Imply provides a wide array of expressive visualizations that enable fast, interactive exploration of your data. The 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, the best visualization for the selected dimensions is automatically selected.
You can choose the visualization you prefer by clicking on the button highlighted in the image below, which is located to the right of the filter and split bars.
The visualizations are described below. The screenshots show visualization types in Pivot Classic. The equivalent types exist in Pivot 2.0.
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:
If you add an Overall visualization tile with conditional formatting to a dashboard page, the colored icon next to the page name indicates the severity of the data on the page. See Dashboard pages for more information.
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.
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 is the natural visualization to show temporal dimensions. This is the perfect visualization to demonstrate a trend over time.
The line chart also has the ability to compare the segments of the first shown dimension to each other over time.
Rather than showing lines for multiple dimensions on a single chart, the sparkline visualization shows multiple line charts, one for each dimension.
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 allows you to see 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 pie chart when using only 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 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.
If your event data contains latitude and longitude coordinates, you can use the street map visualization to pinpoint events to precise locations on a map.
The Imply street map visualization is enabled with an account-level feature flag. To enable the feature, contact your Imply support representative.
Once Imply has enabled the feature, create dimensions for your latitude and longitude data. Add the dimensions to the show bar and select Street map from the visualization selector on the right side of the page.
You can click the paintbrush icon to change the default Grid display to Blobs, for a circular representation of the data. The darker the grid or blob color, the higher the concentration of events at that location. Click anywhere in a grid or blob to see the corresponding latitude and longitude coordinates and the number of related events. Click Filter to filter the event display by specific latitude and longitude coordinates.
The stacked area chart is similar to the line chart, and can be used in similar situations. Unlike the line chart, the area chart lets you see what all the values add up to together. This lets you see the overall trend, while also showing the individual contributions of different dimensions.
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 Managing dashboards.