Tableau Relationships are a versatile approach to combining data for multi-table analysis. Relationships give data sources a lot of flexibility while relieving a lot of the work of managing joins and degrees of information to enable correct analysis.
Tableau Relationships bring to light nuance in your data that was previously difficult to overlook, such as the level of detail in your measures. Unmatched values across tables, as well as table cardinality
Consider a relationship to be a contract between two parties. When you create a viz with fields from these tables, Tableau pulls data from these tables and uses that contract to construct a query with the required joins. We propose that you utilize relationships as your primary method of merging data because it makes data preparation and analysis easier and more straightforward.
In this blog, you’ll learn more about the Basics of creating Tableau Relationships and new Data Models.
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What is Tableau?
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In the Business Intelligence Industry, Tableau is a strong and quickly developing Data Visualization application. It facilitates the conversion of raw data into an understandable format. Tableau assists in the creation of data that professionals at all levels of a corporation can comprehend.
Christian Chabot, Pat Hanrahan, and Chris Stolte founded Tableau, a data visualization and business intelligence platform, in 2003. It gained traction as firms sought relevant insights from different data sources while also collaborating with their workforce.
Tableau excels at Data Visualization, making it an excellent tool for analyzing massive amounts of data. Tableau has helped top enterprises in a variety of industries save processing time and become more data-driven, all while assuring Flexibility, Security, and Reliability.
Key Features of Tableau
Tableau is a superior alternative to other BI solutions because it provides more functionalities. Here are a couple of such examples:
- It has a big variety of integrations to select from.
- A one-of-a-kind drag-and-drop feature.
- Your queries or questions are converted into Visual Representations.
- Tableau is available on mobile, web, and desktop platforms.
- It enables you to build a variety of Visualizations to aid in data investigation.
- Tableau provides over 200 connectors that allow users to connect to external data sources such as RDBMS, the cloud, and spreadsheets securely.
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Data Model in Tableau
A Data Model is associated with each data source that you build in Tableau. A Data Model can be thought of as a graphic that instructs Tableau on how to query data in the associated database tables.
The structure of the Data Model is created by the tables that you add to the canvas on the Data Source page. A simple Data Model, such as a single table, can be used. It can also be more complicated, with several tables utilizing various combinations of links, joins, and unions.
The Data Model is divided into two layers:
- Logical Layer: In the logical layer, you use relationships to mix data (or noodles). Consider this layer to be the Data Source page’s Relationships canvas. See Use Relationships for Multi-table Data Analysis for further information.
- Physical Layer: At the physical layer, joins and unions are used to combine data between tables. In this tier, each logical table contains at least one physical table. Consider the physical layer to be the Join/Union canvas from the Data Source page. To examine or add joins and unions, double-click a logical table.
Tableau Relationships to Analyze Multi-Table Data
To determine how to query data, Tableau needs rules to follow semantics. There are two forms of semantic behavior in relationships:
- Measures automatically aggregate to the level of detail of their pre-join source table using Smart Aggregations. This differs from joins, where measures forget their source and adopt the level of detail of the post-join table.
- Contextual joins: Unmatched values are handled independently per viz, thus a single relationship can support all join types at the same time (inner, left, right, and full)
The join type in contextual joins is defined by the combination of measurements and dimensions in the viz and their source tables. The diagram below depicts the 8 R’s of relationship semantics, with purple representing smart aggregation and teal representing contextual join behavior.
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Interpreting analysis findings from multiple connected tables
Tableau only retrieves data from tables that are relevant to the visualization. Each example displays the subgraph of tables that were connected to produce the result.
How to Set Up Tableau Relationships
Relationships are created in the data source’s logical layer. This is the canvas’s default view, as seen on the Data Source page.
Tableau Relationship Step 1: Drag a table to the canvas
The Edit Relationship dialogue box is shown. Tableau will attempt to establish the relationship automatically based on existing key restrictions and matched fields. If it cannot find the appropriate fields, you must pick them.
To modify the fields: Choose a field pair, then click on the list of fields below to find another pair of matching fields.
Tableau Relationship Step 2: Add another table to the canvas
If no restrictions are found, a Many-to-many connection is formed, with referential integrity set to Some records match. These default options are a safe bet and offer the greatest flexibility for your data source.
The default settings allow full outer joins and improve queries during analysis by pooling table data before building joins. Each table’s column and row data becomes available for investigation.
In many analytical contexts, adopting the relationship’s default parameters will provide you with all of the data you want for analysis. Even if your data is many-to-one or one-to-one, using a many-to-many connection will work.
If you know the specific cardinality and referential integrity of your data, you may alter the Performance Options settings to more correctly describe your data and improve how Tableau searches the database.
Tableau Relationship Step 2.1: Repeat the steps to add more tables.
As needed, repeat the procedures to add more tables.
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Operations on Tableau Relationship
Below are some basic instructions on Tableau Relationship which can come in handy!
Edit a Tableau Relationship
To launch the Edit Relationship dialogue box, click a relationship line. The fields used to describe the connection can be added, changed, or removed. To make a compound relationship, add more field pairs.
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Move a Tableau Relationship
Drag a table close to another table to shift it. Alternatively, hover over a table, click the arrow, and then choose Move.
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Remove a Tableau Relationship
Hover over a table, click the arrow, and then pick Remove to relocate it.
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View a Tableau Relationship
Hover your mouse over the relationship line (noodle) to reveal the matching fields that describe it. You may also view the contents of any logical table by hovering over it.
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Tips for Building Relationships
- The first table you drag to the canvas becomes the root table for your data source’s Data Model. After dragging out the root table, you can drag out any number of additional tables in any order. You must think about which tables should be associated with one another, as well as the matching field pairs that you specify for each relationship.
- Viewing the data from the data source before or during analysis might help you get a feel of the scope of each table before you start developing relationships. View Underlying Data for additional information. When a relationship is invalid, you can also use ViewData to access the underlying data of a table.
- At least one matched pair of fields must be present in each relationship. To make a compound relationship, combine numerous field pairs. The data type of matched pairs must be the same. This requirement is not altered by changing the data type in the Data Source tab. For queries, Tableau will continue to use the data type from the underlying database.
- Calculated fields can be used to establish relationships. When you construct the relationship, you can also indicate how fields should be compared by using operators.
- When you delete a table from the canvas, all of its descendants are also deleted. When the root table is deleted, all other tables in the model are likewise deleted.
Conclusion
Effective Data Analysis requires asking appropriate, precise questions and understanding the answers beyond the field labels. Before diving into SQL log files for an explanation if you obtain unexpected results, make sure you can articulate the relationships in your data—and your question—in simple English.
Relationships facilitate cross-table analysis with smart aggregations and more flexible analysis with contextual joins. Internalizing a couple of fundamental aspects of relationships will help you understand the strategies and tactics given here, reducing the need for you to memorize them.
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