Pivot 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 within them by clicking on various graphic elements to select data segments for drill down and deeper analysis.

When you change the splits, Pivot will automatically guess the best visualization for the selected dimensions.

You can you 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.

Cube view vis selector menu

The visualizations are described below.


This is the overview visualization that’s shown when there is no split selected. It presents a numeric summary of the currently selected measures, applying any selected filter criteria.

Cube view


The table visualization is the default for any categorical (string) split. It presents a table view of the data with shading that aids visualization of measure magnitude.

Cube view table

The table visualization supports multiple nested splits, as well as multiple measures as columns.

Cube view table multi split multi measure

Line chart

The line chart is the natural visualization for temporal splits. This is the perfect visualization to demonstrate a trend over time.

Cube view line chart

The line chart also has the ability to compare the segments of the first split to each other over time. Note the different colored lines in the chart below.

Cube view line chart


The Grid table is a visualization that allows you to compare two dimensions to each other.

Bar chart

The bar chart is selected for numeric visualizations. It naturally acts as a histogram - showing the frequency distribution of occurrences over numeric buckets.

Cube view bar chart


The heatmap is a visualization that uses two splits and does not nest them within each other but rather shows them as a 'matrix'. It works particularly well when one (or both) of the dimensions are contentious.

Cube view heatmap


The map visualization is the natural choice for dimensions that represent geographically encoded data.

Cube view geo