We all like a pretty dashboard. For us data nerds, there’s something extremely enticing about the colors and graphs depicting our environment in real-time. But while Kibana and Grafana bask in glory, there is a lot of heavy lifting being done behind the scenes to actually collect the data.
In 6.0, we introduced a new experimental language for Kibana’s query bar. Since then, we’ve been listening to your feedback and making adjustments. Like all experiments, some things worked and some didn’t. Let’s walk through what’s changing in 6.3, what’s new, and where we’d like to go next.
Vega declarative grammar is a powerful way to visualize your data. A new feature in Kibana 6.2, you can now build rich Vega and Vega-Lite visualizations with your Elasticsearch data. So, let’s start learning Vega language with a few simple examples.
We live in a world of big data, where even small-sized IT environments are generating vast amounts of data. Once an organization has figured out how to tap into the various data sources generating the data, and the method for collecting, processing and storing it, the next step is analysis.
While Logz.io provides Kibana — the ELK Stack’s visualization tool — as part of its service, a lot of users have asked us to support Grafana. One of the leading open source visualization tools today, Grafana has some added value when compared to Kibana, especially around visualizing time-series data.
The Panopta Monitoring Cloud adds CounterMeasures, empowering users to automatically mitigate and react to incidents, freeing up more time for IT and DevOps professionals.