What Is Network Telemetry? 

Network telemetry is the process of collecting, measuring, and analyzing data from network devices and traffic to gain insights into network performance, health, and security. This data provides visibility into what’s happening on the network, enabling IT professionals to monitor, troubleshoot, and optimize network operations, which helps in identifying potential issues like packet loss or congestion before they impact users.

How it works:

  • Data collection: Network devices like switches, routers, and servers generate data from various sources, including logs, flow records, SNMP traps, and packet captures. 
  • Data transmission: This collected data is sent to a central repository or a network management system. 
  • Analysis and visualization: Monitoring tools and software analyze this data to identify patterns, issues, and trends, often presenting it in a clear, visual format for easier interpretation.
  • Passive vs. active telemetry approaches: The data being monitored can include real traffic and events or synthetic (generated) data.

With the increasing complexity and scale of modern networks, especially in environments spanning cloud, data center, and edge, telemetry has become indispensable. The shift toward telemetry reflects a broader industry movement toward data-driven operations and observability, providing granular and actionable insights.
This is part of a series of articles about network monitoring.

How Network Telemetry Works 

Data Collection

Data collection forms the bedrock of network telemetry, beginning at the device level. Routers, switches, firewalls, and endpoints are instrumented with software or embedded hardware probes that extract operational metrics, state changes, traffic statistics, and event logs. These collectors can capture both instantaneous snapshots and time-series data, depending on configuration and network requirements.

Accurate data collection depends on both the breadth and granularity of captured metrics. Devices use telemetry protocols or programmable interfaces to access internal counters and system variables. 

Data Transmission

Once collected, telemetry data must be transmitted from network elements to centralized management and analysis platforms. Efficient data transmission requires careful encoding to minimize bandwidth usage without compromising fidelity. Modern telemetry solutions utilize protocols that support compressed streaming, batching, and sampling to avoid overwhelming production traffic or storage backends.

Transmission mechanisms vary by environment and need. Some organizations use secure, low-latency protocols over dedicated control channels to transport telemetry datasets regularly or in near real time. 

Analysis and Visualization

Collected and transmitted telemetry data only becomes actionable through analysis and visualization. Telemetry platforms ingest raw datasets and correlate them using algorithms, machine learning, and policy-driven rules. Analysis can reveal patterns in performance degradation, route instability, utilization spikes, or security threats that would be difficult to discern with static monitoring.

Visualization tools turn complex telemetry streams into dashboards, charts, and alerts that operators can readily interpret. Effective visualization helps identify bottlenecks, forecast capacity needs, detect anomalies, and enable root-cause analysis.

Passive vs. Active Telemetry Approaches

Network telemetry can be classified into passive and active approaches, each with distinct use cases and considerations. Passive telemetry observes existing traffic or device events without injecting additional packets; examples include flow export protocols (like NetFlow or sFlow) or log-based reporting. This method is less invasive and avoids interfering with production data, but it may provide less detail about path performance or application-layer issues.

Active telemetry involves generating synthetic traffic or explicit probes to measure metrics such as latency, jitter, and packet loss. Tools like active ping and path testing inject traffic frames or probes specifically designed to assess the state of particular segments. While more detailed, active methods require careful orchestration to avoid overloading networks or distorting normal operational traffic.

Benefits of Network Telemetry Tools

Network telemetry tools provide deep, continuous visibility into network operations, helping organizations move from reactive to proactive network management. By automating data collection and analysis, these tools reduce manual effort and improve the accuracy and speed of decision-making.

Key benefits include:

  • Real-time visibility: Continuous streaming of telemetry data enables operators to monitor the network in real time, detect anomalies early, and respond to incidents faster than with traditional polling methods.
  • Improved troubleshooting and root-cause analysis: Detailed, time-aligned data helps correlate events across layers and devices, making it easier to isolate the root cause of performance issues or outages.
  • Scalability across complex environments: Telemetry supports large-scale, distributed networks, including cloud, edge, and hybrid environments, without overwhelming management systems, due to efficient data handling and streaming protocols.
  • Proactive performance optimization: Trends and patterns in telemetry data allow for predictive analytics, helping teams forecast congestion, capacity needs, or equipment failures before they impact services.
  • Enhanced security monitoring: Fine-grained data from flows and device states enables better detection of anomalies, lateral movement, or policy violations, supporting threat detection and response efforts.
  • Reduced operational overhead: Automation in data collection and correlation minimizes the need for manual checks, freeing up resources for higher-value tasks and reducing mean time to resolution (MTTR).
  • Customizable monitoring and alerting: Telemetry tools can be tuned to focus on specific metrics, paths, or applications, enabling targeted monitoring and context-rich alerts tailored to operational needs.

