#Tableau prep join vs union how to
#Tableau prep join vs union free

For example, to give a comprehensive view for geospatial analysis, city-level data often needs to be combined with country-level data. Oftentimes, data at different levels of granularity needs to be combined.

Or various statistical operations can be performed such as variance or standard deviation.ĭates and text-based fields can be aggregated as count, count distinct, maximum, or minimum (for text, maximum and minimum are based on sort order).

Numeric fields can be aggregated by various mathematical operations depending on the desired outcome.

How we aggregate multiple values down to a single value: For example, are we summing the number of shirts of each color for a total number of shirts? Are we taking the maximum hourly temperature reading over the course of a day and providing the daily max? Are we doing a count distinct of IP addresses to hit a webpage and measuring the unique pageviews?.

If we’re looking at voter turnout, is it at the level of political party? Political party and voting district? Political party, voting district, age bracket, and gender? The field or fields that determine what makes up a row are the grouping fields (in Tableau Prep).

How the outcome will be structured: Consider the new granularity-that is, what a row represents.

If your analysis requires aggregation, you need to consider two things:

#Tableau prep join vs union free

Watch a free training video on the Aggregate step in Tableau Prep.

#Tableau prep join vs union how to

Skip ahead to understand how to combine aggregated data or visit the OnlineHelp to learn how to aggregate values in Tableau Prep. Understanding aggregations can sometimes depend on what you’re trying to accomplish. This changes the data to a lower granularity (aka a higher level of detail). Aggregation is a mathematical operation that takes multiple values and returns a single value: operations like sum, average, count, or minimum.

For example, when looking at graduation data, granularity would describe whether a row in the data set represents a single person or the graduating class of a university.īut what if you wanted to take a data set of student information and identify a value for the graduating class as a whole-that is, decrease the granularity? Aggregation and granularity are complementary concepts. Granularity is the level of detail of the data. Reference Materials Toggle sub-navigationĭata is generated and analyzed at many different levels of granularity.Teams and Organizations Toggle sub-navigation.Plans and Pricing Toggle sub-navigation.