The Power of Flexibility: Tableau’s New Relationship Functionality

Contributors: Dr Shailee Choudhary , Jas Gaurav Singh , Garima Chawla

Are you tired of the tedious process of joining data in Tableau? Say goodbye to Joins and hello to Tableau’s powerful relationships feature! In this post, we’ll explore how using relationships can simplify your data blending process, improve performance, and give you more flexibility in creating visualizations. Get ready to unlock a whole new world of possibilities with Tableau’s Relationships.

Introduction to Tableau and its features

Tableau is a powerful data visualization tool that has revolutionized the way businesses and organizations analyze and understand their data. Tableau’s drag-and-drop interface makes it easy to create beautiful visualizations of your data, and its built-in statistical and geographic features make it even easier to find insights in your numbers.

Tableau’s new relationship functionality makes it possible to connect tables and entire datasets with ease. This is made possible by the ability to create relationships between data points. Relationships are built through a combination of “joins” and “blends”, which are used to link two or more tables together in unique ways. With this feature, users can quickly uncover hidden insights in their data by making correlations between different datasets tables.

What are Relationships in Tableau?

Tableau’s relationships feature allows you to connect data tables without the need for joins. This means that you can work with multiple data tables in a single view, and the data will be dynamically linked. This is a powerful tool that can save you time and effort when working with complexity.

When it comes to working with data in Tableau, it’s important to understand the difference between joins and relationships. Joins are static way of combining data that works on physical layer and finally results in merged table, while relationships are dynamic and flexible way to combine data, which works on logical layer, and the resultant tables remain separate and distinct.

Relationship in Tableau works on common fields to combine tables, but it does not merge them together. When you create a relationship, Tableau automatically creates a link between the two data sources based on the common field. Relationships use joins, but they are handled automatically by Tableau in the backend. For example, Based on Sample_Superstore dataset, lets map a relationship based on two data tables that contain information on Orders and Returns. You can create a relationship between them based on the order ID field as depicted in the Figure 1.

Let’s dig down into the information provided in the relationship depicted:

1. Two independent tables Orders and Returns are combined using relationship.

2. The common field, Order ID, is used for joining criteria.

3. Cardinality refers to numerical relationship (One/Many) between rows of table1 and the rows in table 2. There are three types of cardinality

a. One-to-one Relationship

b. One-to-Many Relationship or Many-to-One Relationship

c. Many-to-Many Relationship

Let’s consider few examples to understand the difference:

In the above figure both m and n indicates many. The first mapping is between Customer and Order table and it depicts that one customer can place multiple orders. Hence the cardinality is one-to-many .

In the second mapping between Student and College table, many students can join one college is depicted. Hence, the cardinality is Many-to-One.

The third mapping between Student and Courses table depicts that many students have joined in many courses. Hence, the cardinality is Many-to-Many. Let’s imagine a situation that one students is allowed to enroll in one course only, in that case it will be one-to-one relationship.

4. Referential Integrity means a row in one table will always have a matching row in the other table, as determined by the value of their combined (shared) fields.

Both cardinality and referential integrity are automatically defined by Tableau but if required we can modify it.

Relationships are created between the logical tables at top-level, logical layer of your data source as depicted in Figure 3. If you click on Orders table, it presents the dataset related to it whereas in case you select Returns table the dataset changes. Hence, the related tables remain separate and distinct, and they are not merged as a single new physical table. Relationships preserves the native level of granularity of each table, which means no values will be dropped and will minimize redundant data as possible.

Relationships work on combining the data automatically per sheet. During drag-and-drop, Tableau evaluates the relationships between the tables and fields so that queries can be written with proper join types, aggregations, and null handling.

Tableau’s Relationships feature is a powerful tool that can save you time and effort when working with complex data sets. By creating flexible relationships between data tables, you can create dynamic visualizations that change as your data changes. This is a great way to explore data and create new insights.

Conclusion

Tableau’s new Relationship functionality is an incredibly powerful tool that enables users to work dynamically with their data and create more meaningful insights. By allowing users to define relationships between different tables, they can access a new universe of possibilities to create meaningful visualizations. Additionally, the ability to filter, group and sort data based on these relationships makes working in Tableau much easier and more efficient. We are excited about this feature because it gives us a great way to unlock deeper levels of analysis from our data and uncover hidden trends that would otherwise go unnoticed.

Stay tuned for our next post about joins, where we’ll get into detail discussion about:

How Relationships differ from Joins