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.

The DIFFERENCE Foundation is a non-profit organization based in the Netherlands that is focused on designing, guiding, and funding highly quantitative research on metabolic dysfunction. The foundation considers metabolic dysfunction—which can trigger obesity, diabetes, many other diseases—the original global pandemic, as it affects almost one quarter of the global population.

At Grafana Labs, we believe that our culture is one of the differentiators that make us an extraordinary company. In the middle of a pandemic, we’ve been in the very fortunate position to be in a high-growth phase: We’ve expanded the team by 118% this year, and our headcount just tipped over 250 global Grafanistas.

If you work in monitoring, you know how important it is for the right people to see the right data. Too much irrelevant data creates a flurry of alerts that hinders productivity, and missing out on key data leads to visibility issues. Worst of all is when you get the wrong people tinkering with data that belongs to other teams.