A data cube is the basic unit of representation of data used for visualization within Imply.
Shown below is a heatmap visualization from a data cube for Wikipedia edits data, using filters for
Latest Day and
Language not English, with the
Time by Hour dimensions being shown.
We'll discuss filters and dimension splits in more detail here.
Filters let you focus on a specific part of your data. It could be a period or time, a certain country or a number of countries, or any other segment of a dimension in your data.
The filters live in the filter bar.
To add a filter, drag a dimension from the dimension panel to the filter bar, or click a dimension and select
Alternatively click on the
+ button in the filter bar.
You can also add filter by clicking (in some cases also dragging) a segment on the main visualization and pressing
There are specific filter controls that are tailored specifically to the type of data being filtered.
You can filter on time and select a filter that is relative to the most recent time of the data.
You can also select a specific time range.
Or select a multitude of disjoint time ranges.
You can select one or more strings to filter on. These can be selected with the aid of a search.
There are other possible filter types that can be used to filter on string data.
You can exclude certain dimensions or select only the values that contain a certain text (or match a regular expression).
You can filter on a number range.
Use dimension splits to visually compare several segments in your data. For example, compare different hours of the day to one another, or different countries to one another. The dimension splits live in the show bar.
To add a dimension split, drag a dimension from the dimension panel to the "Show" bar, or click a dimension and select "Show". To replace all the existing dimension splits, drag a dimension to the visualization panel.
You can also add a dimension split by clicking the
+ button in the show bar.
The splits you select will affect the shown visualization.
Data cubes are designed to be explored quickly.
The exploration if focused on contextual cues that allow you to drill deep into your data.
Data cubes are dynamic and often backed by streaming data that has frequent, high-frequency updates.
You can adjust how often Imply polls for data updates from the data
toggles menu on the header bar as shown below.
All time based calculations performed within Imply are timezone-aware.
You can select your desired timezone from the data cube toggles dropdown menu in the header bar.