In versions of Tableau before 2020.2, the data model has only the physical layer Tables added to the physical layer (joined or unioned) create a single, flattened table (denormalized) for analysis. In previous versions of Tableau, the data model in your data source consisted of a single, physical layer where you could specify joins and unions. Each logical table contains physical tables in a physical layer. In Tableau 2020.2 and later, a logical layer has been added in the data source. This gives you more options for combining data using schemas to fit your analysis. In Tableau 2020.2 and later, the data model has the logical (semantic) layer and a physical layer. In previous versions of Tableau, the data model had only the physical layer. Relationships, part 3: Asking questions across multiple related tables (Link opens in a new window)Īlso see video podcasts on relationships from Action Analytics (Link opens in a new window), such as Why did Tableau Invent Relationships? (Link opens in a new window) Click "Video Podcast" in the Library (Link opens in a new window) to see more.Relationships, part 2: Tips and tricks (Link opens in a new window).Relationships, part 1: Introducing new data modeling in Tableau (Link opens in a new window).Use Relationships for Multi-table Data Analysis.Learn more: For related information on combining data using relationships, also see these topics and blog posts: Your upgraded workbooks will work the same as they did before 2020.2. The behavior of single-table analysis in Tableau has not changed. Important: You can still create single-table data sources in Tableau that use joins and unions. Each logical table can contain one or more physical tables. In previous versions of Tableau, the physical layer was the only layer in the data model. You can think of it as the Join/Union canvas. The physical layer of the data model is where you can combine data using joins and unions. You do not need to specify join types for relationships during analysis Tableau automatically selects the appropriate join types based on the fields and context of analysis in the worksheet. When you combine data from multiple tables, each table that you drag to the canvas in the logical layer must have a relationship to another table. You can also think of it as the Relationships canvas, because you combine tables here using relationships instead of joins. The top-level view that you see of a data source is the logical layer of the data model. Physical tables are merged into a single, flat table that defines the logical table
Logical tables remain distinct (normalized), not merged in the data source Level of detail is at the row level of merged physical tables Level of detail is at the row level of the logical table Logical tables are like containers for physical tablesĭouble-click a logical table to see its physical tables Physical tables can be joined or unioned to other physical tables Logical tables can be related to other logical tables Tables that you drag here are called physical tables Tables that you drag here are called logical tables Join/Union canvas in the Data Source page Relationships canvas in the Data Source page In this example, the Book logical table is made of three, joined physical tables (Book, Award, Info). Physical tables can be combined using joins or unions. They act like containers for physical tables.ĭouble-click a logical table to open it and see its physical tables. Logical tables can be combined using relationships (noodles). The top-level view of a data source with multiple, related tables. Double-click a logical table to view or add joins and unions.
Think of the physical layer as the Join/Union canvas in the Data Source page. Each logical table contains at least one physical table in this layer. You combine data between tables at the physical layer using joins (Link opens in a new window) and unions. For more information, see Use Relationships for Multi-table Data Analysis. Think of this layer as the Relationships canvas in the Data Source page. You combine data in the logical layer using relationships (or noodles).