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SigNoz

New Relic vs DataDog - Features, Pricing, and Performance Compared

New Relic vs DataDog: Both tools are popular for application and infrastructure monitoring, offering a wide range of features. This post compares New Relic and DataDog on key aspects like APM, log management, infrastructure monitoring, and OpenTelemetry support. Info I instrumented a sample Spring Boot Application and sent data to Datadog and New Relic to evaluate my experience. Some takeaways are subjective and based on personal preference.

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.

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.

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.

Latest Top 13 Distributed Tracing Tools [perfect for microservices]

Modern digital organizations have rapidly adopted microservices-based architecture for their applications. Distributed tracing tools help monitor microservices-based applications. Choosing the right distributed tracing tool is critical. How do you know which is the right one for you? In this post, we will cover the top 13 distributed tracing tools in 2024 that can solve your monitoring and observability needs.

Monitoring your Nextjs application using OpenTelemetry

Nextjs is a production-ready React framework for building single-page web applications. It enables you to build fast and user-friendly static websites, as well as web applications using Reactjs. Using OpenTelemetry Nextjs libraries, you can set up end-to-end tracing for your Nextjs applications. Nextjs has its own monitoring feature, but it is only limited to measuring the metrics like core web vitals and real-time analytics of the application.

Choosing an OpenTelemetry backend - Things To Keep In Mind

OpenTelemetry is a Cloud Native Computing Foundation(CNCF) incubating project aimed at standardizing the way we instrument applications for generating telemetry data(logs, metrics, and traces). However, OpenTelemetry does not provide storage and visualization for the collected telemetry data. And that’s where an OpenTelemetry backend is needed. Cloud computing and containerization made deploying and scaling applications easier.

Docker Logging - Types, Configuring Drivers, Logging Strategies [Complete Guide]

Log analysis is a very powerful feature for an application when it comes to debugging and finding out which flow is working properly in the application and which is not. In a world of containerization and cloud computing, it is essential to understand logs generated by a Docker environment to maintain healthy performing applications. In this article, we will discuss log analysis in Docker and how logging in Docker containers is different than in other applications.