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Developers Can Leverage OpenTelemetry to Achieve Fuller Visibility

Michael Olechna
Guardsquare

Observability is currently a hot topic. Businesses and consumers are increasingly relying on digital apps for everyday functions, which means every company needs a high performing app or website. When you take a minute to evaluate why, the numbers quickly make sense. In 2025, the number of mobile users worldwide is projected to reach 7.49 billion. And as digital adoption continues to grow, so does users' quality expectations. Each one of those users, including developers, is expecting a frictionless, high-quality experience. As end-user experiences become more connected with an organization's bottom line, a solution to catch performance hiccups becomes necessary. Hence the adoption of front-end observability through initiatives like digital experience monitoring. And who better to execute this initiative than the developers writing the code. But there's a problem with traditional observability tools tailored for DevOps, SRE, and IT teams. Developers need a tool that can be portable and vendor agnostic, given the advent of microservices. It may be clear an issue is occurring; what may not be clear is if it's part of a distributed system or the app itself. Enter OpenTelemetry, commonly referred to as OTel, an open-source framework that provides a standardized way of collecting and exporting telemetry data (logs, metrics, and traces) from cloud-native software. Prior to the onset of OpenTelemetry, there was a lack of standardization when collecting and instrumenting telemetry data. When it came to code instrumentation, there was significant variation. Due to this variation, the result was a lack of data portability and a burden on the developer to maintain large, complex instrumentation libraries. This doesn't just add significant time and effort on the developer's part. This directly impacts visibility into app performance, potentially leading to a negative end user experience. It also creates vendor lock-in and inefficiencies that can be costly for an organization, further affecting business revenue. As the market shifts toward developer-first observability, the need for a solution like OTel becomes readily apparent — explaining its rapid rise in popularity since its launch in 2019. OTel gave developers a way to ingest, view, and export telemetry data. The best part (or one of many)? It's vendor agnostic. This unified method of collecting data makes it easier for modern development teams to get a clearer, more complete picture of their apps' health and performance. The platform also provides a rich set of APIs and SDKs that are also vendor agnostic. With full control of their data, development teams can quickly instrument cloud-native apps and get started with ease. When drilling down into specific benefits, perhaps the most important feature is OTel's versatility. In addition to being vendor agnostic, the platform supports a wide range of vendors, both commercial and open source. This is key to developers being able to leverage their telemetry data long-term because they have the ability to take it with them. Should they choose to change vendors, it's as easy as exporting their OTel data to their new vendor. This eliminates the manual and time intensive process of data re-instrumentation. When discussing use cases for these benefits, three specific examples immediately come to light. The first is faster identification of performance bottlenecks. By examining telemetry data in OTel, teams can determine performance bottlenecks by tracking the time it takes to execute individual operations. Leveraging this information provides critical context to help solve application performance issues and optimize app performance. The second use case is troubleshooting problems. OTel provides a single source of truth for all telemetry data in a distributed system. Thus, development teams can track the flow of execution through their systems by examining OTel data. Developers can track down the root cause of the issue for faster resolution and ensure they are treating the cause, not a symptom. The third use case, data control, relates to one of the key benefits — OTel's versatility. OpenTelemetry is designed to work and integrate with various observability tools and platforms. This includes backends and popular tracing systems like Jaeger, as well as other metrics and logging solutions. Again, this puts data control back in the hands of developers. They can select the tools they are comfortable with or continue using what's already in their workflow, while maintaining a clear view of their app's telemetry data. By adopting OpenTelemetry, developers gain fully contextualized visibility into their distributed applications. In turn, they're able to identify performance bottlenecks faster, get down to the root cause to debug issues, optimize their resource utilization, and improve the overall reliability and user experience of their software systems.

