In this post, we will discuss some key considerations and strategies to collect and analyze your AWS Lambda logs. This will include 1) what to know about logging Lambda functions, 2) how to ship log data to a centralized logging solution, and 3) how to search and visualize log data on monitoring dashboards.

As organizations migrate to Elastic from incumbent vendors, quickly onboarding log data from their current solution into Elastic is one of the first orders of business. Data onboarding often involves having to adjust ingestion architecture and implement configuration changes across data sources. We want to ensure that users trialing or migrating to Elastic can get data in quickly to start seeing the power of Elastic solutions as quickly as possible.

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.

SREs, developers and DevOps staff for mission-critical modern apps know being notified in real-time when or before critical conditions occur can make a massive difference in end-user digital experiences and in meeting a 99.99% availability objective.

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.

Companies generate data at an exponential rate, and the task of analyzing data to produce relevant security insights can be overwhelming. With evolving market dynamics and threat landscapes, security teams have a greater need for integrated and scalable monitoring that provides real-time and meaningful insights into the state of organizational security posture.

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?