Monitoring, Observability, and Telemetry Explained
Tags: monitoring, devops, observability
Published: 2024-04-02 19:39:44

This article was originally posted on [EverythingDevOps](https://everythingdevops.dev/monitoring-observability-and-telemetry-explained/).

Whenever something goes wrong in a system or application, it affects the customers, the business performance, and of course, the Site Reliability Engineers (SREs) who stay up late to fix it.

While engineers often use terms/tools such as monitoring, observability, and telemetry interchangeably, they aren't the same things. Monitoring sets triggers and alerts that go off when an event occurs, Telemetry gathers system data, and [Observability](https://everythingdevops.dev/what-is-observability/) lets you know what's going on in the system.

The trio is responsible for quickly restoring downtime within organizations; however, to utilize them effectively, you need to understand their significant differences and how they work together. By the end of this article, you will have a clear understanding of how each concept works and how they can be combined to ensure your system is reliable.

## **What is Monitoring?**

  
After deploying an application, to ensure that there are little to no downtimes, several post-deployment practices are put in place. One of such practices is monitoring.  

[Monitoring](https://www.bmc.com/blogs/it-monitoring/) can be defined as the continuous assessment of a system's health and performance to ensure that it is running as expected. With monitoring, you don't have to wait for a customer to report an issue before you know that something is wrong. Its triggers and alerts are set to go off when an event occurs.

In monitoring, you define what "normal" means for your system and set up alerts to notify you when the system deviates from that normal state. For example, you could set up an alert to notify you when the CPU usage of your server exceeds 80%. This way, you can take action before the server crashes.

Monitoring not only gives you an insight into the health of your system but also helps you to identify trends and patterns in the system. This helps you know how the application has been used over time and makes it easier to predict future issues.

## **Types of Monitoring**

Although the concept of monitoring stays the same, its application differs. This difference is due to the diverse nature of IT environments, the complexity of systems, varied use cases, and emerging technologies and trends.

There are different types of monitoring, each designed to monitor a specific aspect of the system. Some of these types include:

**Application Performance Monitoring (APM):** APM focuses on monitoring the performance and availability of software applications. It involves tracking metrics related to response times, throughput, error rates, and resource utilization within the application. [APM tools](https://newrelic.com/lp/developersignup?utm_medium=cpc&utm_source=google&utm_campaign=EVER-GREEN_BRAND_SEARCH_BRAND_EMEA_MENA_EN&utm_network=g&utm_keyword=new%20relic&utm_device=c&_bt=665547843535&_bm=e&_bn=g&cq_cmp=12301448307&cq_con=117946549135&cq_plac=&gad_source=1&gclid=Cj0KCQiAz8GuBhCxARIsAOpzk8wmVY4Vs4zLCDpkqoN0HX0MoSXvEv2IHgkmY6dA18Gbi6emHTIecCEaAiItEALw_wcB) provide real time insights into application bottlenecks, code inefficiencies, and user experience issues.

**Infrastructure Monitoring:** This type of monitoring focuses on the health and performance of physical and virtual infrastructure such as servers, networks, and storage. It includes monitoring the CPU utilization, memory usage, disk space, and network bandwidth to ensure optimal performance.

**Network Monitoring:** It involves monitoring the performance of network infrastructure and traffic. It includes the monitoring of devices such as routers, switches, firewalls, and load balancers, as well as the analysis of network traffic patterns, packet loss, latency, and bandwidth utilization. Network monitoring helps ensure optimal network performance, troubleshoot connectivity issues, and detect security threats.

**Log Monitoring:** Log monitoring involves collecting, analyzing, and correlating log data generated by systems, applications, and devices. It helps organizations track system events, troubleshoot issues, and ensure compliance with regulatory requirements. Log monitoring tools provide centralized log management, real-time alerting, and advanced analytics capabilities to facilitate log analysis and investigation.

**Security Monitoring:** This type of monitoring focuses on detecting and responding to security threats and vulnerabilities. It involves monitoring network traffic, system logs, and user activities to identify suspicious behaviour, unauthorized access, and security breaches. Security monitoring tools provide real-time threat detection, incident response, and security analytics capabilities to help organizations protect their sensitive data.