Michael Olechna is Product Marketing Manager at Guardsquare

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Developers Can Leverage OpenTelemetry to Achieve Fuller Visibility

Michael Olechna
Guardsquare

Observability is currently a hot topic. Businesses and consumers are increasingly relying on digital apps for everyday functions, which means every company needs a high performing app or website. When you take a minute to evaluate why, the numbers quickly make sense. In 2025, the number of mobile users worldwide is projected to reach 7.49 billion. And as digital adoption continues to grow, so does users' quality expectations. Each one of those users, including developers, is expecting a frictionless, high-quality experience. As end-user experiences become more connected with an organization's bottom line, a solution to catch performance hiccups becomes necessary. Hence the adoption of front-end observability through initiatives like digital experience monitoring. And who better to execute this initiative than the developers writing the code. But there's a problem with traditional observability tools tailored for DevOps, SRE, and IT teams. Developers need a tool that can be portable and vendor agnostic, given the advent of microservices. It may be clear an issue is occurring; what may not be clear is if it's part of a distributed system or the app itself. Enter OpenTelemetry, commonly referred to as OTel, an open-source framework that provides a standardized way of collecting and exporting telemetry data (logs, metrics, and traces) from cloud-native software. Prior to the onset of OpenTelemetry, there was a lack of standardization when collecting and instrumenting telemetry data. When it came to code instrumentation, there was significant variation. Due to this variation, the result was a lack of data portability and a burden on the developer to maintain large, complex instrumentation libraries. This doesn't just add significant time and effort on the developer's part. This directly impacts visibility into app performance, potentially leading to a negative end user experience. It also creates vendor lock-in and inefficiencies that can be costly for an organization, further affecting business revenue. As the market shifts toward developer-first observability, the need for a solution like OTel becomes readily apparent — explaining its rapid rise in popularity since its launch in 2019. OTel gave developers a way to ingest, view, and export telemetry data. The best part (or one of many)? It's vendor agnostic. This unified method of collecting data makes it easier for modern development teams to get a clearer, more complete picture of their apps' health and performance. The platform also provides a rich set of APIs and SDKs that are also vendor agnostic. With full control of their data, development teams can quickly instrument cloud-native apps and get started with ease. When drilling down into specific benefits, perhaps the most important feature is OTel's versatility. In addition to being vendor agnostic, the platform supports a wide range of vendors, both commercial and open source. This is key to developers being able to leverage their telemetry data long-term because they have the ability to take it with them. Should they choose to change vendors, it's as easy as exporting their OTel data to their new vendor. This eliminates the manual and time intensive process of data re-instrumentation. When discussing use cases for these benefits, three specific examples immediately come to light. The first is faster identification of performance bottlenecks. By examining telemetry data in OTel, teams can determine performance bottlenecks by tracking the time it takes to execute individual operations. Leveraging this information provides critical context to help solve application performance issues and optimize app performance. The second use case is troubleshooting problems. OTel provides a single source of truth for all telemetry data in a distributed system. Thus, development teams can track the flow of execution through their systems by examining OTel data. Developers can track down the root cause of the issue for faster resolution and ensure they are treating the cause, not a symptom. The third use case, data control, relates to one of the key benefits — OTel's versatility. OpenTelemetry is designed to work and integrate with various observability tools and platforms. This includes backends and popular tracing systems like Jaeger, as well as other metrics and logging solutions. Again, this puts data control back in the hands of developers. They can select the tools they are comfortable with or continue using what's already in their workflow, while maintaining a clear view of their app's telemetry data. By adopting OpenTelemetry, developers gain fully contextualized visibility into their distributed applications. In turn, they're able to identify performance bottlenecks faster, get down to the root cause to debug issues, optimize their resource utilization, and improve the overall reliability and user experience of their software systems.

Michael Olechna is Product Marketing Manager at Guardsquare

Hot Topics

The Latest

A perfect storm is brewing in cybersecurity — certificate lifespans shrinking to just 47 days while quantum computing threatens today's encryption. Organizations must embrace ephemeral trust and crypto-agility to survive this dual challenge ...

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...