3. Creating Joins in Tableau

 
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Overview

In many Tableau workbooks, you will need to join multiple data tables together. In this lesson, we will learn how to create joins in Tableau - which is surprisingly straightforward.

Summary

Joining datasets

- In many Tableau workbooks, more than one dataset is fed into the workbook
- Using data joins in Tableau can make analysis easier and improve performance
- Tableau provides 4 different types of joins out of the box

Types of joins in Tableau

- Inner: Includes matches only in both tables
- Left: Includes all values in the left table and all matches from the right
- Right: Includes all values in the right table and all matches from the left
- Full Outer: Includes all values from both tables. Members without matches will contain nulls

Transcript

When a workbook has multiple data connections, the way that you connect these together can have a significant impact on the performance of the workbook. In this lesson, we will learn how to join data sources and examine the advantages and disadvantages of this connection technique. When you're joining data sets together, the data sets need to come from the same source. So for example, two data tables that exist within a server or in this example, three different tabs on an Excel sheet.

To join datasets, we start by taking the primary data session which is going to be transaction data.

And with transaction data, I want to join fee earners by division because fee earners by division contains some additional data to this particular dataset.

And with the join, this will create a single data table that I can use in my extract.

So in addition to the seven columns currently showing in transaction data, when I apply, I join, I get some new columns including division, rank and budget rate per hour. Automatically Tableau creates what's called an inner join.

If we click on this particular icon, we can see how the join happens. Tableau has automatically found fee earner columns in both datasets. And on the left-hand side, here are the columns And on the right-hand side, we have the columns from the second data source.

for an inner join, We only include values with matches in both tables. So for example, if we had a fee earner in transaction data but did not appear in the fee earner, then they would not appear in the join data set.

Next up is a left join. a left join includes all the values in the left table, in transaction data and all matches from the right table.

If we have members without matches on the right hand side, they will show up as a null.

Right is the opposite where we include all the values and fee and are by division under then all the matches from the left table. And then the last option is the full outer which includes all values from both tables. Members without matches show up as nulls. In this example, all the data is matched correctly. So the choice of join won't make a huge difference.

The main advantage of using this joining technique and tableau is that you can choose the type of join you want to make. However, this option is limited.

If you have data sets from multiple data sources, you can't use joins. Instead, you need to use data blending through relationships which I'll show you in the next lesson.

Once you're happy with the joining question, you can simply select go to sheet one and begin creating your visualizations.

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