• • • • • A closer look at two popular data visualization tools and tips for choosing the best option for you Despite similar sounding names, Kibana and Grafana take very different approaches to the data they visualize. In this post, I’ll walk you through their main differences and show which tool is best suited for your specific needs. [New Post]: Grafana vs. Kibana: How to Get the Most Out of Your Data Visualization by guest author Read it here now>> — OverOps (@overopshq) Getting Started I found it slightly complicated to install and setup Kibana, but if you are already using any components in the ELK (Elasticsearch, Logstash, Kibana) stack then you have already conquered these hurdles. For experimentation purposes, I recommend using Docker. This docker-compose.yml file should get you started, start the service with docker-compose up. Then follow to load data for experimentation.
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Grafana offers a, or you can run it yourself, with Grafana available in all Linux package managers, Homebrew for Mac, and as a Docker image. Gopro quik for mac. I used the Docker option, by running: Open your browser to and log in, the default account details are ‘admin’ for both the username and password. Data Sources Grafana and Kibana have two well-defined, yet different, directions for visualizing data, and they reflect this in the sources you can pull data from. Kibana focuses on helping you search, explore and analyze log data stored in Elasticsearch, and that’s the only source it supports, if you’re not using Elasticsearch then there’s no point using it. If you are, then its integration is tight and well developed.
Grafana offers inbuilt methods for connecting to over, including Elasticsearch, and focuses on visualizing time-series metric data, which can be logging data, but is better suited to visualizing data from constant streaming sources, such as sensors and metric reporting. Add a data source Massaging Data Before filtering and querying your log data with Kibana, you need to configure indices of your data, in the long term this will speed up your experiences with Kibana. Create an Index Once this is complete, Kibana has two options for searching your data, the more standard or the.
They are both powerful and broad but require a steep learning curve if you’ve not used them before. On the positive side,. Grafana offers fewer options for refining the underlying data before you visualize it.
However each data source ships with a query editor that suits the source, not a generic language for all. For example, here’s the query editor for an Elasticsearch data source: Grafana Query Editor Visualizing Data Once you do understand the query languages that Kibana supports, then the charts you can create are complex and detailed and you can save queries to recreate visuals with up-to-date data. For example, this stacked pie chart groups the quantities of data into the 5 ranges you can see on the top level of the key (right-hand side of picture), and then relates the second ranges on the outer ring to each pie segment. This enables you to see how two sets of data relate to each other. Create stacked pie chart This chart visualizes log data as geographic locations on a world map, great for identifying where an event occurred. Create a geo-coded chart Grafana tries to make visualizing a chart as straightforward as possible, giving you visuals before you spend a long time creating a dashboard that is not useful to you.