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Definition: What Is Observability?

In IT, software, and cloud computing, observability is the ability to get visibility into the internal state of your systems by collecting and visualizing the system’s output data, such as logs, metrics, and traces. It is important to note that the more observable your system is, the more accurate, faster, and efficient it is at identifying and resolving issues.

The term ‘observability’ was first introduced by engineer Rudolf E. Kálmán. He used it in 1960 to describe control systems. In engineering, control systems are devices that regulate and direct other systems’ actions through control loops to produce desired outcomes. He defined observability as the measure at which knowledge of a system’s external outputs infers its internal state. The term has, however, been redefined and is now widely used by software-as-a-service (SaaS) companies today.

What Is the Difference Between Observability and Monitoring?

Despite their similarity, observability and monitoring are two distinct but related concepts.

One key difference between observability and monitoring is how they approach the issue and the ‘unknowns.’ While monitoring captures how well your IT infrastructure works, observability focuses on getting complete visibility across your systems.

Monitoring is all about collecting and analyzing telemetry—key metrics that matter to business and IT operations—that can be used to understand or diagnose any system issue over time. Monitoring uses various metrics to ensure performance doesn’t exceed the threshold.

With observability, you aim to identify and address any ‘unknown unknowns’ or events the team didn’t consider. It explores this telemetry data and uses the insight generated from monitoring to enable a holistic understanding of your system’s health and performance. This is particularly important because modern IT environments are complex and have many interdependent variables. However, this is not to say that observability eliminates the need for monitoring; monitoring is just one way to achieve observability.

Read Observability vs Monitoring for a more detailed comparison of the differences between the two.

Why Is Observability Important?

Observability is important because it gives you performance-focused insights, more control, and an understanding of complex modern IT systems.

During the monolith architecture days, visibility into systems was much more straightforward. Today, modern IT systems are dynamic, complex, and have a lot of interconnected parts; some, like cloud-native architectures such as Docker, Kubernetes, and microservices, are small, short-lived, and ephemeral. This is useful because it makes them reliable, secure, accessible, and, most importantly, flexible.

However, their continuously changing nature and complexity make traditional monitoring ineffective. Thus, the majority of the issues are neither known nor monitored. Hence the focus on observability.

With observability, organizations can take a more proactive approach to unknown problems using techniques like AIOps and get visibility into the system’s functionality by monitoring. Observability also enables everyone to understand what needs to be done to improve performance before the issue negatively impacts the business and organization.

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Who Uses Observability?

Technically, everyone benefits from observability in one way or the other.

Site reliability engineers (SREs) and IT operations teams ensure the IT systems are up and available. Their responsibilities are made more accessible through observability, as it informs them of an issue and provides context and information so they can be proactive. In addition, when we look at the software development lifecycle, developers and the DevOps teams need observability to understand software systems’ health and performance and address bugs and events.

Besides the technical teams, the business and managerial leads benefit from observability. Insights from observability solutions allow thoughts leads and business stakeholders to set goals and policies and improve operations concerning customer-affecting issues, time to market, and cost. Thus, they can make better business decisions based on business goals and what matters the most.

For end-users, observability plays a significant role in the customer experience by ensuring the service can meet the user’s needs with no downtime.

Benefits of Observability

The benefits of observability extend beyond IT use cases and help everyone on both the technical and business sides of the organization. Here are a few of them:

  • Full visibility and faster troubleshooting. An observability solution takes monitoring metrics to drive efficiencies and understand the application’s end-to-end process and critical incidents. This allows the team to proactively prevent ‘known unknowns’ and ‘unknown unknowns’ from recurring. Also, if something does happen, you can quickly resolve the issue and find the root cause.
  • Improved user experience. Regardless of the system’s complexity, you can provide a good user experience to end users if you have a solid understanding of your infrastructure. Observability analyzes user experience using synthetic monitoring and real user monitoring (RUM) data. One can learn more about a system’s functionality through synthetic monitoring and determine whether the program is operating as intended using RUM. Both are pretty helpful in ensuring that the user experience is provided at its best.
  • Business analytics. By collecting and exploring telemetry, you can understand your system’s real-time fluctuations with observability. With this information, you can also understand your clients better, minimize outages, and set alerts on failure points before they impact service-level agreements (SLAs) or business objectives.
  • Scalability. Building scalable distributed systems has become more accessible thanks to the advancement and development of new technological stacks. However, this advancement came with some complexity that makes traditional monitoring hard. Observability helps you measure these systems’ states as you efficiently develop applications using modern frameworks and cloud infrastructure.

How Does It Work?

Observability solutions integrate with the built-in monitoring capabilities of applications to identify and gather telemetry data, namely logs, metrics, and traces, collectively known as the three pillars of observability. This data can determine the what and why of every incident that might lead to an issue.

  • Logs: Logs are text files with timestamps that you cannot alter. They record events, processes, messages, and other information from operating systems and applications. Logs track events and spot unexpected and emerging behaviors, such as security breaches in your infrastructure’s load balancers and caches. Observability tools with log management capabilities like Sematext Logs streamline the process by centralizing your logs and helping you see meaningful patterns across your platform. Thus making troubleshooting and debugging systems more efficient. Generally, logs can be in a line or multiple and come in three formats: plain text, structured logs, binary.
  • Metrics: Metrics are numeric values that capture the specific attributes of a system over time. Though they come in various formats, they’re structured, making them easy to query and store. You can think of them as the measured values derived from system performance that convey information about the system, or SLAs. In addition to giving visibility and insight, metrics are used to assess the severity of issues and trigger alerts when some attributes exceed a specific threshold.
  • Traces: Every action or request that takes place within the distributed system is known as a ‘span.’ Traces capture this span and provide information about the action or request’s journey through the distributed system. Traces are used to track, pinpoint, and profile bottlenecks and prioritize areas for optimization and improvement. Thus, you can use traces to specify metrics and logs that may be relevant if an issue emerges.

