![]() ![]() If the data source isvery large, you can set a context filter to includeonlybreakfast products. Your task is to find the top 10 breakfast products by profitability for all stores. You can set one or more context filters to improve performance.Ģ) Create a dependent numerical or top N filter -You can set a context filter to include only the data of interest, and then set a numerical or a top N filter.įor example, suppose you're in charge of breakfast products for a large grocery chain. However, you can set one or more categorical filters as context filters for the view.You can think of a context filter as being an independent filter (Option stating - To create a dependent filter eliminated here).Any other filters that you set are defined as dependent filters because they process only the data that passes through the context filter.ġ) Improve performance -If you set a lot of filters or have a large data source, the queries can be slow. That is, each filter accesses all rows in your data source without regard to other filters. ![]() hyper extract.Ģ) Create and refresh extracts faster:While Tableau has always optimized performance for creating and refreshing extracts, version 2020.3 supports faster extract creation and refreshes for even larger data sets.ģ) Experience better performance when interacting with views that use extract data sources:Although smaller extracts continue to perform efficiently, larger extracts perform more efficiently.īy default, all filters that you set in Tableau are computedindependently. tde extracts that you previously had to create separately into a single. hyper extracts can support more data, you can consolidate. hyper extracts, the primary benefits include the following:ġ) Create larger extracts:You can create extracts with billions of rows of data. hyper format take advantage of the improved data engine, which supports the same fast analytical and query performance as the data engine before it, but foreven larger extracts.Īlthough there are many benefits of using. Instead of the standard 'Customer' field name, it contains an abbreviated version called 'Cust.'īeginning in version 10.5, when you create a new extract, it uses the.hyperformat instead of the. Now suppose a fourth table, 'August2016', is added to the underlying data. The table names are 'May2016,' 'June2016,' and 'July2016.'Ī union of these tables creates the following single table that contains all rows from all tables. You can also create your own calculation or, if possible, modify the underlying data to combine the non-matching fields.įor example, suppose you have the following customer purchase information stored in three tables, separated by month. When field names in the union do not match, fields in the union contain null values.You can merge the non-matching fields into a single field using the merge option to remove the null values.When you use the merge option, the original fields are replaced by a new field that displays thefirstnon-null value for each row in the non-matching fields. Topic 12: Explain how continuous fields are displayed/ Explain the default aggregation for measuresīy default, both field names are present in the Union, but contain several null values!.Topic 11: Use color from the marks card/ Connecting to & Preparing Data/ Create live connections and extracts.Topic 10: Explain the difference between discrete date parts and continuous date values/ Explain when to use a parameter.Topic 9: Explain what kind of information dimensions usually contain/ Create sets by using marks and the data pane.Topic 8: Change data type for a data field/ Create a map using geographic data.Topic 7: Add interactive elements for consumers/ Explain the differences between using live connections versus extracts.Topic 6: Explain what kind of information measures usually contain/ Assign a geographic role to a data field.Topic 5: Create groups by using marks, headers, and the data pane/ Create a chart to show specific values.Topic 4: Configure a dashboard layout and create device-specific dashboards/ Add relationships to a data source.Topic 3: Describe how an aggregated measure changes when dimensions are added to a view/ Explain when to use a join versus a relationship.Topic 2: Explain the difference between dimensions and measures/ Create a data source that uses multiple connections.Topic 1: Change default properties for a data field/ Create a live connection to a data source.
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