Tying Feature Flagging (OpenFeature) with OpenTelemetry
Quick demo on tying Feature Flagging (OpenFeature) with OpenTelemetry. It uses LaunchDarkly as feature flagging backend and SigNoz as observability backend.
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The latest News and Information on Distributed Tracing and related technologies.
OpenTelemetry vs. OpenTracing - Decoding the Future of Telemetry Data
OpenTelemetry and OpenTracing are open-source projects used to instrument application code for generating telemetry data. While OpenTelemetry can help you generate logs, metrics, and traces, OpenTracing focuses on generating traces for distributed applications. If you’re thinking of choosing between OpenTelemetry and OpenTracing, go for OpenTelemetry. OpenTracing is now deprecated, and users of OpenTracing are advised to migrate to OpenTelemetry.
OpenTelemetry vs Datadog - Choosing the Right Monitoring Tool
OpenTelemetry and DataDog are both used for monitoring applications. While OpenTelemetry is an open source observability framework, DataDog is a cloud-monitoring SaaS service. OpenTelemetry is a collection of tools, APIs, and SDKs that help generate and collect telemetry data (logs, metrics, and traces). OpenTelemetry does not provide a storage and visualization layer, while DataDog does.
Redacting Sensitive Data in OpenTelemetry Collector
This guide covers types of data that can be redacted and step-by-step instructions for configuring the Attribute Processor.
Jaeger vs. Grafana Tempo: A Comprehensive Comparison for Distributed Tracing
When it comes to monitoring, diagnosing, and optimizing the performance of complex systems today, you can’t really go wrong with tracing tools. And while OpenTelemetry has become the go-to choice for instrumenting apps and collecting traces, there are several other options in the backend that can effectively store, manage, and analyze traces sent by OpenTelemetry. Two of these open-source tools are Jaeger and Grafana Tempo. In this article, we’ll compare and contrast the two.
Deploying the OpenTelemetry Collector to AKS
While investigating some issues users raised around the OpenTelemetry Collector running in AKS, I found a few nuances that are worth noting. In this article, I'll go over some changes you have to implement in your values.yaml to make it work for you.
How to Monitor JVM with OpenTelemetry
The Java Virtual Machine (JVM) is an important part of the Java programming language, allowing applications to run on any device with the JVM, regardless of the hardware and operating system. It interprets Java bytecode and manages memory, garbage collection, and performance optimization to ensure smooth execution and scalability. Effective JVM monitoring is critical for performance and stability. This is where OpenTelemetry comes into play.
An Overview of the OpenTelemetry Collector's Configuration File
In this video, I’ll provide an overview of the OpenTelemetry Collector’s configuration file (config.yaml) with examples from the Splunk distribution. I will briefly explain the components of the Splunk OTel Collector, and walk you through a sample generic configuration of the OTel Collector. We’ll then use the Splunk Observability Cloud interface to construct the commands needed to install the Splunk OTel Collector on a specific host. This installation will copy a default Splunk OTel Collector configuration onto the host, and we’ll review the Splunk specific components of this configuration.
OpenTelemetry Batches Like a Boss!
The Batch Processor in the OpenTelemetry project is used in both the SDKs and the OpenTelemetry Collector. What exactly is this processor, and what does it do? And, how can you use the OTLP Logs, OTLP Metrics, and OTLP Traces Functions in Cribl Stream and Cribl Edge to build batches like a boss?
Setting up and Understanding OpenTelemetry Collector Pipelines Through Visualization
Observability provides many business benefits, but comes with costs as well. Once the (not-insignificant) work of picking a platform, taking an inventory of your applications and infrastructure, and getting buyin from leadership (both from the business and engineering sides of the house) is done, you then have to actually instrument your applications to emit data, and build the data pipeline that sends that data to your observability system.