Observability Engineering : Deep Dive #2 - Observability vs Monitoring

Observability vs. Monitoring: Understanding the Key Differences between the two approaches to gaining insights into your systems

In today's complex and dynamic technological landscape, traditional monitoring approaches fall short of providing comprehensive insights into system behavior and performance.

This is where observability engineering steps in, revolutionising how we understand and monitor modern software systems.

In this blog post, we delve into the world of observability engineering, highlighting its significance, key differences from monitoring, benefits, challenges, and practical integration strategies.

Introduction to Observability Engineering

Observability is the degree to which the internal state and behaviour of a system can be inferred from its external outputs.

In other words, it's about gaining insights into what's happening within a system based on its observable external behaviours. Unlike monitoring, which focuses on collecting predefined metrics, observability is about uncovering the unknown unknowns and understanding the system's holistic behaviour. It goes beyond simple metrics to encompass logs, traces, events, and other contextual data.

Observability engineering is the practice of collecting and analyzing telemetry data from your systems in order to gain insights into their behaviour. This data can be used to identify problems before they cause outages, to understand the impact of changes to your systems, and to make better decisions about how to improve them.

Understanding Observability

Observability engineering, encompassing the analysis of logs, metrics, and traces, has become an indispensable practice in ensuring the reliability and optimal performance of software systems. By delving deep into these pillars of observability, IT professionals can unlock valuable insights, detect anomalies, and proactively address potential issues.

Granular log analysis allows engineers to extract meaningful information from system logs, unveiling patterns and anomalies that may impact system performance. Techniques such as log aggregation, parsing, filtering, and correlation empower them to gain a comprehensive understanding of system behavior.

Quantitative metrics provide standardized measurements of system performance, resource utilization, and user behavior. By leveraging metrics like latency, throughput, error rates, and resource utilization, observability engineering enables data-driven decision-making, optimization, and alignment with business objectives.

Traces and distributed tracing offer a holistic view of system interactions, tracing the path of requests across services and microservices. Detailed traces empower engineers to identify latency issues and pinpoint bottlenecks, enabling them to optimize system performance and enhance the user experience. Specialized tools like OpenTelemetry, Jaeger, and cloud-based tracing frameworks provide the necessary infrastructure for effective tracing.

The true power of observability engineering lies in combining logs, metrics, and traces. This synergy offers a comprehensive understanding of system behavior, enabling engineers to detect patterns, anomalies, and potential issues before they escalate. By consolidating these three pillars, organizations can achieve high system reliability and performance optimization.

Understanding Monitoring

Monitoring is the process of collecting and analyzing data about a system to detect and prevent problems. Monitoring typically involves collecting metrics, such as CPU usage, memory usage, and network traffic. This data can be used to identify trends and patterns, which can help to identify potential problems before they cause outages or other disruptions.

In today's digital world, businesses rely on a variety of systems to operate. These systems can include servers, applications, networks, and more. To ensure that these systems are running smoothly and efficiently, it is important to monitor them.

Monitoring systems can help you to:

  • Identify the metrics to collect. The first step in the monitoring process is to identify the metrics that you want to collect. Metrics are quantitative measurements of a system's performance. Common metrics include CPU usage, memory usage, network traffic, and response times.
  • Collect the data. Once you have identified the metrics to collect, you need to collect the data. This can be done using a variety of tools, such as monitoring agents, cloud monitoring services, and application performance monitoring (APM) tools.
  • Analyze the data. Once you have collected the data, you need to analyze it. This can be done using a variety of tools, such as dashboards, alerts, and reports.
  • Take action. Once you have analyzed the data, you need to take action. This may involve taking steps to prevent problems from occurring, or taking steps to fix problems that have already occurred.

The monitoring process is an ongoing process. You need to continuously collect, analyze, and take action on data to ensure that your systems are healthy and reliable.

Here are some additional tips for monitoring your systems:

  • Set up alerts. Alerts can help you to identify potential problems before they cause outages or other disruptions. When a metric exceeds a threshold, an alert can be sent to you or to a team of administrators.
  • Create reports. Reports can help you to track the performance of your systems over time. Reports can also help you to identify trends and patterns that may indicate a problem.
  • Use a monitoring tool that is scalable. As your systems grow, you need to make sure that your monitoring tool can scale to collect and analyze data from your growing systems.
  • Use a monitoring tool that is flexible. You need to make sure that your monitoring tool can be customized to meet the specific needs of your systems.

Differences between Observability and Monitoring

Monitoring and observability are complementary approaches to managing and maintaining software systems.

By using both monitoring and observability, you can get a complete picture of the health of your systems and take proactive steps to prevent problems and improve performance.

Here are some tips for implementing a monitoring and observability strategy:

  • Start with the basics. Don't try to do too much too soon. Start with a small set of metrics and logs, and gradually expand your data collection as you gain experience.
  • Use the right tools. There are a number of tools available to help you collect, store, and analyze monitoring and observability data. Choose tools that are well-suited for your specific needs.
  • Get buy-in from stakeholders. Monitoring and observability are a team effort. Make sure that you have the support of key stakeholders, such as developers, engineers, and operations staff.

Monitoring and observability are essential for managing and maintaining software systems in today's complex and ever-changing environment.

Feature Monitoring Observability
Purpose Detect and prevent problems Understand the state of a system
Data collected Metrics Metrics, logs, traces
Analysis Alerts, trends, patterns Root cause, performance, reliability
Benefits Identify and fix problems before they cause outages Improve performance, reliability, security


Observability engineering has emerged as a game-changing approach in the field of system monitoring, offering comprehensive insights into the behavior and performance of modern software systems. This blog post explores the significance of observability engineering, highlighting its key differences from traditional monitoring practices and presenting the benefits, challenges, and practical integration strategies associated with this approach.

Observability goes beyond traditional monitoring by focusing on capturing and analyzing the internal state and behavior of a system through the use of telemetry data, including logs, metrics, and traces. By leveraging these pillars of observability, engineers gain a deep understanding of system behavior and performance, enabling faster troubleshooting, proactive issue detection, and improved system resilience.

In contrast, monitoring is a subset of observability that concentrates on collecting predefined metrics to ensure system health. While monitoring provides specific insights into predefined metrics, observability enables engineers to explore the unknown unknowns and gain a holistic view of system behavior.

By integrating observability and monitoring, organizations can achieve a comprehensive approach to system monitoring. The blog post discusses practical strategies for integrating these approaches effectively and highlights the importance of selecting appropriate tools and technologies.

In conclusion, observability engineering represents a paradigm shift in system monitoring, providing deep insights into system behavior and performance. By embracing observability and integrating it with monitoring practices, organizations can drive operational excellence and ensure the reliability and performance of their modern software systems.