All tagged Data Preparation

When you union tables in Tableau, you append two tables of data vertically. That means if you have two tables that are 1,000 rows each, the result of the union will be a single table that is 2,000 rows. Shared columns will align automatically, and unique columns will not.

Unions provide the ability to relate multiple tables that contain different data types of structures without worrying about granularity. Many times, fact tables that primarily consist of measures cannot be joined together because the join would lead to undesired row duplication.

One of the primary ways we combine multiple tables of data in Tableau Prep is with joins! Joins allow us to merge multiple tables of data horizontally on a common key.

One of the things that distinguishes joins in Tableau Prep from Tableau Desktop are all the join types we can create! In Tableau Desktop, we're limited to Left, Right, Inner and Outer joins. In Tableau Prep we have those four options with the addition of Left Unmatched, Right Matched and All Unmatched. Those unmatched join types provide greater flexibility to create customized workflows and identify values which did not match in our join clause.

Do you want to use Tableau to look at running headcount over time? For example, you work with employee level data which has Start Dates and End Dates and you'd like to be able to see how the total active headcount at the company has changed over the last 8 quarters.

That's something Tableau can do, but it isn't going to work out of the box. It's going to take some creative data structuring and calculations to get things working.

In 2020, Tableau unveiled a new way to combine multiple tables of data in Tableau Desktop: a Relationship. Billed as "smart joins", relationships provide the capability to merge multiple tables of data horizontally on shared dimensions (like a join). However, they don't duplicate data when the tables being combined have different levels of granularity.

So, are they better than joins? Not always! Relationships can reduce data duplication and improve performance, but a data set that is generated from a relationship might present blind spots if the data isn't perfectly clean.

A huge benefit of using joins in Tableau is that we can combine data from multiple tables into a single table for analysis. When those tables are in different databases, it can be a little tricky to work out where to start. If you know how to join tables within the same database, that's great! Cross-database joins only take a few more steps to execute, and I'll walk you through them in today's video.

How do you join two tables together in Tableau when they don’t share the same common field? Or what if that common field is slightly different in both tables? A Join Calculation can help solve a lot of those problems! In this video, we take a look at how to use a join calculation to join tables with mismatched fields.

Are you having trouble getting your data to work in Tableau the way you want? Join us to learn how to prepare data for analysis in Tableau!

You can find a thousand tutorials on how to use Tableau, but they're all worthless until your data is structured for analysis in the format that Tableau likes. Don't get stuck in your analysis journey because Tableau doesn't like your data.

6 data formats to avoid, 8 key rules for structuring data, and 1 video you can't miss. Check out the recording to learn about the key concepts you can utilize to prepare for your data for Tableau.

Groups are an incredible feature in Tableau, but they are limited. For instance, you can’t leverage a group for a blend or cross-database join.

If you need to use the output of a group for data preparation, you will probably want to turn it into a calculated field. Rather than doing it slowly by hand, why not leverage a quick calculation which can speed up the process?

Check out the video below to learn how you can easily turn a group into a calculated field in Tableau.