![]() ![]() If you are well-versed in data modeling and design, Tableau’s new data model lends itself well to concepts of data normalization and Star/Snowflake schemas. With the release of Tableau Desktop 2020.2, Tableau has managed to improve not only efficiency and performance but usability as well! However, efficiency and performance should not come at the cost of usability. We should be deliberate in how we store this data for efficiency and performance. By adding rows to the grid, you add columns in the table. The Table Designer opens and shows a grid with one default row, which represents a single column in the table that youre creating. We live in a world surrounded by data that is growing at exponential rates. Right-click on Tables and select Add New Table. Having worked with Tableau since its infancy, I can say that Tableau’s new data model is a natural progression towards product maturity. Tableau queries data in their natural level of detail, thus preserving granularity and functionality. You would simply define a logical item table with a plethora of item dimensions and relate it to your logical sales table through a field such as item_number. Gone are the days when you would need to store item descriptions every time an item sells in your Tableau data extract. Because Tableau does not combine logical tables into a single, flat table and instead keeps logical tables separate (in addition to having logical relationships defined), we are able to minimize data extract size by dividing the extract into different logical tables/groupings instead. Logical Table B also has a specific level of granularity. In the diagram above, Logical Table A and its underlying physical tables result in a single, flat table with a specific level of granularity. In the case where a logical table contains more than one physical table, these tables are first joined and/or unioned before relating to other logical tables. The following diagram illustrates these new layers within a single data source beginning with Tableau Desktop 2020.2:Ī logical table can contain one or more physical tables. You can create writable Azure Cosmos DB nodes all over the world, making your application fast. ![]() If you double-click into a logical table, you dive deeper into the physical layer and can define joins and unions. Azure Table Storage) Besides performance, another big difference between Azure Cosmos DB Table API and Azure Table Storage is that Azure Cosmos DB is made for geographic performance and availability (opens new window). By default, when you drop tables into the data source canvas in Tableau Desktop, you are working in the logical layer and can define relationships between logical tables. ![]() To delve deeper into Tableau’s new data model, we need to understand the difference between Logical and Physical tables. This new paradigm allows Tableau to maintain various levels of granularity across different tables while simultaneously minimizing data extract size due to efficiency. Beginning with Tableau Desktop 2020.2, Tableau introduces relationships and splits tables into logical and physical layers. In versions prior to 2020.2, users would define joins and unions between physical tables in which Tableau would create a singular, flat table for analysis. With the recent release of Tableau Desktop 2020.2 came the introduction of a brand-new data model. ![]()
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