Telemetry Glossary: In-Depth Guide to Terms, Architecture, and Usage

Telemetry

Telemetry is the automatic measurement and remote transmission of data from distributed sources to a centralized location for monitoring, analysis, and control. Originating from the Greek tele (remote) and metron (measure), telemetry enables the collection of operational parameters from sensors or software agents embedded in machinery, vehicles, or digital platforms.

Telemetry underpins mission-critical systems across aviation, aerospace, IT infrastructure, energy, healthcare, and beyond. It operates through a chain: sensors or agents measure physical or digital phenomena (temperature, speed, voltage, system performance), transmit data over various channels (RF, satellite, cellular, Ethernet), and deliver it to a central system for storage, visualization, and automated response.

In aviation, telemetry is vital for real-time aircraft health monitoring, predictive maintenance, and regulatory compliance. ICAO and ARINC standards ensure interoperability and data integrity, defining rigorous requirements for aviation telemetry systems.

Sensor

A sensor is a device that detects and responds to physical phenomena—such as temperature, pressure, acceleration, humidity, or magnetic fields—and converts these into signals for measurement and transmission.

Sensors are the foundation of telemetry, interfacing the physical world with digital monitoring platforms. In aviation, sensors monitor critical parameters like airspeed (pitot tubes), altitude, engine performance (vibration, EGT), and environmental conditions. Modern sensors often include built-in diagnostics, analog-to-digital conversion, and must meet stringent standards for accuracy and reliability (ICAO, ARINC).

Software Agent

A software agent is a lightweight, autonomous program running on servers, endpoints, or embedded devices to collect, preprocess, and transmit digital telemetry—such as CPU utilization, memory consumption, network metrics, logs, and traces. Software agents enable real-time health monitoring in IT and avionics, and are often certified for use in regulated environments. Agents may aggregate, filter, and securely transmit data to centralized observability platforms.

Metric

A metric is a quantitative measurement tracked over time—such as CPU usage, memory consumption, network latency, engine RPM, or environmental temperature. Metrics are typically stored in time-series databases (TSDBs) and support threshold-based alerting, trending, and capacity planning. Aviation metrics include altitude, rate of climb, cabin pressure, and more.

Event

An event is a discrete, timestamped occurrence representing a significant change or action—such as user logins, system errors, configuration changes, or hardware failures. Events are logged with metadata and support forensic analysis, incident response, and compliance auditing.

Log

A log is a record of actions, status changes, and operational messages generated by systems, applications, or devices. Logs are essential for debugging, auditing, and security analysis, and are often ingested, parsed, and indexed by platforms like ELK Stack or Splunk. Aviation logs (FDR/QAR) are critical for post-flight analysis and accident investigation.

Trace

A trace tracks the end-to-end path of a request or transaction across multiple components, revealing dependencies, timing, and failure points. Traces are vital in distributed systems (microservices, IMA avionics) for root-cause analysis and performance optimization. Standards like OpenTracing and OpenTelemetry define portable trace formats.

MELT (Metrics, Events, Logs, Traces)

MELT stands for Metrics, Events, Logs, and Traces—the four pillars of telemetry data. Together, they offer a holistic view of system health and behavior, enabling proactive monitoring, troubleshooting, and optimization. MELT is foundational for observability platforms and is recommended by organizations like CNCF and OpenTelemetry.

Time-Series Database (TSDB)

A Time-Series Database (TSDB) is optimized for storing and querying telemetry data indexed by time. TSDBs (e.g., Prometheus, InfluxDB) handle high-velocity metric streams, support retention policies, aggregation, and fast retrieval for real-time and historical analysis—crucial for aviation and industrial monitoring.

Data Lake

A data lake is a centralized repository for storing structured and unstructured telemetry data at scale. Data lakes support raw ingestion (sensor streams, logs, binary files) and flexible analytics, machine learning, and compliance reporting. Effective data governance is essential, especially in regulated industries like aviation.

