Operations | Monitoring | ITSM | DevOps | Cloud

Dashboards are a crucial tool in your monitoring arsenal, as they allow you to visualize and correlate telemetry data from across your stack in a single place. Historically, Datadog offered two dashboard types: Screenboards, for pixel-level control on a canvas, and Timeboards, for troubleshooting a specific point in time. Now, we’re excited to introduce a new dashboard layout that combines the best of Timeboards and Screenboards in a single, seamless editing experience.

When Kubernetes launches and schedules workloads in your cluster, such as during an update or scaling event, you can expect to see short-lived spikes in the number of Pending pods. As long as your cluster has sufficient resources, Pending pods usually transition to Running status on their own as the Kubernetes scheduler assigns them to suitable nodes. However, in some scenarios, Pending pods will fail to get scheduled until you fix the underlying problem.

Datadog Notebooks simplify the way teams across an organization find and share knowledge. By bringing together live data and rich Markdown text, Notebooks help teams create powerful, data-driven documents—from runbooks and support playbooks to incident postmortems and data reports. And with collaboration functionalities like real-time editing and commenting, team members can simultaneously make changes to a document and gather feedback along the way.

Given the overwhelming importance of data to organizations, anything they can do to speed up troubleshooting problems in the databases they use is of great value. If a company can speed up or avoid troubleshooting, it frees up time they can invest in doing and building more with their data. Careful attention to database query construction and execution also pays similar dividends, as improved database performance helps organizations get more done faster.

Network Policy is a critical part of building a robust developer platform, but the learning curve to address complex real-world policies is not tiny. It is painful to get the YAML syntax right. There are many subtleties in the behavior of the network policy specification (e.g., default allow/deny, wildcarding, rules combination, etc.). Even an experienced Kubernetes YAML-wrangler can still easily tie their brain in knots working through an advanced network policy use case.

In this post we’ll explore the concepts of data lake, data hub and data lab. There are many opinions and interpretations of these concepts, and they are broadly comparable. In fact, many might say they’re synonymous and we’re just splitting hairs. But let’s look again carefully. We can discern some subtle trends in the way people are doing things, and find distinctions in these expressions.

Your users access your application from a wide range of browsers, which have their own implementations of HTML, CSS, and JavaScript. For instance, many modern JavaScript features such as Promises and Arrow Functions are unsupported by some browsers. These inconsistencies can lead to missing elements and malfunctioning workflows that affect some—but not all—of your user base.

In this enterprise cost containment series, we’ve tackled a range of topics from cloud to professional services and more. Now, I want to dive into the topic you may have expected us to cover from the start: monitoring. After all, at SolarWinds, we create monitoring software. The goal of this post is not to present or sell software, though. It’s our intention to help you have conversations with management and stakeholders—no matter the monitoring you use.

Welcome back to our five-part series in which we discuss a top habit for DBAs to increase business functionality. In part one of this series, we discussed the importance of a capability maturity model (CMM) and more specifically Level 1 of the information management maturity model (IMMM) and how the framework can provide a step-by-step process for DBAs to follow while also allowing businesses to gain skills along the way.