Observability vs. Telemetry vs. Monitoring

Observability, telemetry, and monitoring are crucial for efficient and reliable software development and deployment. While these terms are related, they have distinct meanings and functions. This article explains clearly the differences between observability, telemetry, and monitoring and how they collectively contribute to system reliability. By understanding their key differences and recognizing their collective value, you can leverage them to gain deeper insights into your systems and ensure optimal application performance and resilience.

What is Telemetry?

Telemetry is an automated process of remotely collecting and transmitting data using sensors and communication systems. It focuses on data collection, so it does not provide analysis or insights but depends on monitoring tools to analyze collected data. Telemetry gathers various metrics—such as performance statistics, error rates, and resource utilization—to gain insights into system behavior.

Types of Telemetry

Two main types of telemetry and the data types they collect are discussed below.

Application Telemetry

This type involves adding code or instrumentation to an application to gather and transmit data about its behavior. OpenTelemetry is a commonly adopted application telemetry tool.

In OpenTelemetry, the SDK is used for initialization, while an API is used for code instrumentation. Instrumentation allows the OTel tool to gather performance, event, and resource utilization-related metrics, pooled using SDKs, and transport to application performance monitoring(APM) tools for analysis. Here are some examples of data collected through application telemetry.

Application Telemetry Data	Description
Performance Metrics	Provide insights into system performance, such as response times, latency, throughput or error rates. Help assess the efficiency of and bottlenecks within the system.
Resource Utilization Metrics	Allows for monitoring of systems' resource utilization, such as CPU usage, memory consumption or network bandwidth, to ensure resource allocation efficiency and optimization.
Event Tracking Metrics	Tracks specific system events, including user actions and API calls.

Network Telemetry

Network telemetry helps to monitor and optimize network behavior by collecting data related to network conditions and performance, such as bandwidth usage, speed, and other network KPIs. Network telemetry can be collected actively (by routinely stimulating the system) or passively (by recording system events and collecting runtime data). Some examples of network telemetry include:

Network Telemetry Data	Description
Traffic Volume	Measures network traffic flowing through different network components. Network telemetry helps prevent security threats by monitoring and analyzing network load. This data can be collected via software probes, network taps or other network monitoring tools/techniques.
Latency	Captures time taken for data or requests to travel across networks. Network telemetry involves checking for signs of software latency such as recurrent delays or inconsistent spikes in application response times. Key network performance indexes in this category include jitter, packet delay variation (PDV) and round trip time (RTT).
Packet Loss	Identifies issues that may impact data integrity or system performance by monitoring the rate at which network packets are lost during transmission. KPIs related to packet loss include packet loss rate, burst packet loss, out-of-order packets and retransmission rates.

What is Observability?

Observability is the ability to understand and infer the internal state of a system based on its external behavior. This external behavior includes the kinds of external outputs that the data systems generate and the latency of data generation. It involves collecting and analyzing data from various sources, including metrics, logs, and traces, to gain insights into system behavior and performance. An observable system is easier to manage, debug, and scale as it focuses on end-to-end user experience and provides actionable insights about IT systems.

Observability platforms provide a comprehensive view of multi-cloud software deployment environments and cloud-native applications that help DevOps teams perform swift root cause analysis and correlation in a way that goes beyond simple monitoring. It also provides teams with inputs so that they can establish automation pipelines. This allows for effective and proactive troubleshooting, optimization, and problem-solving, covering all use cases.

Types of Telemetry in Observability

The following are the types of telemetry commonly used in observability tools.

Metrics

Metrics provide quantitative measurements of system performance and behavior. They include data points such as response times, error rates, CPU usage, memory utilization, and network throughput. Metrics-driven telemetry provides values that help IT teams recognize and resolve abnormal system performance for improved application health and performance.

Logs

Logs capture specific events or activities within a system. They provide detailed information about system behavior, capturing error messages, user actions, application events, and other information. Logs also provide contextual information about metrics. With Log-based telemetry, SRE and DevOps teams can see the exact time an event occurred, why it occurred, and which other events are connected to it, making it helpful in troubleshooting and investigating performance issues.

Traces

Traces follow the flow of requests or transactions through different system components. They can track how the application or network components work together or flow across networks to deliver the desired end results. These telemetry data provide detailed insights into the execution path and timing of distributed application requests. Traces enable you to understand dependencies, analyze performance bottlenecks and identify issues that may span multiple components.

Metrics, logs, and traces are three pillars of observability, and they work together to provide a comprehensive understanding of system behavior in the case of microservices. Metrics provide a high-level overview of system performance, logs offer detailed event-based information, and traces provide root cause visibility into distributed systems.