Common Protocols and Standards for Network Telemetry 

SNMP and Its Limitations

The Simple Network Management Protocol (SNMP) has long served as the standard for network device monitoring, enabling centralized platforms to poll devices for status, counters, and configuration data. SNMP’s widespread adoption and maturity make it nearly ubiquitous, with vast tool support and broad vendor compatibility. Using Management Information Bases (MIBs), SNMP can poll for device status, interface statistics, and error rates.

However, SNMP has significant limitations for telemetry use cases in modern networks. It operates on a pull-based (polling) paradigm, introducing scalability and latency bottlenecks as networks grow. The protocol also struggles with high-frequency data collection and lacks sufficient granularity, particularly for real-time, event-driven monitoring. Security shortcomings in earlier SNMP versions further restrict its suitability for sensitive or high-assurance environments.

sFlow, NetFlow, and IPFIX

sFlow, NetFlow, and IPFIX are foundational flow-based telemetry protocols, widely used to collect traffic summary data from switches and routers. sFlow employs statistical sampling to provide scalable and efficient measurements, capturing packet headers and interface counters at periodic intervals. This approach is well-suited for high-speed networks, offering low overhead but less granular visibility into individual flows.

NetFlow, pioneered by Cisco, and its IETF standard derivative IPFIX, operate by exporting flow records, summarizing source, destination, port, protocol, and byte/packet counts. While NetFlow typically collects entire conversation records, IPFIX extends the model to support customizable fields and vendor-independent interoperability. Both protocols provide deeper traffic visibility but may introduce higher processing and transmission costs compared to sFlow.

NETCONF, RESTCONF, and YANG Push

NETCONF and RESTCONF are modern network management protocols that use standardized data models (primarily YANG) to read or modify device configuration and operational state. NETCONF leverages XML encoding over SSH, offering transaction support and fine-grained access control, making it suitable for programmable network operations. RESTCONF provides similar capabilities using RESTful APIs and lighter JSON encoding.

YANG Push extends NETCONF and RESTCONF by allowing devices to stream telemetry updates to collectors, rather than relying solely on polls or SNMP traps. With YANG Push, clients subscribe to specific data sets, and network devices asynchronously send updates upon state changes or on a recurring schedule. This enables event-driven telemetry, greatly increasing measurement frequency and reducing client-side polling burdens.

gNMI and Streaming Telemetry

The gRPC Network Management Interface (gNMI) is a protocol designed for programmatic network management and telemetry based on the gRPC framework. gNMI enables both configuration management and high-efficiency streaming telemetry, drastically reducing overhead and supporting flexible subscription models. Devices can push continuous updates or periodic snapshots to collectors, supporting monitoring and anomaly detection.

Streaming telemetry with gNMI uses structured data models, often based on YANG, and binary encoding for compact, high-speed transmission. This architecture supports large-scale, real-time observability across dynamic, cloud-era environments where traditional polling fails to scale. gNMI’s ecosystem is rapidly expanding, with broad vendor support and integration into next-generation network management platforms.

In-Band Network Telemetry (INT/IOAM)

In-band Network Telemetry (INT) and In-situ Operations, Administration, and Maintenance (IOAM) techniques embed telemetry metadata directly inside live data packets as they traverse the network. Instead of relying on external collectors or sampling, each packet accumulates telemetry information, such as hop latency, packet loss, or queue depth, at every device along its path. 

INT and IOAM face operational challenges, such as compatible hardware/software support and potential impacts on packet sizes and forwarding performance. Still, for key use cases like data center spine-leaf fabrics, multi-cloud overlays, and real-time applications, in-band telemetry provides unmatched insights into network behavior, enabling microsecond-accurate troubleshooting, congestion management, and path validation.

Use Cases of Network Telemetry in Modern Networks 

Real-Time Traffic Optimization

Through continuous monitoring of link utilization, flow patterns, and application-level performance, network telemetry supports real-time traffic engineering to avoid congestion and improve resource allocation. Automated systems can rebalance workloads, reroute traffic, or adjust quality-of-service (QoS) policies midstream, minimizing packet loss and delay while maximizing throughput.

