The Kubernetes ecosystem contains a number of logging and monitoring solutions. These tools address monitoring and logging at different layers in the Kubernetes Engine stack. This document describes some of these tools, what layer of the stack they address, as well as best practices for implementation including an example from the field, a quick start, and a demo project.

Stackdriver Monitoring contains a wealth of information about cloud resource usage, both for Google Cloud Platform (GCP) and and other sources. This post will explain how to use the Stackdriver Monitoring API to read, downsample, and export data from Stackdriver to BigQuery. Pub/Sub metrics will be used to demonstrate this.

When a visitor types your website into the search bar, they intend to see your website and not a broken link/webpage. In a digital world where we want everything right now, a website that is not available right now is a website we probably will not go back to in the future. That much should be obvious to everyone. If you are not careful, your website could be sending your visitors to your competitors. So how do you keep track of your website 24/7? One answer is to use an uptime monitoring service.

The one complaint that an IT administrator dreads to receive is one where an end user says, “My application is slow!”. The application in question can be a web application, an enterprise application like SAP, Microsoft SharePoint, or a SaaS application like Salesforce or Office 365. Since the application is accessed over a network, it’s natural that the network team is pulled up first under the suspicion that it’s a network issue.

If you’re trying to evaluate and understand the ROI of building an observability practice and carve out a budget for it, you’re not alone. You’ve probably got some monitoring and metrics capability already, but that’s proving to not be enough–how can you empower your teams as your environment becomes too complex for the basics? And how much will that cost?

Software as a Service (SaaS) is a mainstream way of bringing enterprise applications to hundreds of thousands of application users at a stable cost. Rather than loading client software on a desktop PC, we lease software from the cloud, and monitor how the SaaS application is delivered. That makes it available to everyone in the enterprise.

Data drives every decision we make at Datadog. That includes decisions about when to leave the office to catch the train! A few years ago, Datadog engineers created MTAServiceChecker.com to provide ourselves and our fellow New Yorkers with detailed insight into New York’s subway system, the MTA. For a recent Datadog hackathon, Chuck Hagenbuch and I, part of our growing engineering team here in Boston, created a version of the service checker for our local system, the MBTA.

Squared Up 4.1 just landed. And what better way to start this announcement than the promise of more feature releases?! It’s been seven months since Squared Up 4.0, but the hard work put in by the development team since then has set us up for a bright, feature-rich future. All those juicy performance improvements and platform changes mean, moving forward, you can expect a new feature release from us every two to three months.

Today is a very special day for Squared Up and, we hope, for the broader SCOM community. We are incredibly excited to announce the launch of Cookdown, a new brand dedicated to optimising SCOM with platform extensions and integrations.