What is Monitoring?

Monitoring is actively observing and measuring an IT infrastructure system's health and performance to ensure it functions as expected. Monitoring involves setting up predefined checks and alerts to detect potential failures and deviations from expected behavior. However, it usually does not provide the same depth and flexibility as observability. Prometheus is a widely regarded open-source tool as the de-facto monitoring tool in the industry, known for its robust data collection, querying, and alerting capabilities.

Types of Monitoring

In any monitoring exercise, different types of monitoring can be used individually or in combination based on specific requirements and goals. Below are some common types of monitoring.

Performance Monitoring

Performance monitoring tracks, measures, and analyzes critical application performance metrics. These include response times, error and request rates, throughput, and resource utilization (CPU and memory) metrics. Performance monitoring thus helps to ensure optimal system performance by identifying potential bottlenecks.

Infrastructure monitoring

Infrastructure monitoring is pivotal for Site Reliability Engineering (SRE) and operations teams, ensuring systems run efficiently and reliably. It provides real-time data, enabling SREs to identify and address potential system bottlenecks or failures promptly. By leveraging these insights, they can uphold their commitments to service level objectives (SLOs) and maintain a balance between innovation and stability. This proactive approach helps SRE teams maintain system resilience, ensuring seamless user experiences. This becomes even more critical in the case of serverless or Kubernetes-based applications where infrastructure is ephemeral.

Availability Monitoring

The most critical software monitoring metric, availability or uptime monitoring, focuses on determining if a system or its components are available and responsive. It involves monitoring factors such as uptime, network connectivity, response status codes, and overall availability of critical functionalities. Availability monitoring can be done using a cloud HTTP or a ping sensor. It helps to detect and address system availability and connectivity issues promptly.

Log Monitoring

Log monitoring involves continuously analyzing system logs (as recorded) to identify patterns, anomalies, and potential issues. Logs provide detailed records of system events and errors, allowing monitoring tools to detect and alert on abnormal behavior.

Real-Time Monitoring

Real-time monitoring involves continuous and immediate data analysis in its current state when a command is issued or a request is made. It detects and responds to events or issues in real time. It involves real-time analytics and alerting systems to capture and analyze real-time data.

Security Monitoring

Security monitoring detects and mitigates potential security threats and vulnerabilities in real-time. It involves monitoring system logs, network traffic, and user behavior to identify anomalies, unauthorized access attempts, or suspicious activities with in-application runtime security.

Capacity Monitoring

Capacity monitoring involves tracking and analyzing resource usage over time to allocate system resources optimally. It includes monitoring metrics such as CPU, memory, disk space, and network bandwidth utilization. Capacity monitoring also provides insights into resource usage variation over time by measuring data processing speed, latency, and volume. It helps DevOps teams to identify resource constraints, improve capacity planning, and optimize system performance.

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Observability vs. Monitoring vs. Telemetry

Differentiators	Observability	Monitoring	Telemetry
Purpose	Understand system internals and behavior	Track system performance and health	Measure and collect data for analysis
Focus	Systems’ current and future internal states	Overall system performance and metrics	Data collection and transmission
Scope	Comprehensive and deep understanding	Specific metrics and events	Data gathering for various purposes
Data	Rich, granular and contextual data	Aggregated data and metrics	Raw and processed data for analysis
Analysis	Exploratory and ad-hoc analysis	Rule-based or threshold-based detection	Statistical analysis and trend prediction
Flexibility	Adaptable, allows for dynamic exploration	Rigid, predefined metrics for monitoring	Flexible, can measure various parameters
Complexity	Addresses complex distributed systems	Primarily focused on individual components	Generalized, can be applied broadly
Tools and Methods	Log analysis, distributed tracing	Alerting systems and dashboarding	Remote sensing, data collection tools

While each concept is essential, it is important to note that none individually guarantees system reliability. They complement each other, and a comprehensive approach should include all three. Telemetry provides real-time data, monitoring helps detect specific issues, and observability facilitates analysis of collected data to find root causes, understand system behavior, and optimize performance.

Now, let us consider how each differs from the other two.

How is Telemetry Different from Observability?

Telemetry involves collecting and transmitting data across diverse systems in multi-cloud environments for onward analysis by another tool. On the other hand, Observability is the ability to understand a system's internal state and behavior based on available data. It encompasses the collection, analysis, and interpretation of telemetry data, allowing for the troubleshooting and exploration of complex systems. As such, while telemetry tools allow for versatile data collection, standardization, and measurement, observability offers essential insights into why a system has an issue and how it can be resolved.

