It is often necessary to combine data from multiple places—different tables or even data sources—to perform a desired analysis. Depending on the structure of the data and the needs of the analysis, there are several ways to combine the tables. Show
Relationships vs JoinsThe default method in Tableau Desktop is to use relationships. Relationships preserve the original tables’ level of detail when combining information. Relationships also allow for context-based joins to be performed on a sheet-by-sheet basis, making each data source more flexible. Relationships are the recommended method of combining data in most instances. For more information, see How Relationships Differ from Joins. However, there may be times when you want to directly establish a join, either for control or for desired aspects of a join compared to a relationship, such as deliberate filtering or duplication. Note: Relationships eventually leverage joins (just behind the scenes). For example, a relationship across data sources will produce a cross-database join when the viz uses fields from tables in different data sources. As such, Improve Performance for Cross-Database Joins may be relevant. Common issues
Tip: While Tableau Desktop has the capability to create joins and do some basic data shaping, Tableau Prep Builder is designed for data preparation. If you need to do multiple joins, clean up field names, change data types, perform multiple pivots, or other sorts of involved data prep, consider using Tableau Prep Builder(Link opens in a new window). Create a join
After you've created a join, Join Your Data. To troubleshoot your join, see Join Your Data. Anatomy of a joinJoins are defined by their type as well as the join clause. Join typesIn general, there are four types of joins that you can use in Tableau: inner, left, right, and full outer. If you aren't sure what join type you want to use to combine data from multiple tables, you should use relationships.
Not all databases support all join types. If an option is unavailable in the join dialog, it is likely due to a constraint from your data source. Join ClausesA join is performed by setting up one or more join clauses. The join clause tells Tableau which fields are shared between the tables and how to match the corresponding rows. For example, rows with the same ID are aligned in the results table. Join clauses most often use the equality operator (=) which matches rows with the same values. It is also possible to perform non-equi joins, such as less than (<) and not equal (<>). A join can also have multiple join clauses. For example, if First name and Last name are stored in separate columns, it may be beneficial to join only if “First name = First name” and “Last name = Last name”. Both conditions will have to be true for rows to be joined. Alternatively, if the goal was to return results when the last name is shared but the first name is not, the join clauses could be “First name <> First name” and “Last name = Last name”. Join clauses can also contain calculations. For example, the join clause could be the concatenation of the name fields “[First name] + [Last name] = [First name] + [Last name]”. Note that not all data source connections support calculations in join clauses. About null values in join keysIn general, joins are performed at the database level. If the fields used to join tables contain null values, most databases return data without the rows that contain the null values. However, for certain single-connection data sources, Tableau provides an additional option to allow you to join fields that contain null values with other fields that contain null values. After you've set up your data source, on the data source page, select Data > Join null values to null values. If the option is greyed out, it is not available for your data source. Note that if you add a second connection to a data source that uses this option, the join reverts back to the default behavior of excluding rows with null values. Cross-database joinsTableau allows joins from tables in different data sources, albeit with some limitations from the database side on which platforms are compatible. Cross-database joins require a multi-connection data source—that is, you create a new connection to each database before you join the tables.
Note: Typically, joining tables from the same database yields better performance. This is because querying data that is stored on the same database takes less time and leverages the native capabilities of the database to perform the join. For more information on cross-database join performance, see Improve Performance for Cross-Database Joins. Other articles in this sectionWhich of the following is the underlying language in a relational database system?SQL is the query language that is used with relational databases. Relational databases and their management systems almost always use SQL as their underlying query language. NoSQL, or not only SQL, databases use SQL and other query languages.
Which of the following Access templates is used to track information about work duties your team needs to complete?Use the Access Task Management Database template to track a group of work items that you or your team need to complete.
Which of the following returns only records that have matching values in both tables?INNER JOIN statement returns only those records or rows that have matching values and is used to retrieve data that appears in both tables.
What is not true about field names quizlet?What is not true about field names? They can be up to 64 characters in length.
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