Elastic Stack has excellent analytics capabilities, thanks to the rich Aggregations API in Elasticsearch. This makes it a perfect tool for analyzing data with lots of metrics. Metric data consists of numeric values as opposed to unstructured text such as documents and web pages. Some examples are data generated by sensors, Internet of Things (IoT) devices, metrics generated by mobile devices, servers, virtual machines, network routers, switches, and so on. The list is endless.

Metric data is, typically, also time series; that is, values or measures are recorded over a period of time. Metrics that are recorded are usually related to some entity. For example, a temperature reading (which is a metric) is recorded for a particular sensor device with a certain identifier. The type, name of the building, department, floor, and so on are the dimensions associated with the metric. The dimensions may also include the location of the sensor device, that is, the longitude and latitude.

Elasticsearch and Kibana allow for slicing and dicing metric data along different dimensions to provide a deep insight into your data. Elasticsearch is very powerful at handling time series and geospatial data, which means you can plot your metrics on line charts and area charts aggregating millions of metrics. You can also conduct geospatial analysis on a map.

We will build a metrics analytics application using the Elastic Stack in Chapter 9, Building a Sensor Data Analytics Application.