It’s common in our everyday language to conflate seeing and understanding when the two are actually very different things. For example, if every day for the last few years we spoke briefly and wrote down the total number of Covid cases in the world, it would be easy to see some trends in the data—you would see the data. But if we present the same data drawn as a chart, it’s easy to understand where the spikes and dips are and when the situation got really bad.

OpenTelemetry (OTel), the emerging industry standard for collecting observability data, recently announced the GA of its demo application – and this is good news for distributed tracing fans.

OpenTelemetry (OTel), is an open source, CNCF (Cloud Native Computing Foundation) project that provides tools, APIs and SDKs for observability data collection (i.e, logs, metrics and traces) from cloud-native applications. Developers can use the data collected from OTel to monitor and analyze application health and performance. To leverage the data and its insights, you can export the data to external solutions, like APMs, open source Jaeger and Zipkin, Helios, and others.

iOS and Android apps are often a common component of distributed applications, forming a key part of the software architecture. These mobile apps provide another way to access data and perform actions on various services, requiring tight integration between the apps and the components which serve the data and control it.

Over the past few months, Helios has experienced rapid growth resulting in our user base increasing, our services multiplying, and our system ingesting more data. Like all tech companies that need to scale, we wanted to avoid our performance becoming sluggish over time.

When developers talk about the options OpenTelemetry opens up to them, one of the most powerful use cases is troubleshooting distributed architectures. With OTel data and insights, developers can identify bugs and solve a wide range of issues across various types of architecture and flows. These include asynchronous flows, flows with Lambda functions, and many more.

One of the things that most excites me about what we at Helios are doing differently than anyone else is trace visualizations. While there are many ways to troubleshoot microservice architectures, a good visual overview goes a really long way to speeding up understanding and therefore accelerating time to a resolution. When your manager asks, “Why did that break down?” with Helios you can answer quickly with accurate data—this is the value of the Helios platform.