We cover everything from initial setup to practical tips for monitoring and improving your fasthttp applications. Follow along to enhance your observability and get a clearer view of your app’s performance.

Our developer’s guide breaks down Prometheus Query Language in an easy-to-understand way, helping you monitor and analyze your metrics like a pro.

Nagios is an open-source monitoring system used to track the performance and health of IT infrastructure, including servers, network devices, applications, and services. It is widely used because of its ability to provide real-time alerts, identify issues before they become critical, and ensure uptime by detecting and addressing system failures promptly. Monitoring Nagios plugins on a more robust platform allows for better scalability, deeper analytics, and long-term storage of performance data.

Whether you’re managing code commits or tracking project tasks, integrating Jira with Azure DevOps can provide an informative, unified view of your development and deployment activities. In this step-by-step guide, we’ll walk through the process of connecting Azure DevOps to Jira using Git Integration for Jira – a versatile extension that enhances project management with real-time visibility and synchronization across your development pipelines.

While building our Service Level Objectives (SLO) product, our team at Datadog often needs to consider how error budget and burn rate work in practice. Although error budgets and burn rates are discussed in foundational sources such as Google’s Site Reliability Workbook, for many these terms remain ambiguous. Is an error budget a static quantity or a varying percentage? Does burn rate indicate how fast I’m spending a fixed quantity, or is it just another way to express error rate?

Most load balancers only check a client certificate when the client first connects. However, this can be problematic if a client stays connected for an extended period of time. Staying connected would allow clients to continually send and receive data. Imagine you have an employee whose certificate and key were stolen by an adversary. If you are using TLS client authentication, that adversary can connect to your infrastructure and maintain illegal access.

Data quality testing is a subset of data observability. It is the process of evaluating data to ensure it meets the necessary standards of accuracy, consistency, completeness, and reliability before it is used in business operations or analytics. This involves validating data against predefined rules and criteria, such as checking for duplicates, verifying data formats, ensuring data integrity across systems, and confirming that all required fields are populated.