This real-time optimization is especially crucial in multi-tenant cloud environments, hybrid integrations, or highly dynamic enterprise networks. By leveraging telemetry-driven analytics, operators can align resource provisioning to real demand, ensuring optimal user experiences and operational cost efficiency, even as network conditions change.

Microburst and Congestion Detection

Microbursts (brief surges in network traffic) can cause excessive packet loss, jitter, or delay, particularly in data center and high-frequency trading environments. Traditional monitoring tools often miss these fleeting events because of their low polling frequency. Network telemetry enables the capture of high-resolution, time-series traffic and buffer data, making it possible to identify microbursts as they occur.

Armed with this insight, operations teams can correlate microbursts with specific flows or workloads and implement targeted rate-limiting, traffic shaping, or buffer adjustment measures. By addressing these transient congestions in real time, organizations can improve application reliability and reduce performance degradation in latency-sensitive environments.

Intrusion and Anomaly Detection

Network telemetry is foundational to modern network security, particularly for intrusion detection and anomaly identification. By continuously analyzing flow records, logs, and protocol-specific metrics, security platforms can establish baselines for normal behavior, quickly detecting deviations that may signal attacks, malware, or insider threats.

With access to fine-grained telemetry, security teams can pivot from broad reactive defenses to proactive threat hunting. Automated analytics and machine learning models can rapidly flag suspicious traffic patterns or unauthorized device activity, reducing dwell time and accelerating incident response in increasingly complex threat landscapes.

IoT and Edge Device Monitoring

The proliferation of IoT and edge computing devices has amplified the need for detailed, distributed telemetry. Lightweight agents and streaming telemetry protocols allow organizations to monitor health, connectivity, and activity across thousands or millions of geographically dispersed endpoints, detecting failures, resource constraints, or potential security breaches before they escalate.

Edge environments, often bandwidth- or power-constrained, benefit from telemetry solutions that support efficient data encoding and selective reporting. This approach ensures that IoT networks remain resilient, secure, and manageable at scale, powering applications from industrial automation to healthcare and smart cities.

Cloud-Native and Hybrid Infrastructure Visibility

Modern IT environments increasingly span on-premises data centers, public clouds, and hybrid infrastructures. Network telemetry provides essential visibility across these heterogeneous landscapes, unifying operational data from legacy hardware, cloud-native platforms, and virtualized network functions (VNFs). This holistic visibility is necessary for enforcing consistent performance, compliance, and security policies wherever workloads run.

With end-to-end telemetry integration, organizations can trace packets and flows across interconnects, containers, and API gateways. This supports rapid root-cause analysis, cost allocation, policy enforcement, and automated remediation; key capabilities for digital businesses prioritizing uptime, customer experience, and security in dispersed environments.

Challenges in Deploying Network Telemetry

There are several issues that may impact the effectiveness of network telemetry monitoring.

Scalability and Performance Overhead

Telemetry inherently generates vast quantities of data, particularly in large-scale or high-frequency environments. As device and link counts grow, the stress on network bandwidth, CPU/memory resources, and backend storage intensifies. Without careful design, telemetry collection and transmission can introduce unacceptable performance overhead, or destabilize production workloads during peak reporting periods.

Data Governance and Quality Assurance

Collecting telemetry data at scale introduces risks related to data quality, accuracy, and governance. Unreliable or inconsistent telemetry may lead to faulty conclusions, missed incidents, or compliance violations. Ensuring completeness, timeliness, and consistency across multi-vendor or hybrid environments is an ongoing challenge.

Managing Large Volumes of Telemetry Data

Storing and processing telemetry streams from modern networks often results in petabytes of data. Without effective data lifecycle management, costs and complexity can escalate rapidly. Traditional relational databases may lack the performance or scale for real-time ingestion and query, necessitating purpose-built time-series databases and big data analytics platforms.

Ensuring Telemetry Data Security and Privacy

Network telemetry, by its nature, exposes detailed operational, configuration, and sometimes user-specific data. If not properly secured, this data becomes a target for attackers seeking to understand or disrupt organizational operations. Risks include interception, tampering, and unauthorized access, all of which can have serious business and regulatory consequences.

Best Practices for Implementing Network Telemetry 

Organizations should consider the following practices to ensure the best use of network telemetry data.

1. Define Clear Telemetry Goals and KPIs

Implementing telemetry should begin with a precise definition of objectives and key performance indicators (KPIs). Clear goals, such as reducing MTTR, detecting intrusions faster, or ensuring bandwidth SLA compliance, help guide the selection of protocols, data models, and reporting intervals. This early alignment prevents scope creep and ensures that telemetry efforts provide tangible value for network operations, security, or business stakeholders.