How is Telemetry Different from Monitoring?

Monitoring is impossible without telemetry data, and telemetry cannot—on its own—offer comprehensive insights into application performance. Telemetry measures and collects real-time data related to performance, usage, errors, and other relevant KPIs. This data is then available to monitoring tools that actively track and alert system health, performance, and availability.

Another key differentiator is that, unlike Telemetry, which offers robust metrics across multi-cloud environments, traditional monitoring tools rely on developers to pre-specify metrics to be tracked.

How is Observability Different from Monitoring?

Observability provides a detailed view of system events, while monitoring offers a comprehensive view of overall system health. Observability allows DevOps teams to follow data trails from end users’ requests to system response, allowing for exact root cause analysis and efficient remediation. On the other hand, monitoring tools continuously observe, collect, and aggregate data related to system performance using predefined metrics, making monitoring specific metrics and components practical.

How to Choose the Right Observability and Monitoring Tools

Choosing the right tools for telemetry depends on several factors, including your specific requirements, the nature of the system or application being monitored, and the goals of your telemetry implementation. Here are some checks to guide you in selecting the appropriate tools.

Criteria	Description
Telemetry Requirements	Determine what data you need to collect, monitor and analyze. Consider the types of metrics, logs, events and traces that are relevant to your system. Identify the telemetry goals, such as performance optimization, error detection or capacity planning.
System Architecture and Data Sources	Have a clear understanding of your system architecture. Identify where the telemetry data is generated, such as servers, network devices or applications. Consider data storage capacity, storage costs and possibilities for long-term storage or archiving. Check if the telemetry tool supports efficient querying and retrieval of historical data for analysis and reporting.
Compatibility and Integration	Assess the compatibility of telemetry tools with your existing infrastructure, programming languages, frameworks and databases. Evaluate if the tools can monitor various systems (i.e. cloud environments, virtual machines and containers). Also, check if the tools integrate seamlessly with your application stack or monitoring systems. If observability is a key requirement, choose a telemetry tool that integrates well with third-party observability solutions. The tools must also support the required data formats, protocols or APIs.
Scalability and Performance	Ensure the solution can collect telemetry data efficiently from various sources and handle large volumes of data. Check for features like load balancing, clustering, or distributed architecture, especially if your deployments are on a large scale.
Ease of Use and Flexibility	The tool must be easy to configure, deploy and manage. It must enable you to add custom metrics or extend functionality through API integration or plugins.
Analytics and Visualization Capabilities	Assess the tools’ data analysis and visualization features to determine if they offer flexible querying and advanced analytics via dashboards, charts and reports.
Security and Compliance	Ensure that the telemetry tools meet your organization's and industry's security and compliance requirements. Double-check for encryption and access control.
Vendor Reputation and Future Roadmap	Research the reputation and track record of the vendor; evaluate their experience, dig up customer reviews and assess their commitment to product development (feature enhancement is important).
Support and Community	Evaluate the support options—such as documentation, forums, or ticket-based support—provided by the vendor. Consider the tools’ size and user community engagement, as it can provide valuable insights into the solutions’ support structure.

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The Future of Telemetry

The increasing need for data-driven insights has continued to drive data collection, analysis, and visualization advancements. As systems become more complex and cloud environments more distributed, telemetry will play an increasingly crucial role in enabling better monitoring, observability, and overall system reliability.

Combining telemetry with artificial intelligence (AI) and machine learning (ML) will allow advanced analytics and improve anomaly detection and predictive capabilities. AI algorithms can swiftly and accurately identify patterns, detect abnormalities, and make predictions for improved system performance and proactive maintenance. These involve more sophisticated visualization techniques and interactive dashboards to provide developers with intuitive and user-friendly insights from telemetry data, facilitating more accessible analysis, troubleshooting, and decision-making.

The future of observability and monitoring tools holds immense potential for leveraging advanced technologies in collecting, analyzing, and utilizing data for improved system performance, optimization, and decision-making across various industries and domains.

Conclusion

Observability, telemetry, and monitoring are vital for ensuring system reliability. Achieving system reliability requires a comprehensive approach, incorporating design principles, fault tolerance mechanisms, and proactive maintenance practices.

With the three working in tandem, you can gather comprehensive and issue-focused telemetry data, monitor overall application health to alert on nonspecific software issues, and conduct deeper root cause analysis of more complicated issues based on system output. These will provide faster anomaly detection and ensure improved system behavior while reducing application downtimes and improving business profits.