O

What is Observability?

In IT and cloud computing, observability is the ability to measure a system’s current state based on the data it generates, such as logs, metrics, and traces.

Observability relies on telemetry derived from instrumentation that comes from the endpoints and services in your multi-cloud computing environments. In these modern environments, every hardware, software, and cloud infrastructure component and every container, open-source tool, and microservice generates records of every activity. The goal of observability is to understand what’s happening across all these environments and among the technologies, so you can detect and resolve issues to keep your systems efficient and reliable and your customers happy. Organizations usually implement observability using a combination of instrumentation methods including open-source instrumentation tools, such as OpenTelemetry.

Many organizations also adopt an observability solution to help them detect and analyze the significance of events to their operations, software development life cycles, application security, and end-user experiences. Observability has become more critical in recent years, as cloud-native environments have gotten more complex and the potential root causes for a failure or anomaly have become more difficult to pinpoint. As teams begin collecting and working with observability data, they are also realizing its benefits to the business, not just IT.

Because cloud services rely on a uniquely distributed and dynamic architecture, observability may also sometimes refer to the specific software tools and practices businesses use to interpret cloud performance data. Although some people may think of observability as a buzzword for sophisticated application performance monitoring (APM), there are a few key distinctions to keep in mind when comparing observability and monitoring.

What is the difference between monitoring and observability?

Is observability really monitoring by another name? In short, no. While observability and monitoring are related — and can complement one another — they are actually different concepts. In a monitoring scenario, you typically preconfigure dashboards that are meant to alert you to performance issues you expect to see later. However, these dashboards rely on the key assumption that you’re able to predict what kinds of problems you’ll encounter before they occur. Cloud-native environments don’t lend themselves well to this type of monitoring because they are dynamic and complex, which means you have no way of knowing in advance what kinds of problems might arise.

In an observability scenario, where an environment has been fully instrumented to provide complete observability data, you can flexibly explore what’s going on and quickly figure out the root cause of issues you may not have been able to anticipate.

Benefits of observability

Observability delivers powerful benefits to IT teams, organizations, and end-users alike. Here are some of the use cases observability facilitates:

1. Application performance monitoring: Full end-to-end observability enables organizations to get to the bottom of application performance issues much faster, including issues that arise from cloud-native and microservices environments. An advanced observability solution can also be used to automate more processes, increasing efficiency and innovation among Ops and Apps teams.
2. DevSecOps and Site Reliability Engineering: Observability is not just the result of implementing advanced tools, but a foundational property of an application and its supporting infrastructure. The architects and developers who create the software must design it to be observed. Then DevSecOps and SRE teams can leverage and interpret the observable data during the software delivery life cycle to build better, more secure, more resilient applications. For the uninitiated, DevSecOps stands for development, security, and operations. It’s an approach to culture, automation, and platform design that integrates security as a shared responsibility throughout the entire IT lifecycle. Site reliability engineering (SRE) is a software engineering approach to IT operations.
3. Infrastructure, cloud, and Kubernetes monitoring: Infrastructure and operations (I&O) teams can leverage the enhanced context an observability solution offers to improve application uptime and performance, cut down the time required to pinpoint and resolve issues, detect cloud latency issues and optimize cloud resource utilization, and improve administration of their Kubernetes environments and modern cloud architectures.
4. End-user experience: A good user experience can enhance a company’s reputation and increase revenue, delivering an enviable edge over the competition. By spotting and resolving issues well before the end-user notices and making an improvement before it’s even requested, an organization can boost customer satisfaction and retention. It’s also possible to optimize the user experience through real-time playback, gaining a window directly into the end-user’s experience exactly as they see it, so everyone can quickly agree on where to make improvements.
5. Business analytics: Organizations can combine business context with full stack application analytics and performance to understand real-time business impact, improve conversion optimization, ensure that software releases meet expected business goals, and confirm that the organization is adhering to internal and external SLAs.

DevSecOps teams can tap observability to get more insights into the apps they develop, and automate testing and CI/CD processes so they can release better quality code faster. This means organizations waste less time on war rooms and finger-pointing. Not only is this a benefit from a productivity standpoint, but it also strengthens the positive working relationships that are essential for effective collaboration.

What are the challenges of observability?

Observability has always been a challenge, but cloud complexity and the rapid pace of change has made it an urgent issue for organizations to address. Cloud environments generate a far greater volume of telemetry data, particularly when microservices and containerized applications are involved. They also create a far greater variety of telemetry data than teams have ever had to interpret in the past. Lastly, the velocity with which all this data arrives makes it that much harder to keep up with the flow of information, let alone accurately interpret it in time to troubleshoot a performance issue.

Organizations also frequently run into the following challenges with observability:

• Data silos: — Multiple agents, disparate data sources, and siloed monitoring tools make it hard to understand interdependencies across applications, multiple clouds, and digital channels, such as web, mobile, and IoT.

• Volume, velocity, variety, and complexity: — It’s nearly impossible to get answers from the sheer amount of raw data collected from every component in ever-changing modern cloud environments, such as AWS, Azure, and Google Cloud Platform (GCP). This is also true for Kubernetes and containers that can spin up and down in seconds.

• Manual instrumentation and configuration: — When IT resources are forced to manually instrument and change code for every new type of component or agent, they spend most of their time trying to set up observability rather than innovating based on insights from observability data.

• Lack of pre-production: — Even with load testing in pre-production, developers still don’t have a way to observe or understand how real users will impact applications and infrastructure before they push code into production.

• Wasting time troubleshooting: — Application, operations, infrastructure, development, and digital experience teams are pulled in to troubleshoot and try to identify the root cause of problems, wasting valuable time guessing and trying to make sense of telemetry and come up with answers.

Also, not all types of telemetry data are equally useful for determining the root cause of a problem or understanding its impact on the user experience. As a result, teams are still left with the time-consuming task of digging for answers across multiple solutions and painstakingly interpreting the telemetry data, when they could be applying their expertise toward fixing the problem right away. However, with a single source of truth, teams can get answers and troubleshoot issues much faster.