**Cloud Monitoring:** Cloud monitoring involves monitoring the performance and availability of cloud-based infrastructure and services. It includes monitoring cloud resources such as virtual machines, storage, databases, and containers, as well as tracking cloud service metrics provided by cloud service providers (CSPs). Cloud monitoring helps organizations optimize cloud resource utilization, manage costs, and ensure service uptime and reliability.

**End-User Monitoring (EUM):** This type of monitoring focuses on tracking the experience of end-users interacting with applications and services. It involves measuring metrics such as page load times, transaction completion rates, and user interactions to assess application performance from the end-user perspective. EUM tools provide insights into user behaviour, application performance, and service availability to help organizations deliver a seamless end-user experience.

## **What's the Problem with Legacy Monitoring?**

The term "legacy" refers to outdated systems, technologies, or practices that are no longer efficient or effective. Legacy monitoring is the traditional approach to monitoring that is no longer sufficient for modern IT environments.

The problem with legacy monitoring is that it is not designed to handle the complexity, scale, and dynamism of modern systems and applications. Legacy monitoring tools are often siloed, inflexible, and unable to provide the level of visibility and insights required to manage modern IT environments effectively.

Some of the challenges associated with legacy monitoring include:

**Lack of Scalability**: Legacy monitoring systems often lack scalability to handle the growing volume, variety, and velocity of data generated by modern IT infrastructure and applications. As organizations scale their operations and adopt new technologies, legacy systems may struggle to keep up, leading to performance degradation and gaps in monitoring coverage.

**Limited Visibility**: Legacy monitoring tools provide limited visibility into the complex interactions and dependencies within modern IT ecosystems. They often focus on monitoring individual components in isolation, rather than providing holistic insights into system behavior and performance across the entire technology stack. This limited visibility hampers the ability to detect and diagnose issues that span multiple layers of the infrastructure.

**Limited support and updates**: Legacy monitoring systems are often inflexible and difficult to adapt to changing business requirements, technologies, and use cases. They may lack support for modern deployment models such as cloud computing, containerization, and microservices, making it challenging to monitor dynamically orchestrated environments effectively.

**Inadequate Security Monitoring**: Legacy monitoring systems may lack robust security monitoring capabilities to detect and respond to cybersecurity threats effectively. As cyberattacks become more sophisticated and targeted, organizations require advanced security monitoring tools that can analyze large volumes of security data in real time and provide actionable insights to mitigate risks.

## **A Historical Overview of Monitoring and Observability**

Since the advent of computing technology and IT management practices over the past decade, monitoring and observability have also evolved. During the early days of computing, primitive monitoring tools focused on hardware components, paving the way for more sophisticated monitoring tools during the mainframe era.

With the popularity of computer networks, network management protocols like [SNMP](https://en.wikipedia.org/wiki/Simple_Network_Management_Protocol) became available, enabling administrators to monitor and manage network resources. The growth of enterprise IT environments, platforms like [HP OpenView](https://en.wikipedia.org/wiki/HP_OpenView) and [IBM Tivoli](https://www.ibm.com/docs/en/tivoli-monitoring/6.3.0?topic=introduction-overview-tivoli-monitoring) emerged, providing centralized monitoring and management capabilities for diverse infrastructures.

In the 2000s, as web-based applications and distributed architectures became increasingly popular, [Application Performance Management (APM)](https://www.dynatrace.com/news/blog/what-is-apm-2/) solutions emerged, monitoring software applications and user experiences.

In 2010, cloud computing and DevOps practices revolutionized IT operations, introducing new challenges and opportunities for monitoring and observability. The development of cloud-native monitoring and DevOps tools enabled organizations to monitor dynamic environments using features such as infrastructure-as-code and containerization. Additionally, observability emerged, emphasizing the importance of understanding system behaviour from rich telemetry and contextual information.

