Patch compliance indicates the number of compliant devices in your network. This means the number of computers that have been patched or remediated against security threats effectively. The distribution and deployment of patches accomplish nothing if your devices are not compliant. So to establish a good patch management strategy, it is important to pay attention to the effectiveness and reach of your patch deployment activities.
You've joined a company, or worked there a little while, and you've just now realised that you'll have to do on-call. You feel like you don't know much about how everything fits together, how are you supposed to fix it at 2am when you get paged? So you're a little nervous. Understandable. Here are a few tips to help you become less nervous.
AWS Auto Scaling groups are a powerful tool for creating scaling plans for your application. They let you dynamically create a group of EC2 instances that will maintain a consistent and predictable level of service. HAProxy’s Data Plane API adds a cloud-native method known as Service Discovery to add or remove these instances within a backend in your proxy as scaling events occur. In this article, we’ll take a look at the steps used to integrate this functionality into your workflow.
We are happy to announce that the Kafka integration is available for Grafana Cloud, our composable observability platform bringing together metrics, logs, and traces with Grafana. Apache Kafka is an open source distributed event streaming platform that provides high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.
Purchase decisions often begin with a price check. Log management is no different. Evaluate your budget and narrow down the options that fit to choose the tool that gives you the most for what you pay. As always, cheaper is better as long as the platform doesn’t cut any corners. But with log management, there is a catch – not all tools are transparent with their pricing model.
This series was co-written by Musa Barighzaai and Tyler Sullberg. In the previous post, we explored high-level differences between thinking in Clojure compared to thinking in JavaScript. We are now ready to start building our first Clojure microservice. The microservice we are going to build will be very simple. It will be an HTTP server that uses a Redis data store to count how many times a given IP address has pinged the /counter endpoint.
