Distributed tracing helps you track requests across services and understand issues affecting your application performance. In distributed cloud architecture, debugging performance issues is complicated. Distributed tracing gives visibility to teams on how a user request performs across different services, protocols, and cloud infrastructure. Let’s start with a brief overview of distributed tracing.

Data costs fill a large column in many organizations’ accounting sheets. Data pipeline setup and management is a large time sink for DevOps, IT, and SRE. Setting up telemetry pipelines to reduce unwanted data often takes even more time that could better be spent creating value rather than reducing costs. This blog will show you one way to quickly set up your data pipeline to filter unnecessary telemetry data.

OpenTelemetry is an open-source project under the Cloud Native Computing Foundation(CNCF) that aims to standardize the generation and collection of telemetry data. The telemetry data helps developer, DevOps and IT teams to keep a check on their application health. The telemetry data collected by OpenTelemetry consist of logs, metrics, and traces. Together, they are used for performance monitoring and observability in distributed systems. At SigNoz, we are building an OpenTelemetry native APM.

OpenTelemetry is an open-source project under the Cloud Native Computing Foundation(CNCF) that aims to standardize the generation and collection of telemetry data. The telemetry data helps developer, DevOps and IT teams to keep a check on their application health. The telemetry data collected by OpenTelemetry consist of logs, metrics, and traces. Together, they are used for performance monitoring and observability in distributed systems. At SigNoz, we are building an OpenTelemetry native APM.

Let’s say we have an issue in production. We’ve all been there, right? The first thing we want to be able to do is reproduce the issue. By reproducing, we can confirm it’s a recurring issue, rather than a sporadic one, and that it requires a fix to ensure that our product is working properly. When shifting from a monolith to microservices, reproducing issues becomes more of a challenge.

Apache Kafka is a high-throughput, low-latency platform for handling real-time data feeds. Its storage layer is in essence a massively scalable pub/sub message queue designed as a distributed transaction log. It can be used to process streams of data in real-time, building up a commit log of changes. Kafka has strong ordering guarantees that enable it to handle all sorts of dataflow patterns including very low latency messaging and efficient multicast publish / subscribe.

In this post, we’ll look at how to host the OpenTelemetry Collector in Azure Container Apps. There are a few gotchas with how it’s deployed, so hopefully this will stop you from hitting the same issues. If you don’t care about the details and just want to run a script, I’ve created one here.