In this way, observability platforms provided insights into complex systems that allowed troubleshooting, debugging, and optimization to be easily accomplished. From early hardware monitoring to modern observability platforms, IT infrastructure and applications have been continually monitored and controlled to gain greater visibility, insight, and control.

## **Why Observability?**

Observability refers to the ability to understand and analyze the internal state of a system based on its external outputs or telemetry data. Observable systems allow users to find the root cause of a performance issue by inspecting the data they generate.

By implementing telemetry-producing tools in systems, you can gather comprehensive data about the system's interactions, dependencies, and performance. Through correlation, analysis, visualization, and automation, observability platforms provide meaningful insights based on this data.

## **Differences between Observability and Monitoring**

The distinction between observability and monitoring lies in their focus, approach, and objectives within IT operations.

Monitoring primarily involves the systematic continuous collection and analysis of predefined metrics and thresholds to track the health, performance, and availability of systems, applications, or services. It operates on the principle of setting triggers and alerts to signal deviations from expected behaviour, enabling reactive management and troubleshooting.

Observability, on the other hand, shifts the focus from predefined metrics to gaining a deeper understanding of system behaviour and interactions. It emphasizes the need for rich, contextual data and insights into the internal state of a system, captured through telemetry data. Observability enables organizations to explore and analyze system behaviour in real time, facilitating troubleshooting, debugging, and optimization efforts.

While monitoring focuses on predefined metrics to track system health and performance, observability emphasizes the need for telemetry data of the system's behaviour to facilitate deeper understanding and analysis. Monitoring is reactive, while observability is proactive, empowering organizations to detect, diagnose, and resolve issues more effectively in complex distributed systems.

## **A Brief Intro to Telemetry Data**

[Telemetry](https://www.splunk.com/en_us/blog/learn/what-is-telemetry.html) data refers to the collection of measurements or data points obtained from remote or inaccessible systems, devices, or processes. This data typically includes information about the operational status, performance metrics, logs, and behaviour of these systems.

The role of telemetry data is to provide insights into how systems operate, how they interact with each other, and how they perform over time. By analyzing telemetry data, organizations can gain a deeper understanding of their systems, identify patterns, detect anomalies, and make informed decisions to optimize performance, enhance reliability, and troubleshoot issues effectively.

## **Comparisons and Relationships**

Monitoring focuses on tracking the health and performance of systems through predefined thresholds. It typically involves collecting metrics related to system resource utilization, response times, error rates, and other key performance indicators (KPIs). Monitoring provides a high-level view of system health and performance, alerting operators to potential issues or deviations from expected behaviour.

Telemetry focuses on collecting data such as logs, metrics, and traces from systems, especially in dynamic cloud environments. While telemetry provides robust data collection and standardization, it lacks the deep insights needed for quick issue resolution.

Observability goes beyond telemetry by offering analysis and insights into why issues occur. It provides detailed, granular views of events in an IT environment, enabling custom troubleshooting and root cause analysis.

APM, while similar to observability, focuses specifically on tracking end-to-end transactions within applications. It offers high-level monitoring of application performance but may not provide the technical details needed for deep analysis.

Overall, telemetry and APM provide monitoring capabilities while observability offers deeper insights into system behaviour and performance, enabling effective troubleshooting and analysis across IT infrastructure.

## **The Transition from Monitoring to Observability**

Imagine a company that operates an e-commerce platform. In the past, they could rely solely on traditional monitoring tools to track the health and performance of their system. These tools would provide the basic metrics such as CPU usage, memory utilization, and response times for their web servers and databases.

However, as their platform grows in complexity and scale, they might frequently encounter issues that traditional monitoring tools can no longer address adequately. For example, during a high-traffic event like a flash sale, they could notice occasional spikes in latency and occasional errors occurring in their checkout process. While they can see these issues occurring, they will struggle to pinpoint the root cause using traditional monitoring alone.

To address these challenges, this company must adopt observability practices. By implementing telemetry solutions that collect a broader range of data, including detailed logs, [distributed traces](https://aws.amazon.com/what-is/distributed-tracing/#:~:text=A%20distributed%20trace%20represents%20the,the%20initial%20request%20interacts%20with.), and custom application metrics. With this richer dataset, they'll gain deeper insights into their system's behaviour and performance.

