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OpenTelemetry Metrics: Concepts, Types & Instruments

OpenTelemetry (OTel) Metrics are part of the OpenTelemetry project, which provides tools, APIs, and SDKs for telemetry data collection. These metrics capture system performance data like request latency, error rates, resource usage, and throughput. OTel aims to standardize observability across languages and platforms, making it easier to use and integrate telemetry data. Metrics are one of three core signals of OpenTelemetry along with logs and traces.

Deep dive into observability of Messaging Queues with OpenTelemetry

Working in the observability and monitoring space for the last few years, we have had multiple users complain about the lack of detailed monitoring for messaging queues and Kafka in particular. Especially with the coming of instrumentation standards like OpenTelemetry, we thought there must a better way to solve this. We dived deeper into the problem and were trying to understand what better can be done here to make understanding and remediating issues in messaging systems much easier.

Crossed 17,000+ Github stars, unlimited dashboards & alerts, improved user experience - SigNal 37

Welcome to SigNal 37, the 37th edition of our monthly product newsletter! We crossed 17,000+ Github stars for our open source project. We’ve enhanced our Dashboards UX and incorporated feedback from users in different areas of our product. Let’s see what humans of SigNoz were up to in the month of May 2024.

Telemetry Data Compliance Module

Telemetry data sent from applications often contains Personally Identifying Information (PII) like names, user IDs, phone numbers, and other information that must be obfuscated before the data is sent to storage or observability tools, in order to be in compliance with corporate or government policies such as HIPAA in the US or the GDPR in the EU.

My 3 Lessons About OpenTelemetry for Observability

As a fan of OpenTelemetry, I love to see Cribl meeting customers where they are and helping them get to where they want to be with a vendor-agnostic approach. Where it is not possible or practical to re-instrument a telemetry source, whether an application or infrastructure, the barrier to adopting OpenTelemetry Signals can be daunting.

How to Monitor Host Metrics with OpenTelemetry

Today's environments often present the challenge of collecting data from various sources, such as multi-cloud, hybrid on-premises/cloud, or both. Each cloud provider has its own tools that send data to their respective telemetry platforms. OpenTelemetry can monitor cloud VMs, on-premises VMs, and bare metal systems and send all data to a unified monitoring platform. This applies across multiple operating systems and vendors.

Serverless observability: How to monitor Google Cloud Run with OpenTelemetry and Grafana Cloud

OpenTelemetry has emerged as the go-to open source solution for collecting telemetry data, including traces, metrics, and logs. What’s especially unique about the project is its focus on breaking free from the reliance on proprietary code to offer users greater control and flexibility. As a senior solutions engineer here at Grafana Labs, I’ve spent a lot of time exploring OpenTelemetry, including in my spare time.

Spans - a key concept of distributed tracing

Spans are fundamental building blocks of distributed tracing. A single trace in distributed tracing consists of a series of tagged time intervals known as spans. Spans represent a logical unit of work in completing a user request or transaction. Distributed tracing is critical to application performance monitoring in microservice-based architecture. Before we deep dive into spans, let's have a brief overview of distributed tracing.

What is Fleet Management in OpenTelemetry

Fleet management in the broader sense is about managing, organizing, and coordinating assets within an organization to ensure efficiency, reduce costs, and maintain compliance. The term originates from the automotive industry. According to Forbes, Fleet management involves a slew of strategies and procedures required to operate a fleet of 5 or more vehicles punctually, cost-effectively, and at optimal efficiency.

DataDog vs Jaeger - key features, differences and alternatives

Both DataDog and Jaeger are tools used to monitor application performance. The difference lies in what they monitor and terms of usage. Jaeger is an open-source tool focused on distributed tracing of requests in a microservice architecture. While DataDog is a SaaS APM vendor covering most monitoring needs of an application. Application performance monitoring is the process of keeping your app's health in check. APM tools enable you to be proactive about meeting the demands of your customers.