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![]() If you choose this option and your extract includes joins, the joins will be applied when the extract is created. Tableau uses Logical Tables as the default structure for storing extract data. This option also works well when your data includes pass-through functions (RAWSQL). If you want to limit the amount of data in your extract and use additional extract properties like filters, aggregation, or Top N, you should select Logical Tables. On the other hand, physical tables store data in one extract table for each physical table in the data source. Logical tables store data in one extract table for each logical table in the data source. Under Data Storage you can select either Logical or Physical tables. This section walks you through each field. You can configure numerous fields when creating an extract. If the Save dialog box doesn't display, see the Troubleshoot extracts section. Next, select a location to save the extract. This initiates the creation of the extract. Select the Incremental refreshbox, and then specify the table to refresh and a column in the database that will be used to identify new rows. You can extract All rows, Sample, or the Top N rows. Select the number of rows you want to extract. (Optional) Select Roll up dates to a specified date level such as Year, Month, etc. Select Aggregate data for visible dimensions to aggregate the measures using their default aggregation. For assistance with this step see the Data Storage section.Įxpand Filters to set up filters to limit how much data gets extracted based on fields and their values. ![]() Under Data Storage, select either Logical Tables or Physical Tables. There are multiple options available within your Tableau workflow to create an extract, but the main approach is explained below.Īfter you connect to your data and set up the data source on the Data Source page, in the upper-right corner, select Extract, and then select the Edit link to open the Extract Data dialog box. This means that even when the original data source isn't available, users can still save, manipulate, and work with the data locally. Offline data access (Tableau Desktop): Extracts allow for offline access to data. Extracts optimize query performance, resulting in faster data analysis and visualization.Įnhanced functionality: Extracts provide access to additional Tableau functionality that may not be available or supported by the original data source.įor instance, users can leverage extracts to compute Count Distinct, enabling more advanced calculations and analysis. Improved performance: Interacting with views that utilize extract data sources results in better performance compared to views connected directly to the original data. This allows users to work with extensive datasets efficiently. Handling large datasets: Extracts can handle massive amounts of data, even reaching billions of rows. Note: Starting from version 2024.1, Tableau introduces a feature that enables users to perform incremental refreshes on extracts using a non-unique key column. During the refresh process, you have the flexibility to choose between a full refresh, which replaces all existing content in the extract, or an incremental refresh, which only includes new rows since the previous refresh. Once a data extract is created, it can be refreshed with the latest data from the original source. By creating a data extract, you can effectively reduce the overall data volume by applying filters and setting other limitations. It serves two purposes: to enhance performance and to utilize Tableau features that may not be available or supported in the original data. A data extract is a subset of information that is saved separately from the original dataset.
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