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What is Observability? An Overview

Observability is rapidly becoming a cornerstone in IT and cloud computing, enabling professionals to gain deeper insights into their systems. This guide offers an introduction to observability, shedding light on its importance and how it differs from traditional monitoring methods. We introduce the core concepts of observability, its practical applications, and its benefits for NetOps, DevOps, and SRE roles. Read on to explore how observability can be a game-changer in ensuring system efficiency and reliability.

What is Observability?

Observability, originating from control theory, is the capability to infer the internal state of a system solely based on its external outputs. In IT and cloud computing, observability encompasses more than just measuring system states. It taps into diverse data points, such as logs, metrics, and traces, generated by every component—from software and hardware to containers and microservices—across multicloud environments. The essence of observability is to decode the complex interactions among these components, in order to swiftly pinpoint and rectify anomalies, ensuring optimal system performance, reliability, and exceptional user experience.

For a deeper dive into observability’s specific application in networking, refer to our entry on network observability.

Addressing Core Pain Points with Observability

Every professional in the NetOps, DevOps, and SRE realms confronts recurring challenges: System uptime is non-negotiable, performance optimization is a constant endeavor, and rapid incident response is always in demand. So, how does observability address these pain points?

  • System Uptime: With observability’s granular insights, teams can proactively identify potential issues that might lead to system downtime. Observability tools can flag a minor problem, ensuring that uptime remains consistent before that small problem snowballs into a critical failure.

  • Performance Optimization: Observability goes beyond just identifying issues; it offers a comprehensive view of system performance. By understanding how different components interact and where potential bottlenecks lie, teams can continuously optimize for peak performance.

  • Rapid Incident Response: In the unfortunate event of an incident, time is of the essence. Observability equips teams with real-time data, allowing them to pinpoint the root cause swiftly and implement fixes, reducing the mean time to resolution (MTTR).

For professionals grappling with these challenges daily, observability isn’t just a tool—it’s a lifeline.

How does Observability Work?

In IT and cloud computing, observability represents the capability to deduce the internal status of a system from the external data it produces. This data encompasses logs, metrics, and traces. The essence of observability stems from telemetry, which is gleaned through active instrumentation of various components within multicloud environments. This instrumentation ensures that every piece of data—an event, a log, or a metric—is insightful and actionable. Through observability, teams can capture a comprehensive, real-time snapshot of system health, performance, and potential anomalies, facilitating proactive problem resolution and system optimization.


Logs are textual records generated by software components. They capture specific events or transactions at any given point in time. Logs are crucial in observability as they offer a chronological account of events, errors, or transactions. This makes them an invaluable resource for post-event analysis and debugging. Whether it’s an error thrown by a backend service or a transaction record in a database, logs provide the raw, detailed context that is often essential to understanding the nuances of a particular incident or anomaly.

Metrics and Telemetry

Metrics, synonymous with telemetry, are numerical data points measured over intervals. They can represent various system aspects, from CPU usage and memory consumption to API request rates. By analyzing these metrics, teams can identify trends, potential bottlenecks, and performance issues. During anomalies, metrics offer a detailed view of the system’s condition, enabling quick resolutions. These metrics act as benchmarks, highlighting system health and performance.

Distributed Tracing

In contemporary architectures, especially microservices, a single user request might traverse multiple services before completion. Distributed tracing allows tracking these requests as they propagate through various services, capturing latency data and identifying bottlenecks. Each trace provides a story—a journey of a request. By analyzing these journeys, teams gain insights into the interdependencies between services, ensuring efficient and optimal request handling.

User Experience

While system metrics and logs are invaluable, understanding how users interact with an application adds another layer to observability. User experience monitoring captures data about how real users access and interact with applications. This includes page load times, click paths, and user frustrations like repeated clicks. By integrating this user-centric data into the observability matrix, organizations can ensure their systems function optimally and resonate well with end-users, leading to enhanced satisfaction and retention.

Embracing Automation and Integration

The modern tech stack is a complex web of tools, platforms, and services. Manually monitoring each component is more than just tedious; it’s inefficient. This is where the power of automation, bolstered by observability, comes into play.

  • Automated Alerts: By setting specific criteria based on system behavior, observability tools can automatically generate alerts for anomalies, ensuring that teams are instantly aware of potential issues.

  • Integration with DevOps Tools: Observability platforms can seamlessly integrate with popular DevOps tools, creating an integrated ecosystem where data flows freely between platforms. Whether it’s CI/CD pipelines, configuration management, or orchestration tools, observability ensures every tool has the data it needs.

  • Streamlined Workflows: With automation, routine tasks like data collection, anomaly detection, and even some remedial actions can be automated, allowing professionals to focus on more complex tasks that demand their expertise.

In an era where automation is the key to efficiency, observability is the backbone, ensuring that automation is both fast and intelligent.

Enhancing Discoverability with Observability

In any complex IT ecosystem, visibility is paramount. But it’s not just about seeing what’s happening—it’s about understanding it. Observability doesn’t just provide raw data; it enhances the discoverability of intricate interactions within the system. Whether it’s the flow of data between microservices or the response time of an API, observability ensures that teams have clear visibility into every facet of their operations. This heightened discoverability is pivotal for proactive issue resolution and system optimization.

What is the Difference Between Observability and Monitoring?

The terms “observability” and “monitoring” are sometimes used interchangeably in the IT domain, but they represent distinct concepts, each with its unique value proposition and application. To grasp the nuances between the two and understand their interrelation, it’s essential to dive deeper into their respective definitions and roles.

