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.

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.

There are a few key differences between distributed tracing and OpenTelemetry. One is that OpenTelemetry offers a more unified approach to instrumentation, while distributed tracing takes a more granular approach. This means that OpenTelemetry can be less time-consuming to set up, but it doesn’t necessarily offer as much visibility into your system as distributed tracing does.

Have you ever tried to find a bug in a multi-layered architecture? Although this might sound like a simple enough task, it can quickly become a nightmare if the system doesn’t have proper monitoring. And the more distributed your system is, the more complex it becomes to analyze the root cause of a problem. That’s precisely why observability is key in distributed systems. Observability can be thought of as the advanced version of application monitoring.

Big data experts already agree that the amount of generated data is growing exponentially and forecast that it will reach 175 zettabytes by 2025. That projection is predicated upon current realities, which include a growing number of internet users and the billions of embedded systems and connected devices around the world. Even conceptualizing that amount of data is daunting — but then consider how best to manipulate and export it.

With observIQ’s latest contributions to OpenTelemetry, you can now use free open source tools to easily monitor Aerospike. The easiest way to use the latest OpenTelemetry tools is with observIQ’s distribution of the OpenTelemetry collector. You can find it here. In this blog, the Aerospike receiver is configured to monitor metrics locally with OTLP–you can use the Aerospike receiver to ship metrics to many popular analysis tools, including Google Cloud, New Relic, and more.