We've introduced two very cool new features to Oh Dear: the ability to temporarily silence alerts and advanced Slack notifications.

When working with observability data, a good portion of it comes in as time series data — things like CPU or memory utilization, network transfer, even application trace data. And the Elastic Stack offers powerful tools within Kibana for time series analysis, including TSVB (formerly Time Series Visual Builder). In this blog post, I’m going to attempt to demystify rates in TSVB by walking through three different types: positive rates, rate of change, and event rates.

We’ve been monitoring 100,000’s of serverless backend components for 2+ years at Dashbird. In our experience, Serverless infrastructure failures boil down to: These isolated faults become causes of failure due to dependencies in our cloud architectures (ref. Difference of Fault vs. Failure). If a serverless Lambda function relies on a database that is under stress, the entire API may start returning 5XX errors.

There is a dark side to digital transformation, but nobody wants to talk about it. I previously wrote about technical debt. But there’s also complexity debt. When a CIO decides to delay modernizing or upgrading systems, there are usually budget considerations and skills gaps that stand in the way. The IT leader’s job is one of continual evaluation of risk and opportunity amid rapid technology disruption.

Laravel development services have been growing in popularity, with the Laravel framework often being compared to CakePHP. This article will show how to choose the best framework to meet specific business or solution requirements in the most effective way. Laravel and CakePHP are both very popular PHP frameworks. PHP is often used for creating dynamic websites or building high-end apps. PHP frameworks make it possible to create affordable websites with impressive UI/UX.

When it comes to building a website, there is so much to think about. And in this case, you can certainly learn from the mistakes of others. So, what are the common mistakes when planning a website, and how can you play it safe? Having analyzed dozens of websites, we've come up with ten most common mistakes to avoid before starting your website. Make sure to tune in, and let's get started.

Red Hat OpenShift is a Kubernetes-based platform that helps enterprise users deploy and maintain containerized applications. Users can deploy OpenShift as a self-managed cluster or use a managed service, which are available from major cloud providers including AWS, Azure, and IBM Cloud. OpenShift provides a range of benefits over a self-hosted Kubernetes installation or a managed Kubernetes service (e.g., Amazon EKS, Google Kubernetes Engine, or Azure Kubernetes Service).

In Part 1, we explored three primary types of metrics for monitoring your Red Hat OpenShift environment: We also looked at how logs and events from both the control plane and your pods provide valuable insights into how your cluster is performing. In this post, we’ll look at how you can use Datadog to get end-to-end visibility into your entire OpenShift environment.

In Part 1 of this series, we looked at the key observability data you should track in order to monitor the health and performance of your Red Hat OpenShift environment. Broadly speaking, these include cluster state data, resource usage metrics, and information about cluster activity such as control plane metrics and cluster events. In this post, we’ll cover how to access this information using tools and services that come with a standard OpenShift installation.

There are some really crucial metrics that are valuable in terms of the insights they offer. Such metrics include user logins, application throughput, network usage and more. Ironically however, some of these metrics are also the ones that are the most variable, having definite valleys and peaks depending on specific times of a week and because of this, it becomes invariably difficult to set up thresholds for analysis and investigation.