Traditional Monitoring

Monitoring is the process of continuously checking predefined metrics or events. It’s inherently rule-based, meaning you set specific thresholds or conditions, and if the system breaches these thresholds, an alert is triggered. Think of monitoring as a security camera that triggers an alarm when someone enters a restricted zone. It’s highly effective for known issues but might not capture unforeseen problems or complex anomalies.

For a more in-depth discussion of the evolution of network monitoring, explore our article on network monitoring’s transition to network observability.


As discussed earlier, observability is the capability to understand a system’s internal state based on its external outputs without predefined queries or rules. It provides an open-ended exploration, allowing teams to ask any question about the system, even the unexpected ones. Observability equips teams with the tools to understand “why” something happened, not just “what” happened.

Key Differences between Observability vs Monitoring

  1. Depth of Insight: Monitoring tells you when something goes wrong based on predefined criteria. Observability, on the other hand, empowers you to explore data freely, allowing you to understand the root cause of issues, even if they weren’t anticipated.

  2. Proactivity vs. Reactivity: Monitoring is largely reactive. You receive alerts for known issues when they occur. Observability is more proactive, enabling teams to identify and address potential problems before they escalate.

  3. Complex Systems: Modern IT ecosystems, especially those leveraging microservices, can be highly dynamic and complex. In such environments, the predefined metrics of traditional monitoring might not capture the intricacies and interdependencies. Observability shines here, offering a holistic view of the system, irrespective of its complexity.

Complementary, Not Competitive

While observability and monitoring are distinct, they aren’t mutually exclusive. They complement each other. Monitoring provides immediate alerts for known issues, ensuring swift response times. Observability offers depth, allowing teams to understand the system’s intricacies, optimize it, and anticipate potential challenges.

While monitoring provides a safety net for known challenges, observability equips you with a magnifying glass to explore, understand, and optimize every facet of your system.

Scaling with Confidence: Observability in Large Systems

As organizations grow, so does the complexity of their IT systems. NetOps and SRE professionals often manage vast, distributed systems spanning multiple geographies. Observability is the compass that guides teams through this complexity.

  • Distributed Systems: Observability tools are adept at collecting data from disparate sources, ensuring that the data provides a cohesive view even if a system is distributed across the globe.

  • Microservices Architecture: With the rise of microservices, understanding how each service interacts with others is crucial. Observability offers insights into these interactions, ensuring that the system remains harmonious as more services are added.

  • Capacity Planning: As user bases grow and traffic increases, ensuring that the infrastructure can handle this growth is paramount. Observability tools can provide predictive insights based on current trends, helping teams plan for future growth.

For large-scale systems, flying blind is not an option. Observability ensures that teams have a clear, real-time view of the entire system, irrespective of the scale.

Why is Observability Important?

In today’s dynamic and multifaceted IT environments, systems are no longer isolated entities. They’re intricately interwoven, with a single malfunction in one component potentially cascading into more significant, systemic issues. Observability emerges as a paramount necessity in such contexts for several reasons:

  1. System Reliability and Performance: At its core, observability offers a granular view of every moving part within a system. This means potential bottlenecks, inefficiencies, or malfunctions can be detected and addressed before they escalate into significant downtime or performance degradation.

  2. Efficient Issue Identification and Resolution: Traditional debugging or troubleshooting methods can be likened to finding a needle in a haystack. With observability, teams are equipped with a magnet. The rich data and insights provided enable quicker identification of anomalies and, consequently, faster resolution.

  3. Enhanced Development and Operational Workflows: Observability isn’t just about issue resolution; it’s also about continuous improvement. By understanding system behavior in real-time, development teams can make informed decisions during the development phase, ensuring that new releases or updates are optimized for performance and stability.

What are the Benefits and Challenges of Observability?

Observability is undeniably a transformative approach to system management. However, like any powerful tool, it brings profound advantages and inherent challenges.


  1. Faster Incident Response: With real-time insights, teams can react promptly to any irregularities, minimizing potential damage or downtime.

  2. Proactive Issue Detection: Observability allows for a proactive stance instead of being reactive. This means detecting potential issues before they become actual problems, ensuring smoother system operations.

  3. Improved Customer Experience: A system that’s continuously monitored and optimized translates to consistent and reliable user experiences. Happy users lead to better business outcomes.

  4. Empowered Decision-Making for DevOps: Observability provides DevOps teams with actionable insights, allowing them to make data-driven decisions, enhancing both system performance and stability.

Challenges and Solutions

Challenges that are often associated with observability—and potential solutions to consider to mitigate them—include:

  1. Data Volume:

    • Challenge: With the multitude of data points generated, it can be daunting to sift through and discern what’s critical.
    • Solution: Utilize observability tools that offer intelligent data aggregation and visualization, enabling teams to quickly identify patterns and important metrics without getting overwhelmed by raw data.
  2. Integration Complexity:

    • Challenge: Implementing observability across diverse systems with unique configurations can be challenging.
    • Solution: Seek out observability platforms that provide out-of-the-box integrations with common tools and systems. Additionally, consider platforms that support open standards and APIs, allowing for customization and integration with proprietary systems.
  3. Potential Information Overload:

    • Challenge: Observability can sometimes lead to an overwhelming amount of information.
    • Solution: Implement observability solutions that incorporate intelligent alerting mechanisms to highlight only anomalies or deviations from the norm. This ensures that teams receive notifications for critical events, helping them filter out noise and concentrate on actionable insights.

Get the Benefits of Network Observability with Kentik

In the ever-evolving IT and cloud computing landscape, staying ahead of potential issues is not just a best practice—it’s a necessity. Observability provides the in-depth insights and proactive approach required to ensure that systems are not just operational but optimized.

The Kentik Network Observability Platform empowers network pros to plan, run, and fix any network. To see how Kentik can bring the benefits of network observability to your organization, request a demo or sign up for a free trial today.

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