Connect 3 Workbook Answers

[Niklas Terki]

Adata extract is a subset of information that is saved separately from the original dataset. It serves two purposes: to enhance performance and to utilize Tableau features that may not be available or supported in the original data. By creating a data extract, you can effectively reduce the overall data volume by applying filters and setting other limitations.

After a data extract is created, it can be refreshed with the latest data from the original source. 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.

Improved performance: Interacting with views that utilize extract data sources results in better performance compared to views connected directly to the original data. Extracts optimize query performance, resulting in faster data analysis and visualization.

Offline data access (Tableau Desktop): Extracts allow for offline access to data. This means that even when the original data source isn't available, users can still save, manipulate, and work with the data locally.

After 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.

Check the box for Incremental refresh, then indicate the table you want to refresh, choose a column in the database to identify new rows, and optionally set a minimum date range for the refresh.

Under Data Storage you can select either Logical or Physical tables. Logical tables store data in one extract table for each logical table in the data source. On the other hand, physical tables store data in one extract table for each physical table in the data source.

If your extract consists of tables combined with equality joins and meets the Conditions for using the Physical Tables option, you should select Physical Tables. This option performs joins at query time and can potentially improve performance and reduce the size of the extract file.

Tableau generally recommends that you use the default data storage option, Logical Tables, when setting up and working with extracts. In many cases, some of the features you need for your extract, like extract filters, are only available to you if you use the Logical Tables option.

The Physical Tables option should be used sparingly to help with specific situations such as when your data source meets the Conditions for using the Physical Tables option and the size of your extract is larger than expected. To determine if the extract is larger than it should be, the sum of rows in the extract using the Logical Tables option must be higher than the sum of rows of all the combined tables before the extract has been created. If you encounter this scenario, try using the Physical Tables option instead.

Instead of connecting to a database table, connect to your data using custom SQL instead. When creating your custom SQL query, make sure that it contains the appropriate level of filtering that you need to reduce the data in your extract. For more information about custom SQL in Tableau Desktop, see Connect to a Custom SQL Query.

If you want to secure extract data at the row level, using the Physical Tables option is the recommended way to achieve this scenario. For more information about row-level security in Tableau, see Restrict Access at the Data Row Level.

However, if you open the extract using the packaged data source (.tdsx) file or the data source (.tdsx) file with its corresponding extract (.hyper) file, you see all three tables that comprise the extract on the Data Source page.

Aggregation allows you to aggregate measures. You can also select Roll up dates to a specified date level such as Year, Month, etc. The examples below show how the data will be extracted for each aggregation option you can choose.

Most data sources support an incremental refresh. Rather than refreshing the entire extract, you can configure a refresh to add only the rows that are new since the previous time you extracted the data.

When you're working with a large extract, it can be helpful to create a smaller sample of the data. This allows you to set up your view without having to run lengthy queries every time you add a field to your analysis. You can easily switch between using the sample data and the complete data source by selecting the appropriate option in the Data menu.

The arrangement and connections between tables are stored in the .tds file, not in the .hyper file. Therefore, when you connect directly to the .hyper file, you lose this information. If you use logical tables storage for the extract, you won't see any references to the original physical tables.

You can remove an extract at any time by selecting the extract data sourceon the Data menu and then selecting Extract > Remove.When you remove an extract, you can choose to Remove the extractfrom the workbook only or Remove and delete the extract file. The latter optiondeletes the extract from your hard drive.

Locate the extract: Select this option if the extract exists but not in the location where Tableau originally saved it. Click OK to open an Open File dialog box where you can specify the new location for the extract file.

Beginning with version 2024.2, Tableau has added a new feature called subrange refresh for incremental refresh. This feature allows users to specify a minimum date range for refreshing extracts. For example, users can choose to refresh data from the last 14 days from the refresh date. By utilizing this feature, users can speed up extract refreshes and save on costs related to unnecessary full extracts.

This update introduces an additional step in the process. During an incremental refresh, Tableau first removes rows from the extract that match the previously recorded highest value. Subsequently, Tableau queries for all rows that have a value higher than or equal to the previous highest value. This approach ensures that any deleted rows are accounted for, along with any newly added ones.

Beginning with version 2020.4, extracts are available in web authoring and content server. Now, you no longer have to use Tableau Desktop to extract your data sources. For more information, see Create Extracts on the Web.

With the introduction of logical tables and physical tables in the Tableau data model in version 2020.2, extract storage options have changed from Single Table and Multiple Tables, to Logical Tables and Physical Tables. These options better describe how extracts will be stored. For more information, see Extract Your Data.

Beginning with version 10.5, when you create an extract it uses the .hyper format. Extracts in the .hyper format take advantage of the improved data engine, which supports faster analytical and query performance for larger data sets.

Similarly, when an extract-related task is performed on a .tde extract using version 10.5 and later, the extract is upgraded to a .hyper extract. After a .tde extract is upgraded to a .hyper extract, it can't be reverted to .tde extract. For more information, see Extract Upgrade to .hyper Format.

With regard to casing, this means that how Tableau stores values have changed between version 10.4 (and earlier) and version 10.5 (and later). However, the rules for sorting and comparing values haven't. In version 10.4 (and earlier), string values like "House," "HOUSE," and "houSe" are treated the same and stored with one representative value. In version 10.5 (and later), the same string values are considered unique and therefore stored as individual values. For more information, see Changes to the way values are computed.

If the Compute Calculations Now option was used in a .tde extract using an earlier version of Tableau Desktop, certain calculated fields were materialized and therefore computed in advance and stored in the extract. If you upgrade the extract from a .tde extract to a .hyper extract, the previously materialized calculations in your extract aren't included. You must use the Compute Calculations Now option again to ensure that materialized calculations are a part of the extract after the extract upgrade. For more information, see Materialize Calculations in Your Extracts.

You can use the Extract API 2.0 to create .hyper extracts. For tasks that you previously performed using the Tableau SDK, such as publishing extracts, you can use the Tableau Server REST API or the Tableau Server Client (Python) library. For refresh tasks, you can use the Tableau Server REST API as well. For more information, see Tableau Hyper API.

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