Imply Polaris provides a wide array of expressive visualizations to enable fast, interactive exploration of your data. Visualizations are not just static charts. You can interact directly with them by clicking on various graphical elements to select data segments for drill-down and deeper analysis.
When you change the shown dimensions, Polaris chooses the best visualization for the selected dimensions automatically.
To choose the visualization you prefer, click on the current visualization type to the right of the filter and split bars. The following example shows Table as the current visualization:
The following sections describe the available visualizations.
The default visualization is the Overall visualization. It presents a numeric summary of the currently selected measures, applying any selected filter criteria.
You can select multiple measures and show a time range comparison as well, as shown in the following screenshot:
If you create an Overall visualization with a single measure, you can apply conditional formatting to color the visualization as an indication of data severity—green for ok, amber for warning, red for danger.
To add conditional formatting, click the paintbrush icon in the visualization pane on the right, then click Add condition. Select the condition to apply to the data and the corresponding formatting. You can create multiple entries, as shown in the following screenshot:
The table visualization is the default view for a categorical (string) dimension. It presents a table view of the data with formatting that aids visualization of measure magnitude.
The table visualization can show multiple dimensions, as well as multiple measures, as columns.
The following example shows the number of Wikipedia events for City Name
New York by Channel for the latest 6 hours, with a comparison to the previous period:
Click the paintbrush icon on the right to access layout options. Polaris displays a nested layout by default—shown above. Click and drag the dimensions in the show bar to rearrange their order and change the nesting.
Hover over the first column heading—City Name > Channel in the above example—to display the Swap axes icon. Click the icon to swap the splits assigned to the axes.
Select the Flat row layout to display a column for each dimension.
The Overall rows display the dimension totals. In the above example, there are 10 events that match the applied filters—9 for the
#en.wikipedia Channel and 1 for
#pl.wikipedia. Of the 9
#en.wikipedia events, 5 are for City Name
London and 4 are for
The line chart visualization is designed to demonstrate a trend over time. It's useful for showing temporal dimensions.
The following example line chart displays two measures—Number of Events and Session Length—over time for four Country values.
You can display two continuous metrics on the same chart with two axes. To do this, select two measures and then click the paintbrush icon in the right pane. Select Show measures in: Cell and Dual axis: Yes.
The following example line chart displays the same Number of events and Session length measures over time as the above chart. On the dual axis chart, the data for the four selected countries displays as a single line for each measure.
In contrast to the line chart visualization that displays lines for multiple dimensions on a single chart, the sparkline visualization displays multiple line charts—one for each dimension.
The following example sparkline chart displays the number of events over time for five Country Name values:
Hover over the first column heading—Country Name in the above example—to display the Swap axes icon. Click the icon to swap the splits assigned to the axes.
The vertical bars visualization shows each dimension in vertically oriented time buckets.
The horizontal bars visualization shows each dimension in horizontally oriented time buckets.
The heatmap is a visualization that shows two dimensions as a 'matrix'. The heatmap visualization is similar to the grid visualization and it works particularly well when one or both of the dimensions are continuous.
The spot matrix shows the distribution of events on a two dimensional matrix using circles, with larger circles representing a greater number of events.
The treemap illustrates how the values of a dimension combine to make up the whole. It is particularly suitable for hierarchical dimensions.
The sunburst, also known as a donut or a pie chart with one split, is a visualization that represents the ratios between the values of a dimension. When rendering multiple dimensions, each is subdivided to show proportional representation.
The geo marks visualization is the natural choice for dimensions that represent geographically encoded data. It can work with country encoded data. To use geo-oriented visualizations, modify the data cube configuration to make the country encoded data to be Geo type.
The geo shade visualization, also known as a Choropleth map, is another way to represent geographically encoded data. To use geo-oriented visualizations with country encoded data, set the data type to Geo in the data cube configuration settings.
The stacked area chart lets you see the overall trend, while also showing contributions of individual dimensions. Unlike the line chart, the area chart lets you see what the total of values added together.
The records visualization shows the raw data underlying the data cube, allowing you to see all dimensions that are in each record. This view can be useful for certain debugging issues. You can also view your records in groups for specific values of one or more dimensions by adding the dimensions to the show bar.
The records table visualization shows the raw data underlying the data cube, in table format. To filter the data, click any value in the table and select Exclude.
The Sankey visualization shows the flow from one set of values to another. In Sankey diagrams, the width of each connection is proportional to the flow volume between the values.
You can use a Markdown tile to add formatted text to a dashboard. For more information, see the Dashboards overview.