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Tableau 2020.2: Understanding Relationships

Tableau introduced relationships in version 2020.2 to combine data from different tables for analysis. Relationships allow for less data preparation, more use cases per data source, and greater trust in results by always preserving all measures, unlike joins. Relationships appear as a new element in the data model pane and can help analyze order item details with product data while representing unmatched quantities as zeros.

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92 views2 pages

Tableau 2020.2: Understanding Relationships

Tableau introduced relationships in version 2020.2 to combine data from different tables for analysis. Relationships allow for less data preparation, more use cases per data source, and greater trust in results by always preserving all measures, unlike joins. Relationships appear as a new element in the data model pane and can help analyze order item details with product data while representing unmatched quantities as zeros.

Uploaded by

ARCHANA R
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Relationships

Reference: https://www.tableau.com/about/blog/2020/5/relationships-part-1-meet-new-tableau-
data-model

In the update 2020.2, tableau introduced ‘Relationships’. These are used to combine data from
different tables for analysis.

It appears like the below image in the Tableau’s version 2020.2.

Figure 1: Relationships in Tableau Public 2020.2


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Relationships can help you in three key ways:

1. Less upfront data preparation: With relationships, Tableau automatically combines only the
relevant tables at the time of analysis, preserving the right level of detail. No more pre-
aggregation in custom SQL or database views!

2. More use cases per data source: Tableau’s new multi-table logical data model means you can
preserve all the detail records for multiple fact tables in a single data source. Say goodbye to
different data sources for different scenarios; relationships can handle more complex data
models in one place.

3. Greater trust in results: While joins can filter data, relationships always preserve all measures.
Now important values like money can never go missing. And unlike joins, relationships won’t
double your trouble by duplicating data stored at different levels of detail.

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Example 1: Shows all rows even if no values match or Represents the unmatched measures as zeros.

For order item details and products, it will show quantity as zero for those product categories which
have no ordered items. [Using ZN(SUM(Quantity))]

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Figure 2: Product Category wise Quantity as per order item details table

You can refer to the reference link for more information.

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