Column Categories

It's important to assign the appropriate categories to columns, as this ensures that the relevant functionality is available when building queries. For instance, building time intelligence calculations requires that the date/ time columns be categorized correctly. Likewise columns must be categorized as such in order to enable map visualizations. There are four main categories to choose from: date/ time, geo location, image URL, and machine learning. Columns that don't fit into those categories should be assigned 'None'.

Pyramid uses heuristics to automatically categorize columns, but they can be changed manually from the Categories if necessary.

Column categories can also be set on a per-user, ad-hoc basis in Discover; click here to learn more.


Choose none for columns that don't contain date/ time, geo location, image URL, or machine learning data.

Date/ Time

Date/ time columns should be given the correct category to enable time intelligence calculations later on. The following date/ time categories are avilable:

  • Year
  • Quarter
  • Month
  • Month Name
  • Day of Month
  • Week Day
  • Week Number
  • Day Name
  • Date
  • Date Time
  • Time

Geo Location

To ensure that map visualizations will be supported in Discover, assign geography columns to the relevant geo location category:

  • Country
  • State
  • County
  • City
  • Zip/ Post Code
  • Address
  • Longitude
  • Latitude
  • Other Geo Level

Custom Maps

Custom map files that have been imported will appear at the bottom of the drop down, with a blue globe icon. Click here to learn more about configuring custom maps.

Image URL

If you have a column of image URLs, as in the image below, select this category so that when the column is added to grids and charts, Pyramid will call and display the images. The column in the data source should be a list of image URLs. Click here to learn more about querying image URL columns.

Machine Learning

Any columns generated by machine learning algorithms should be assigned to the machine learning category.