Tying data collection and visualization to KPIs allows for continuous performance assessment and improvement tracking. As network environments evolve, revisiting goals and metrics ensures ongoing relevance, maximizing the return on telemetry investments and supporting agile responses to new operational or regulatory challenges.

2. Choose Efficient Encoding and Transport Mechanisms

The choice of encoding and transport protocols directly impacts telemetry efficiency. Formats like Google Protocol Buffers (protobuf) or JSON can significantly reduce message size, while transport protocols like gRPC or message queuing (Kafka, MQTT) improve fault tolerance and real-time streaming. Tailoring encoding and transport to network conditions and data criticality minimizes latency, conserves bandwidth, and improves end-to-end system reliability.

Organizations should benchmark protocol overhead against their environment and select options tailored to device capabilities and operational requirements. Leveraging standards-based solutions aids interoperability, while adaptive encoding (such as dynamic field selection or compression) further optimizes the trade-offs between detail, timeliness, and resource consumption.

3. Ensure End-to-End Data Encryption and Security

Protecting telemetry data from interception, tampering, or unauthorized disclosure is essential. End-to-end encryption, including TLS for node-to-node transport and at-rest encryption for storage, blocks eavesdropping and ensures data integrity. Strong authentication and authorization mechanisms, such as mutual TLS certificates and fine-grained API access controls, further limit data exposure to approved users and systems.

Security best practices also require continuous monitoring for abnormal access patterns, audit logging for traceability, and periodic security assessments for configuration drift or emerging risks. Compliance frameworks may require integrating telemetry security controls with broader IT governance, ensuring consistent enforcement of privacy and data protection requirements.

4. Centralize Storage, Analysis, and Visualization

Managing telemetry at scale is greatly simplified by centralizing data storage and analytics on unified platforms. Centralization supports real-time correlation across device types and geographic regions, reducing operational silos and simplifying root-cause analysis. Scalable time-series databases or data lakes underpin high-volume ingestion and on-demand analytics, enabling granular insights and long-term capacity planning.

Centralized dashboards and visualization tools allow stakeholders, from network engineers to business analysts, to monitor KPIs and trends from a single source of truth. Customizable alerting and automated reporting further support rapid, data-driven decision-making across IT and operations teams, improving service health and organizational agility.

5. Automate with AI/ML for Real-Time Insights

Manual data analysis quickly becomes impractical as telemetry scales. Artificial intelligence (AI) and machine learning (ML) automation can rapidly identify trends, correlate events, and predict or prevent problems before users are impacted. ML models detect baseline deviations, forecast capacity constraints, and classify anomalies far faster and at greater scale than human operators.

Integrating AI/ML into telemetry platforms enables closed-loop remediation, automatically triggering corrective actions, scaling resources, or escalating incidents based on real-time observations. Continuous learning and model refinement, paired with explainable analytics, ensure that automation aligns with evolving infrastructure risks and operational goals.

6. Integrate Telemetry into Observability Stacks

Network telemetry is most valuable when integrated into broader observability architectures, encompassing metrics, logs, and traces. Full-stack observability improves cross-domain root-cause analysis and enables unified views of user experience, application performance, and infrastructure health. Tools like OpenTelemetry or vendor-specific observability suites standardize data ingestion and normalization, simplifying integration efforts.

A unified observability approach helps break down barriers between network, application, and security teams, promoting collaborative troubleshooting and holistic optimization. Sharing telemetry-driven insights and automating workflows across teams builds organizational resilience, accelerates innovation, and supports seamless management.

Related content: Read our guide to network observability

Network Telemetry with Selector.ai

Selector extends traditional network telemetry by unifying data collection, enrichment, and analysis across multi-vendor and hybrid environments. The platform ingests a wide range of telemetry — including NetFlow, gNMI, SNMP, syslog, and OpenTelemetry — and harmonizes it into a consistent model for real-time correlation and analysis.

Selector’s AI-driven correlation engine links telemetry from devices, applications, and infrastructure to uncover root causes and performance dependencies that would otherwise go unseen. With features like high-frequency streaming telemetry, a Digital Twin for historical replay, and natural-language Copilot for intuitive queries, Selector helps teams transform raw telemetry data into actionable insights that drive faster troubleshooting, better automation, and greater operational efficiency.

Learn more about how Selector’s AIOps platform can transform your IT operations.