Telemetry involves the automatic gathering, processing, and transmission of data from distant or inaccessible points to a central location for analysis and monitoring.
A nurse in a hospital is too busy to watch every patient every minute. She relies on telemetry to monitor vital signs, such as blood pressure, and receive alerts if a patient's condition worsens.
Telemetry systems automatically collect data from sensors attached to a patient, a jet engine, or an application server. The system then sends that information to a central site for performance monitoring and to identify problems before they become emergencies.
Telemetry was developed to measure industrial, scientific, and military data from remote locations, including tracking how a missile performed in flight or the temperatures inside a blast furnace. In IT and security, telemetry data monitors metrics such as application downtime, database errors, or network connections. This data is the raw material for observability, the practice of understanding how well applications and services are working and how users interact with them.
How does telemetry work?
When telemetry monitors physical objects, it uses sensors that measure characteristics such as temperature, pressure, or vibration. When telemetry monitors IT systems, software agents gather digital data about performance, uptime, and security, then send that data to collectors that process it and transmit it for storage or analysis.
Telemetry data appears in multiple formats depending on the agent that produced it, so it must be normalized, or made to fit a standard structure, before any analytic tool can use it. Historically, that normalization happened through a schema-on-write process, which required knowing the required format in advance and enforcing that schema before the data was logged. That approach is no longer viable given the volume, variety, and velocity of data produced by today's IT infrastructures. A more practical approach is schema-on-read, which applies structure to the data at query time instead of locking it in upfront.
Types of telemetry data
What telemetry actually tells you depends on the system being tracked and how the data gets used.
For servers, the data might include how close processors and memory are to being overloaded.
For networks, it might be latency and bandwidth.
For applications and databases, it might be uptime and response time.
Telemetry designed to detect attacks may track the number of incoming requests to a server, changes to the configuration of an application or server, or the number and type of files being created or accessed.
Telemetry data generally comes in three forms:
Metrics. Numeric data, such as the amount of time it takes to process a request, the number of incoming requests to a server, or the number of failed requests.
Logs. Timestamped, structured or unstructured records of discrete events, capturing what happened, when, and in what context.
Traces. The path a transaction takes across infrastructure components (applications, databases, networks) and services (search engines, authentication mechanisms), useful for pinpointing exactly where a slowdown or failure occurred.
See our post on the pillars of observability for more detail.
How is telemetry used?
Telemetry data gives teams a real-time view of application performance, so they can perform root cause analysis, prevent bottlenecks, and identify security threats. For security monitoring, unusual network traffic patterns might indicate a denial-of-service attack. Unusual requests from an unknown application or repeated failed login attempts may signal an attempted breach.
Telemetry also tracks how users interact with applications and systems. That behavioral data helps teams improve user interfaces and test whether tweaks to an application or website increase engagement or sales. It can help reduce costs by identifying and eliminating underused assets, such as cloud servers no longer in use, or by helping plan infrastructure budgets based on usage trends.
Telemetry from Internet of Things devices tracks shipments and enables preventive equipment maintenance. It can also support business models in which a company sells performance, maintenance, or production data from equipment in the field.
Ways telemetry is used
Telemetry is used to monitor application performance in real time by tracking response times, CPU usage, memory consumption, and throughput. Continuous collection and analysis help teams detect bottlenecks quickly, enabling faster troubleshooting and improving the user experience.
Telemetry supports security by collecting data on network traffic, user behavior, and system logs so teams can detect anomalies that indicate malicious activity. It enables alerts for unauthorized access attempts or unusual data transfers, and it provides logs for forensic investigations of events before and during an attack.
Telemetry helps optimize resource use by continuously gathering data on server capacity, bandwidth, and storage, which supports informed decisions about scaling infrastructure, identifying underutilized resources, and anticipating capacity constraints.
Telemetry also reveals user behavior patterns, showing where users struggle or encounter friction, and informing interface design, feature development, and product changes.
Continuous monitoring of telemetry lets teams track error rates, system crashes, and hardware failures in real time, enabling faster responses to potential failures and improving system reliability.
Drawbacks and challenges of telemetry
Modern IT infrastructures generate very large data streams in a variety of formats, and not all of it is critical or useful. System administrators and other IT staff can be overwhelmed by the volume, and storage costs can climb quickly.
Teams must decide what data matters most and how to transmit, format, and analyze it. Every transmission method has tradeoffs. Sending telemetry data directly from the monitored application removes the need for additional software, but if that application is complex and generates a lot of data, sending it could bog down the application or network being monitored.
Teams also need ways to control the cost of storing telemetry data. One option is to land all of it in a data lake, retrieving only what's needed for analysis when it's needed. Another challenge is gathering and analyzing information from older devices and applications that were not built to support modern telemetry, like networks that still report performance and health data using the Simple Network Management Protocol. Finding and deploying analytical tools, including AI and machine learning capable of sifting through terabytes of data to surface the incidents and trends that deserve attention, is another challenge.
Give your telemetry a pipeline, not a firehose
Telemetry only creates value when it reaches the right destination, in the right shape, without draining your budget. A vendor-agnostic observability pipeline is designed to route, transform, and reduce telemetry so the data arriving at analytical tools is useful and cost-effective. Cribl Stream includes integrations between more than 80 pairs of data sources, tools, and data stores, and it converts data from one format to another on the fly so it is ready for real-time analytics when it arrives.
Teams can add new data sources, like data lakes, and new destinations, like AI analytics tools, through a drag-and-drop interface, without re-architecting the pipeline each time requirements change. Cribl Stream has been tested at volumes of more than 20 petabytes per day, so it scales with large telemetry loads, and built-in monitoring helps confirm the right data is reaching the right place. For long-term, cost-effective storage, Cribl Lake offers full-fidelity retention, and Cribl Search lets teams query that data where it sits instead of rehydrating everything into an analytics tool first. For telemetry generated at the edge, such as Kubernetes nodes, cloud instances, or remote offices, Cribl Edge collects data at the source to ensure it is captured before it enters the pipeline.
The move to the cloud, mobile-first computing, and distributed workforces has made IT infrastructure more critical and more complex. Manual troubleshooting cannot handle this scale; telemetry is a required step toward observability, which helps organizations monitor and maintain service quality for customers, employees, and business partners. To avoid rising costs, organizations should control how they collect, route, and store telemetry so it supports operational goals rather than becoming an added expense.
Telemetry FAQs
What is the difference between telemetry and monitoring?
While both telemetry and monitoring involve observing system performance, telemetry focuses on collecting and analyzing data from remote sources. Monitoring often involves setting up alerts and thresholds to detect anomalies, while telemetry provides a deeper understanding of the underlying system behavior.
How can telemetry help improve application performance?
Telemetry data can be used to identify performance bottlenecks, optimize resource allocation, and pinpoint the root causes of issues. By understanding how applications are behaving in real-time, organizations can take proactive steps to improve performance and user experience.
What are the challenges associated with implementing telemetry?
One of the main challenges is managing the large volumes of data generated by telemetry systems. Organizations must carefully consider data storage, processing, and analysis to extract valuable insights while minimizing costs. Additionally, integrating telemetry with existing systems and tools can be complex, requiring technical expertise.
How can telemetry be used to enhance security?
Telemetry can help detect security threats by monitoring network traffic, user behavior, and system logs for anomalies. By identifying unusual patterns or suspicious activities, organizations can proactively address security vulnerabilities and prevent breaches.
