Our Kibana team has been hard at work implementing and executing on a new Kibana strategic vision to streamline the dashboard creation process and sand down the rough edges of creating visualizations for dashboards. We accomplished our goal and reduced the overall time it takes users to go from a blank slate to a meaningful dashboard that conveys insights about the data.

For DevOps teams that want to accelerate release velocity and improve reliability, logs can unlock the insights you need to move faster. But for managers and budget owners, logging can be an unpredictable pain. Trying to estimate logging spend, especially with the adoption of microservices and container-based architecture, seems like an impossible task.

The LogDNA Agent is a powerful way for developers and SREs to aggregate logs from their many applications and services into an easy-to-use web interface. With only 3 kubectl commands, the installation process is quick and simple to complete for any number of connected systems. To help control the logs that are stored and surfaced in the LogDNA web interface, users can set Exclusion Rules, which enables the exclusion of certain queries, hosts, and tags directly from the UI.

The aim of this article is to demonstrate how you can instrument a Java application using Opentelementry and Jaeger. In this example, we will be instrumenting our Java application using OpenTelemetry and the OpenTelemetry Java client, and the tracing data will be exported and visualized using Jaeger. We will use the Logz.io Jaeger backend as it is compatible with common tracing standards like Zipkin, OpenTelemetry, and OpenTracing.

As my colleague, Tim Frank, wrote about recently in his blog post, "The Department of Defense Data Strategy: An Important Start," in late 2020 the Department of Defense (DoD) released its new Data Strategy — providing focus and direction for the Department’s efforts to become data-centric at all levels of its enterprise.

Previously, I wrote a Beginner’s Guide to Jaeger + OpenTracing Instrumentation for Go providing guidance on manually instrumenting Go services. This is useful for cases where we want fine-grained tracing of specific functions. However, what if all we want is to trace a service’s inbound and outbound calls with little to no additional code?

When building a microservices system, configuring events to trigger additional logic using an event stream is highly valuable. One common use case is receiving notifications when errors are seen in one of your APIs. Ideally, when errors occur at a specific rate or frequency, you want your system to detect that and send your DevOps team a notification. Since AWS APIs often use stateless functions like Lambdas, you need to include a tracking mechanism to send these notifications manually.