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.
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.
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.
Grafana Tempo, the recently announced distributed tracing backend, relies on integrations with other data sources for trace discovery. Tempo’s job is to store massive amounts of traces, place them in object storage, and retrieve them by ID. Logs and other data sources allow users to quickly and more powerfully jump directly to traces than ever before. Previously we investigated discovering traces with Loki and exemplars.
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.
