Grafana is a popular way of monitoring and analysing data. You can use it to build dashboards for visualizing, analyzing, querying, and alerting on data when it meets certain conditions. In this post, we’ll look at an overview of integrating data sources with Grafana for visualizations and analysis, connecting NoSQL systems to Grafana as data sources, and look at an in-depth example of connecting MongoDB as a Grafana data source.
One of the biggest challenges with data visualization for complicated software systems is getting quick access to the underlying data and connecting it to some form of cloud-hosted solution. Traditionally it has required quite a bit of middleware and upfront setup with additional tooling.
So you want to build a better dashboard, do you? Well good, you’ve come to the right place! Splunk dashboards are amazing. They are incredibly versatile and customizable. The creation of a dashboard is incredibly simple and can be done all through the UI. If more in-depth customization is required, that can be done through the SimpleXML using HTML panels, in-line CSS, or by uploading a new app from Splunkbase or custom JS/CSS.
With Elastic 7.12, Discover now uses the fields API by default. Reading from _source is still supported through a switch in the Advanced Settings. This change stems from updates made to Elasticsearch in 7.11 with the extension of the Search API to include the new fields parameter. When using the new search parameter, both a document’s raw source and the index mappings to load and return values are used.
Exemplars are a hot topic in observability recently, and for good reason. Similarly to how Prometheus disrupted the cost structure of storing metrics at scale beginning in 2012 and for real in 2015, and how Grafana Loki disrupted the cost structure of storing logs at scale in 2018, exemplars are doing the same to traces. To understand why, let’s look at both the history of observability in the cloud native ecosystem, and what optimizations exemplars enable.
SquaredUp Dashboard Server lets you and your team create beautiful dashboards, for any tool or data, that you can share with everyone in your organization. Here’s a quick introduction to the product where we show you exactly what you can achieve, in no time at all. Let’s start with the three tabs on the top left of the screen: Getting Started, Next Steps, and Sample Dashboards. We’ll run through them one by one.
This article describes how to monitor a (cluster of) redis server. Bleemeo will detects automatically the redis server (it can runs in a Docker container or directly on the host) and create automatically a service dashboard. This article will also covers creating a custom dashboard with additional metrics.
A very fun part of my job as a Solutions Engineer at Grafana Labs is getting to learn the ins and outs of a new feature or play with a plugin while it is still in development. So, when I heard murmurs that our latest Enterprise plugin would be an integration with Jira, I felt the forsaken call of the agile sirens luring me back to my days when I worked as a technical writer on a product team.
We’ve been experimenting with new ways to use and operate Prometheus over the past year. Every successful Grafana Agent experiment turns into an upstream contribution for the whole Prometheus community to benefit from. In this blog post, I go over the history of the Agent’s successful — and not so successful — experiments.
