How OpenTelemetry is Transforming Observability

OpenTelemetry is the new cloud-native standard for application observability. This article is the second of a three-part series that explores the technology’s business benefits and advantages.

The true state of application performance is reflected by a myriad of output data that falls under the umbrella term of telemetry. When  developers and operators  gain a comprehensive view of what’s taking place across different environments, they can proactively monitor applications and rapidly swing into action to head off looming problems.

Easier said than done.

Applications used to be a lot simpler with just inputs and outputs. As systems and development practices evolve, applications have grown more complex. Nowadays, you commonly find applications composed of several interdependent components that need to come together to produce an experience.  When that experience breaks, identifying the root cause and resolution       becomes  significantly more  challenging and time consuming. Developers need to have a view of the system as a whole, not just their application.

Prior to the advent of OTel, organizations were basically stuck, relying upon a fragmented way of troubleshooting specific components. As a result, it was very difficult and cumbersome to observe the application as a whole. That’s the problem that OTel resolves.

The OpenTelemetry framework – also known as OTel – launched in 2019 as a merger of two existing open-source projects, OpenCensus and OpenTracing. What OTel essentially offers is      standardization of the way that organizations  format and collect metrics, logs, and traces from all of these different components. 

OTel provides a unified framework for collecting, processing, and exporting telemetry data with a set of standard formats, software development kits (SDKs), libraries, and other tools that not only lets organizations collect data from their applications but also separates these processes from the observability solutions that previously required proprietary ways of collecting that data. OTel eliminates that earlier complexity by giving organizations a single pipe to run all their data through – offering organizations a valuable tool to improve the observability of their applications.

Now it’s possible for organizations to collect telemetry data from a variety of sources, including applications, services, and infrastructure, and use it with numerous supported monitoring vendors that support OTel. That gives them a consistent way to collect telemetry data, which makes it easier to compare data from different sources and to track historical performance. In the previous blog, we explained how the API layer of an application provides the ideal listening point from which to generate this telemetry. This can help bridge gaps that exist when certain components do not generate that telemetry or when the telemetry implementation lacks customizability. 

Game-changer for Observability, Monitoring App Performance

At the same time, OpenTelemetry’s inherent scalability means that it’s flexible enough to be used to collect data from large and complex systems – a valuable tool for organizations keen to improve the observability of their applications and infrastructure. With the OTel framework in hand, operators can look for bottlenecks, and troubleshoot problems with a framework that provides a consistent way to instrument, collect, and export telemetry data. They can tap into a standardized, flexible, interoperable, and real-time framework for collecting, processing, and exporting telemetry data. The upshot: deeper insights about the behavior and performance of their applications and systems, improved reliability, availability, and performance.

Transformation of Observability

Observability, in this context, refers to the ability to understand the behavior and performance of an application or system through the analysis of telemetry data. Let’s look in more detail at how OTel is reshaping that landscape:

  • Administrators now have a standard for collecting, exporting, and processing telemetry data, regardless of the language, framework, or infrastructure being used. This standardization makes it easier for developers and operators to use and share telemetry data across different systems and tools.
  • OTel gives them a flexible framework they can deploy to customize and extend the collection and processing of telemetry data. This flexibility enables operators to tailor their observability solutions to their specific needs and requirements.
  • Now there’s a common language and standardized format for telemetry data, which enables different tools and systems to work together seamlessly. This interoperability allows operators to integrate their observability solutions with other tools and services, such as monitoring, logging, and tracing.
  • Real-time insights into the behavior and performance of applications and systems is a major boon, enabling administrators to detect and diagnose issues quickly. This real-time visibility is critical for maintaining the reliability and availability of modern distributed systems.
  • OpenTelemetry is an open-source project that is developed and maintained by a community of contributors. This community-driven development model ensures that the project stays relevant and responsive to the needs of its users and the broader observability community.

Future-Proofing Application Intelligence

All the rich data provided by application observability can sometimes exceed the ability of an organization to pay attention it. You may be able to derive rich insights with enough time looking at and analyzing the data, but this can be very time consuming. Artificial Intelligence (AI) can analyze application performance data at scale to identify patterns and insights and potentially predict bottlenecks or other issues. To achieve this, AI requires to train models, which in turn requires large amounts of high-quality data. Structured data, with well-defined and predictable syntax, is easier to derive insights. OTel lets you generate data from your live applications in a way that is structured, and it can generate a ton of it. Even if using AI tools is not on your to-do list for next week, the structured data that is produced by OTel in your infrastructure today could one day contribute to training your future AI.

Ultimately, the wider deployment of OTel promises to be a boon for better observability as enterprises look to better understand and predict the overall health of their applications. No more guesswork and no more frustration about being kept in the dark.