How to Implement Observability?

As an organization’s distributed software systems and infrastructure grows in scale and complexity, achieving systems observability becomes more important. Here are some key elements and strategies to consider when implementing observability:

  • Choose an observability tool. The value of observability is its ability to offer visibility so the team can predict and resolve issues quickly. However, this is only possible by collecting telemetry data regardless of the data format from your technology stack and infrastructure. Thus, your observability system needs to be as dynamic as your business. For example, if you have containerized applications, you should be able to monitor any container that gets spun up with your tool.
  • Make it part of your Incident response process. Tying your incident response process with observability allows the team to detect security events early, get data about outages, and respond to what has happened or is about to happen. Of course, you should pair this with effective incident response practices like continuous monitoring, intuitive search experiences, and automated alerts to ensure that the time to resolution (MTTR) metrics are minimal and that service reliability is guaranteed.
  • Correlate telemetry data. In addition to collecting telemetry data, you need to aggregate and standardize metrics, logs, and traces across the organization. This helps to easily decode an event that isn’t observable in a single log. It is especially valuable if you want to stay on top of your data, find trends, and get complete visibility across your infrastructure metrics.
  • Leverage AlOps and machine learning capabilities. By incorporating AI and machine learning capabilities into your observability strategies, you can apply automation when aggregating data and correlate it to capitalize on the intelligence they capture fully. This allows you to move away from vague potential issue predictions to more targeted and accurate predictions. This level of insight drives faster incident responses and higher visibility, especially in today’s complex and fast-changing environment.
  • Cultivate an observability culture. Observability is more than the tool and the telemetry data you collect; it requires a mindset shift within the organization. Thus, building an observability culture around the process and tool is vital to ensure that it becomes a real-world practice and not just another excellent idea. To ensure this, you should champion continuous improvement, training, and education about observability so the entire organization gets started on it and sees the value it brings.

Challenges of Observability

Observability makes it relatively easy to maximize the value of your distributed cloud-native technology data, but there are still challenges to establishing full-scale observability. Here are a few of them:

  • Volume, velocity, variety, and complexity. Cloud environments generate more telemetry data, which comes in various formats and structures than the team has ever had to interpret in the past. While this has the advantage of more insights and granularity, it also makes it difficult to keep up with the flow of information and its analysis. This is much worse with containerized applications that can spin up and down in seconds. Observability helps mediate this by helping the team better understand every interdependency and eliminate blind spots across the ever-changing environments.
  • Data silos. Many organizations have complex IT environments with countless data sources, many with their own monitoring solution. These silos make it hard to correlate and understand the data, filter out the correct information, and provide situational context regarding interpretation. A good way to avert this is to structure your data into a standardized format.
  • Alert fatigue. Observability tools often tend to generate a lot of alerts to draw team attention to issues or possible issues. While this makes troubleshooting relatively easy, it might also be challenging. Receiving too many alerts can lead to alert fatigue and operational noise, causing your team to ignore even the most crucial ones. A robust alerting system that uses many alert pathways is something that organizations must adopt to avoid this.

How to Choose the Right Observability Tool?

Observability technologies offer organizations a centralized platform for telemetry data aggregation, visualization, and insight into the internal state of their whole infrastructure in a dispersed environment. With these tools, one can monitor the system, get feedback, and resolve issues proactively addressed before they arise.

However, one can only fully benefit from observability by using the right observability solution. Therefore, here are a few key features your tool of choice needs to possess.

  • Telemetry data fuels observability thus, your tool should be able to collect and aggregate data from various data sources across your systems regardless of the data format or structure. This should be coupled with a sound storage system for fast retrieval and long-term retention.
  • An intuitive and easy-to-navigate interface that lets you deploy, manage, and automate processes. From this interface, you should be able to access telemetry data, reports, visualizations, and KPIs to get real-time insights into the collected data and set up alerts on critical business metrics.
  • It should have a good visualization interface for easy consumption. This would make decision-making and resolution much faster.
  • The underlying infrastructure and supporting elements must offer easy scalability and dependability without unduly burdening IT operations. This cuts across support for third-party integrations and seamless integration with the various languages and frameworks your organization already uses in its distributed applications.
  • The tool should offer support, timely updates, and product enhancements, considering the complexity and steep learning curve of observability and the cloud environment.
  • Lastly, It should improve the customer experience and meet your business objectives.
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System Observability with Sematext

Sematext Cloud is a full-stack observability solution that provides real-time visibility into the performance of your environment by collecting logs and metrics from your various data sources, from network to servers, databases, processes and more. It reports on resource utilization and key performance metrics including CPU, memory, disk usage and load.

Sematext comes with out-of-the-box and customizable dashboards where observability is brought to life. It allows you to create alerts, connect apps, and filter or aggregate data. Further, Sematext agent features an auto-discovery service that detects new services and log sources once it is installed on a host, and starts monitoring their logs and metrics as soon as they are found.

In addition to logs and metrics, the Sematext Cloud suite includes all the tools needed to ensure the best user experience. Experience, the real user monitoring tool, helps assess the actual experience and satisfaction of your customers, providing data on how the application performs in various corners of the world. On the other hand, Synthetics is a synthetic monitoring tool to test key web application metrics such as speed, uptime, error rates, third APIs responses, SSL certificate expiry, and many more.

Sematext has extensive anomaly detection and alerting capabilities that allow you to avoid wasting time continually monitoring what is going on with your system. When an issue occurs, it will notify you via your preferred notification channel, which includes e-mail, Slack, and custom webhooks.

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