Protocol

A protocol defines rules for encoding, transmitting, and interpreting telemetry data. Examples include:

• MQTT: Lightweight, for IoT/remote sensors.
• HTTP/HTTPS: Web-based telemetry and APIs.
• Modbus, OPC UA: Industrial automation.
• gRPC: High-performance, microservices.
• SNMP: Network device monitoring.
• ARINC 429/615A, ACARS, ADS-B: Aviation standards.

Protocol selection depends on bandwidth, latency, reliability, security, and compliance.

Observability

Observability is the ability to infer a system’s internal state from its external outputs (metrics, events, logs, traces). Observability platforms aggregate and analyze MELT data, enabling teams to answer “What is happening?”, “Why?”, and “How to remediate?”. In aviation, observability supports continuous safety assurance and proactive risk management.

Monitoring

Monitoring is the continuous measurement and alerting on system health and performance indicators derived from telemetry. Monitoring platforms (e.g., Prometheus, Nagios) provide dashboards, alerts, and integration with incident workflows. In aviation, robust monitoring is mandated for flight safety and compliance.

Predictive Maintenance

Predictive maintenance uses telemetry and analytics to forecast equipment failures, enabling proactive intervention. By analyzing trends in vibration, temperature, or error rates, predictive models estimate the remaining useful life of components. Aviation fleets use predictive maintenance to reduce downtime and optimize repair schedules.

Security Information and Event Management (SIEM)

SIEM platforms aggregate and analyze security telemetry (logs, alerts, events) for real-time threat detection and response. SIEM supports compliance, rapid incident response, and integration with broader observability pipelines. Aviation SIEM systems monitor IT and operational systems for cyber threats, aligning with ICAO cybersecurity standards.

Data Transmission

Data transmission in telemetry involves encoding and sending measured data to a central hub using RF, satellite, cellular, Ethernet, or serial links. Aviation telemetry uses protocols like ARINC 429, ACARS, and ADS-B, with redundancy and encryption for security and reliability. Channel selection depends on range, bandwidth, and regulatory requirements.

Data Processing

Data processing encompasses cleansing, normalizing, enriching, and validating telemetry before storage or analysis. Processing ensures data accuracy, timeliness, and integrity—critical for regulatory compliance and operational trust. Advanced pipelines may apply machine learning for anomaly detection or predictive analytics.

Data Storage

Data storage refers to mechanisms for persistently storing telemetry data—ranging from onboard memory to cloud or on-premises databases. Storage systems must support high ingestion rates, compression, retention policies, and compliance with regulations (e.g., ICAO mandates for FDR data retention). Tiered storage optimizes performance and cost.

Visualization

Visualization transforms telemetry into dashboards, charts, and maps for rapid situational awareness and decision-making. In aviation, visualization powers operations centers and cockpit displays. Best-in-class tools support interactive analysis, alerting integration, and intuitive design for operational safety.

Alerting

Alerting is the automated notification of abnormal conditions detected in telemetry streams. Alerts can be sent via email, SMS, dashboards, or incident platforms, and may trigger automated remediation. Effective alerting balances sensitivity and specificity, and is mandated by aviation safety standards.

Redundancy

Redundancy is the duplication of critical telemetry components (sensors, channels, storage) to ensure continuous data flow in case of failures. Aviation systems employ dual/triple redundancy for sensors, communication links, and storage, with automatic failover to meet stringent reliability requirements.

Encryption

Encryption applies cryptographic techniques to protect telemetry data in transit and at rest, ensuring confidentiality and integrity. Telemetry encryption uses protocols such as TLS/SSL (network) and AES (storage). Regulatory frameworks (ICAO, GDPR) mandate encryption for sensitive aviation and operational data.

Conclusion

Telemetry is the data backbone of mission-critical operations—delivering real-time insights, enabling safety and compliance, and powering analytics and automation. By understanding key concepts like sensors, metrics, MELT, protocols, observability, and security, organizations across aviation, IT, and industry can unlock new levels of performance, reliability, and innovation.

For expert guidance on telemetry architectures, platform integration, or compliance, contact our team or schedule a demo .