, often in industrial or environmental settings

Examples: Temperature readings, GPS coordinates, device battery status, environmental sensors

Use Case: Predictive maintenance of industrial equipment

Telemetry in IT and AI Infrastructure

Observability, Monitoring, and Telemetry: How They Differ

Telemetry supplies the raw data—metrics, events, logs, traces, and more

Monitoring leverages telemetry to assess predefined indicators (e.g., CPU spikes, latency), often with alerting

Observability is the overarching practice of inferring system state and diagnosing issues through comprehensive telemetry, even for unknown or novel failure modes

Key Frameworks and Standards

OpenTelemetry (OTel): Open source, vendor-agnostic standard for telemetry data collection, processing, and export. OTel supports traces, metrics, and logs, and enables instrumentation via SDKs in multiple languages.

OpenTelemetry Protocol (OTLP): The wire protocol for telemetry data, supporting gRPC and HTTP, with protobuf payloads and configurable compression

OpenTelemetry Collector: Proxy for ingesting, processing, and exporting telemetry data

Prometheus: Leading open-source metrics collection and alerting toolkit, widely used for infrastructure and application monitoring

Grafana: Visualization platform supporting time-series data from multiple sources

Example: A SaaS provider uses OpenTelemetry to instrument hundreds of microservices, exporting metrics, traces, and logs to a centralized observability backend (e.g., Grafana or Splunk). This enables real-time dashboards, automated alerting, and rapid root-cause analysis.

Benefits of Telemetry

Continuous Performance Monitoring: Enables always-on visibility into system health, performance, and user experience

Predictive Maintenance: Detects trends and anomalies for proactive remediation, reducing downtime (e.g., identifying disks likely to fail)

Enhanced Security: Surfaces suspicious activity and compliance gaps (e.g., alerting on repeated failed logins)

Data-Driven Decisions: Provides actionable insights on resource utilization, feature adoption, and operational efficiency

Optimized User Experience: Highlights friction points for workflow improvements (e.g., slow user journeys)

Cost Optimization: Identifies resource waste, informs scaling strategies, and controls cloud spend

Industry-Specific Examples

Healthcare: Remote patient monitoring, early anomaly detection

Automotive: Vehicle diagnostics, fleet management

Finance: Fraud detection, compliance monitoring

Retail/E-commerce: Cart abandonment analytics, personalized recommendations

Cloud/SaaS: Resource optimization, uptime guarantees

AI/ML: Model drift monitoring, inference latency

Challenges and Considerations in Telemetry

Data Privacy & Compliance: Telemetry may capture sensitive information. Compliance with GDPR, CCPA, HIPAA, and other data protection frameworks is mandatory. Mitigation: Anonymize or pseudonymize data, restrict access, and audit pipelines.

Data Volume & Scalability: High-frequency telemetry can overwhelm storage and processing capabilities. Mitigation: Apply sampling, aggregation, retention policies, and discard non-essential data.

Legacy System Integration: Older devices/software may lack modern telemetry support. Mitigation: Use adapters or upgrade legacy endpoints incrementally.

Data Quality & Governance: Incomplete or noisy data undermines analytics. Mitigation: Enforce schemas, validate inputs, and maintain integrity checks.

Storage, Bandwidth, and Cost: Large telemetry datasets can incur significant costs. Mitigation: Use tiered/compressed storage, and tune sampling/intervals.

Security Risks: Telemetry may be a target for attackers. Mitigation: Encrypt data in transit and at rest, monitor access, and audit regularly.

Step-by-Step Implementation: Deploying Telemetry in IT Environments

1. Identify Requirements

Define what you aim to achieve (e.g., “Which features are least used?”). Determine required metrics, events, and data sources.

2. Instrument Systems

Deploy agents, SDKs, or sensors in applications, infrastructure, or IoT endpoints. Follow best practices for low overhead and privacy compliance.

3. Establish Data Pipelines

Configure secure data transmission (e.g., OTLP/gRPC, HTTP, MQTT). Integrate with message queues/streaming if required for scale.

4. Set Up Data Storage

Select appropriate storage (time-series DB, data lake, warehouse). Define retention/archival policies.

5. Analyze and Visualize

Use dashboards, alerting systems, and analytics platforms for actionable insights. Build custom or prebuilt observability dashboards.

6. Iterate and Optimize

Review telemetry, refine collection methods, and address data gaps. Audit for privacy, security, and data quality.

Practical Tools and Further Reading

Key Frameworks and Platforms

OpenTelemetry: Standardized, open source

Prometheus: Metrics and alerting

Grafana: Visualization

Splunk: Enterprise analytics

New Relic: Cloud observability

Datadog: Cloud monitoring

Frequently Asked Questions (FAQs)

How does telemetry differ from monitoring and logging?
Telemetry encompasses all types of system data collection and transmission. Monitoring utilizes telemetry to track system health and trigger alerts. Logging is a specific telemetry type focused on detailed event records.

Is telemetry data always real time?
No. Telemetry can be streamed in real time or delivered in batches, based on system needs.

How is privacy maintained in telemetry?
Through data anonymization, minimization of sensitive data, encryption, and compliance with regulations (GDPR, CCPA).

Which protocols are typically used for telemetry?
HTTP, gRPC, MQTT (IoT), SNMP (network), and OTLP (OpenTelemetry).

Is telemetry applicable outside IT/software?
Yes—telemetry is used in healthcare, automotive, energy, logistics, and more.

Summary Table: Telemetry at a Glance

References

• Splunk: What is Telemetry?
• IBM: What is Telemetry?
• TechTarget: What is Telemetry?
• Proofpoint: What is Telemetry?
• OpenTelemetry Documentation
• Edge Delta: What is Telemetry Data?
• OpenTelemetry: What is OpenTelemetry?
• OpenTelemetry: OTLP Specification
• OpenTelemetry: Collector
• OpenTelemetry: Metrics
• OpenTelemetry: Logs
• OpenTelemetry: Traces
• OpenTelemetry: Instrumentation
• Grafana
• Splunk
• New Relic
• Datadog
• Prometheus
• Practical OpenTelemetry (Book)
• Learning OpenTelemetry (O’Reilly)