Having good observability is vital to the health of your cloud infrastructure and applications, and a key element to using that information effectively is being able to create dashboards with relevant metrics. Today we are announcing a new dashboard creation experience from Cloud Monitoring that allows you to generate a greater variety of visualization types, introduces better flexibility for dashboard layouts, and makes data manipulation easier so you can create dashboards that better fit your needs.

Our Open Access feature enables easy, and unlimited sharing of dashboards across your organization – with any audience at all. A highly popular feature, these shared dashboards help you bring better visibility and collaboration – conveying the shared “truth”! With our latest release SquaredUp 5.0, we have made Open Access dashboards fully interactive, like the rest of our dashboards, so you can see that next level of detail you’ve been wanting.

In collaboration with the AWS team, we have just launched another AWS integration, the AWS IoT SiteWise plugin for Grafana. AWS IoT SiteWise is a managed service that helps users collect, store, organize, and monitor data from industrial equipment, across facilities, at scale.

Monitoring your application can be a difficult task if you’re just getting started with it. You need to know which metrics matter most and understand which ones you can combine into meaningful graphs. In the (not just holiday ⛄️) spirit of making things smoother for our users, we’ve added a magic dashboard to the Ruby integration that shows you the volume of emails you are sending with ActionMailer.

At SquaredUp, we are all about dashboards and visualizations that enable you to build the ideal single pane of glass. If you work in monitoring, you know that designing the perfect dashboards that give you all of your important metrics at a glance, is easier said than done. Most of the time, it takes several attempts to optimize a dashboard for easy reading, and more often than not, this step is neglected. I wouldn’t recommend skipping over this step though.

At AWS re:Invent today, Dr. Werner Vogels, VP and CTO of Amazon.com, announced a partnership between AWS and Grafana Labs, resulting in the Amazon Managed Service for Grafana, a scalable managed offering that provides AWS customers a native way to run Grafana directly within AWS alongside all their other AWS services. We’d like to give a big thanks to the whole Grafana community. Not only have they inspired us with so many great use cases, but they help us build better software every day.

Cloud native observability is at a watershed moment. The explosion of microservices has created previously unseen amounts of monitoring data, limiting the ability of humans and computer systems to extract meaning from data with last-generation tools. Debugging is often a process of detecting correlation, and then turning correlations into causal connections. This is where modern cloud native tooling comes in.

In this blog post you’ll learn how to create visualizations for Kibana dashboards from Jupyter Notebooks. The good news for you as a Python developer: You won’t have to spend time writing cumbersome plain JSON documents or even JavaScript code. Under the hood, we’re going to make use of Kibana’s Vega plugin which became generally available with Elastic Stack 7.10.

The Network Monitoring Dashboard is critical for your users to process, interact with, and analyze data. While most enterprise teams understand the essentiality of dashboards, they often fail to maintain the efficacy of a well-designed dashboard. A common phenomenon among dashboards is that even the most sophisticated-looking dashboards, embedded with colorful graphics, can be deceptive and uninformative for the decision-making process.

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