They can now trace individual requests as they flow through their microservices architecture, allowing them to identify bottlenecks and latency issues more effectively. They can also correlate application logs with system metrics to understand how the changes in application code affect the overall system health.

By transitioning to observability, this company gains a more comprehensive understanding of its system's behaviour and performance. They can proactively identify and resolve issues, leading to improved reliability and a better user experience for their customers.

## **Choosing the Right Tool**

Choosing the right observability tool is crucial for ensuring the reliability of modern software systems. Below are key factors to consider when selecting an observability tool for your organization:

**Data Collection:** Look for a tool capable of collecting and aggregating data from various sources like logs, metrics, and traces - [the three pillars of observability](https://www.techtarget.com/searchitoperations/tip/The-3-pillars-of-observability-Logs-metrics-and-traces). Before choosing a tool, ask yourself, "Can this tool handle the diverse data sources present in our infrastructure, and can it process high volumes of data in real time?" Example tools include [Datadog](https://www.datadoghq.com/), [Splunk](https://www.splunk.com/), and [New Relic](https://newrelic.com/lp/developersignup?utm_medium=cpc&utm_source=google&utm_campaign=EVER-GREEN_BRAND_SEARCH_BRAND_EMEA_MENA_EN&utm_network=g&utm_keyword=new%20relic&utm_device=c&_bt=665547843535&_bm=e&_bn=g&cq_cmp=12301448307&cq_con=117946549135&cq_plac=&gad_source=1&gclid=CjwKCAiAibeuBhAAEiwAiXBoJG1KiG6xQarGQP4FS2TsxQjkF-c1EyG2FJ9Jp4hQMwR04LepDRoExxoCvEgQAvD_BwE).

**Visualization and Analysis:** Choose a tool with intuitive and customizable dashboards, charts, and visualizations. A question to ask is, "Are the visualization features of this tool user-friendly and adaptable to our team's specific needs?" Tools like [Grafana](https://grafana.com/) and [Kibana](https://www.elastic.co/kibana) provide powerful visualization capabilities.

**Alerting and Notification:** Select a tool with flexible alerting mechanisms to proactively detect anomalies or deviations from defined thresholds. Consider asking questions like "Does this tool offer customizable alerting options and support notification channels that suit our team's communication preferences?" A tool like [Prometheus](https://prometheus.io/) provides robust alerting capabilities.

**Scalability and Performance:** Consider the scalability and performance capabilities, especially if your organization handles large volumes of data or has a growing user base. Ask yourself, "Is this tool capable of scaling with our organization's growth and maintaining performance under increased data loads?" You can utilize a tool like [InfluxDB](https://www.influxdata.com/) for scalable time-series data storage.

**Integration and Compatibility:** Assess compatibility with your existing software stack, including infrastructure, frameworks, and programming languages. To make this assessment, ask yourself, "Does this tool seamlessly integrate with our current technologies and support the platforms we use, such as Kubernetes or cloud providers?" Most observability tools are open-source and support a wide range of integrations.

**Extensibility and Customization:** Evaluate the tool's extensibility to incorporate custom instrumentation and adapt to specific application requirements. Ask yourself, "Can we easily extend the functionality of this tool through custom integrations and configurations to meet our unique monitoring needs?"

By considering these questions, you can make a more informed decision when selecting an observability tool for your organization.

## **Conclusion**

This article has explored the vital aspects of observability and monitoring in modern IT operations. It covered the role of telemetry data, compared observability with related concepts, illustrated the transition from traditional monitoring to observability, and discussed key factors in selecting observability tools.

Throughout, we've emphasized the importance of balancing monitoring and observability for efficient IT operations, highlighting how a comprehensive approach can provide deep insights and enhance system reliability in today's dynamic digital landscape.

By understanding the differences between monitoring, observability, and telemetry, and by leveraging the right tools and practices, DevOps teams can gain a deeper understanding of their systems, proactively identify and resolve issues, and optimize performance to deliver a seamless user experience.