![]() ![]() Relationships are the default for Tableau Desktop 2020.2 and higher. Joins however have a fixed level of granularity, which is defined by the type of join and join clauses you choose. This makes sense because the granularity of data can change in relationships, depending on the fields you are using in your dashboard. Please note that relationships only show a line between the tables ( DimProduct and DimProductSubcategory), whereas joins indicate the type of join by two circles:Ī key difference is that the preview of the data, at the bottom, will show only data from the selected table in relationships, compared to all data when using joins. In the following screenshot, you can see the data source canvas, with two datasets combined in a relationship on the left-hand side, and the same datasets combined using a join on the right-hand side. Each logical table contains physical tables in a physical layer.įor now, you can think of the logical layer as more generic, where the specifics are dependent on each view, whereas the physical layer dives deeper, starting from the data source pane. In Tableau 2020.2 and later, a logical layer has been added in the data source. To read all about the physical and logical layers of Tableau's data model, visit the Tableau help pages. It is the new default option in the data canvas therefore, we will first look into relationships, which belong on the logical layer of the data model, before diving deeper into the join and union functionalities that operate on the physical layer. RelationshipsĪlthough this chapter will primarily focus on joins, blends, and manipulation of data structures, let's begin with an introduction to relationships: a new functionality since Tableau 2020.2, and one that the Tableau community has been waiting for a long time. ![]() ![]() We will start this chapter off by explaining this new feature before we look into the details of joins, blends, and more. In version 2020.2, Tableau added functionality that will you allow you to join or blend without specifying one of the two methods in particular. In this chapter, we will discuss the following topics: It may be required in order to discover answers to questions that are difficult or simply not possible with a single data structure. This is possible by manipulating the data structure, which can help you achieve data analysis from different angles, using the same dataset. This functionality allows you to, for example, show the count of cities per country without changing the city dataset to a country level.Īlso, you may find instances when it is necessary to create multiple connections to a single data source in order to pivot 旋转 the data in different ways. In such cases, you will need to blend the data. Sometimes, you may need to merge data that does not share a common row-level key, meaning if you were to match two datasets on a row level like in a join, you would duplicate data because the row data in one dataset is of much greater detail (for example, cities) than the other dataset (which might contain countries). You can also join tables from disparate data sources or union data with a similar metadata structure. For this purpose, we can use joins, which combine a dataset row with another dataset's row if a given key value matches. You may need to use Tableau to join multiple tables from a single data source. Connecting Tableau to data often means more than connecting to a single table in a single data source. ![]()
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