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).
“But I know, somehow, that only when it is dark enough can you see the stars.” Martin Luther King, Jr.
Welcome to our monthly product newsletter - SigNal 22!
Last month our team worked on improving the logs tab and improved the correlation between telemetry signals to drive contextual insights faster. We were also trending on GitHub and crossed 2000+ developers in our slack community.
Nodejs tops the list of most widely used frameworks for server-side programming by developers. Powered by Google’s V8 engine, its performance is incredible. As a result, Nodejs has now become a critical part of the technology stack of large-scale enterprises and startups. And as nodejs is based on Javascript, it is also easier to learn and begin with.
Monitoring and troubleshooting distributed systems like those built with microservices is challenging. Traditional monitoring tools struggle with distributed systems as they were made for a single component. Distributed tracing solves this problem by tracking a transaction across components.
OpenTelemetry is an open-source standard for instrumenting cloud-native applications for generating different types of telemetry data. A robust observability framework set up using OpenTelemetry can help tremendously while troubleshooting software in production.
OpenTelemetry consists of several main components. But to understand the architecture, we will start by understanding some of the key design principles behind the design and architecture of OpenTelemetry.
Observability vs monitoring has been a common topic in DevOps recently. There has been a lot of debate, and I have learned a lot from them when I started my observability journey. Most literature on observability is associated with a particular product or shares a textbook definition.
Loki and Prometheus are both open source tools. While Loki is a log aggregation tool, Prometheus is a metrics monitoring tool. Loki’s design is inspired by Prometheus but for logs. This blog post compares the two common monitoring tools Loki vs Prometheus, to help you understand their key differences.
Docker logs play a critical role in the management and maintenance of containerized applications. They provide valuable information about the performance and behavior of containers, allowing developers and administrators to troubleshoot issues, monitor resource usage, and optimize application performance. By capturing and analyzing log data, organizations can improve the reliability, security, and efficiency of their containerized environments.
Structured logging is the method of having a consistent log format for your application logs so that they can be easily searched and analyzed. Having structured logs allows for more efficient searching, filtering, and aggregation of log data. It enables users to extract meaningful information from log data easily.