Understanding Cardinalities in Power BI

Blogs

Unlocking the Power of AI in Microsoft Teams: Integrating OpenAI and Teams Toolkit
September 9, 2024
Leveraging SHORTCUTS for Data Management in Microsoft Fabric
September 9, 2024

Understanding Cardinalities in Power BI

When working with Power BI, relationships between tables form a key part of the data modeling process. One critical aspect of these relationships is cardinality. Understanding cardinality is essential for creating efficient, accurate reports, ensuring correct data interpretation, and optimizing performance.

Cardinalities

Cardinality in Power BI refers to the nature of the relationship between two tables based on the number of related rows in each table. It defines how one row in a table is related to rows in another table. Cardinality is a fundamental concept for defining table relationships in any data model.

Each model relationship is defined by a cardinality type. There are four cardinality type options, representing the data characteristics of the “from” and “to” related columns. The “one” side means the column contains unique values; the “many” side means the column can contain duplicate values.

There are four types of cardinality supported in Power BI:

  1. One-to-One (1:1)
  2. One-to-Many (1:*)
  3. Many-to-One (*:1)
  4. Many-to-Many (:)

When you create a relationship in Power BI Desktop, the designer automatically detects and sets the cardinality type. Power BI Desktop queries the model to know which columns contain unique values. For import models, it uses internal storage statistics; for DirectQuery models it sends profiling queries to the data source. Sometimes, however, Power BI Desktop can get it wrong. It can get it wrong when tables are yet to be loaded with data, or because columns that you expect to contain duplicate values currently contain unique values. In either case, you can update the cardinality type as long as any “one” side columns contain unique values (or the table is yet to be loaded with rows of data).

  1. One-to-One (1:1) Cardinality

In a One-to-One relationship, each row in the first table is related to one, and only one, row in the second table. This type of relationship is relatively rare and is used in scenarios where each record in both tables has a unique match.

Example:

Consider two tables: Employees and EmployeeDetails. If each employee has exactly one record in EmployeeDetails and vice versa, a 1:1 cardinality would be appropriate.

Use Case:

1:1 relationships are most useful when splitting a large table into smaller ones for better organization or performance reasons.

  1. One-to-Many (1:*) Cardinality

One-to-Many is the most common type of cardinality in Power BI. In this relationship, a single row in one table can correspond to multiple rows in another table. The “one” side is typically called the “lookup” table, and the “many” side is known as the “fact” table.

Example:

A table of Products  and a table of Sales. Each product can appear in multiple sales records, but each sale is tied to a single product which means each product can have multiple sales that can be in different city, quantity etc

Use Case:

1:* cardinality is essential for building data models that require aggregating data, such as tracking sales per product, customer transactions, or order histories.

  1. Many-to-One (*:1) Cardinality

A Many-to-One relationship is essentially the inverse of One-to-Many. Here, many rows in the first table can map to a single row in the second table. Power BI automatically detects this scenario when creating relationships, especially when modeling data imported from other systems.

Example:

A Sales table and a Customers table. Many sales records can be associated with a single customer, but each sale can only be attributed to one customer.

Use Case:

This cardinality is the standard for filtering and summarizing data. It’s used when analyzing large fact tables (such as transactions) with lookup tables (like customers, products, or dates).

  1. Many-to-Many (:) Cardinality

The Many-to-Many cardinality is used when both tables contain non-unique values in the respective columns involved in the relationship. This type of cardinality can be tricky as it allows multiple matches between rows in both tables. Power BI introduced support for : relationships to simplify modeling complex data scenarios without the need for bridge tables.

Example:

A Students table and a Courses table. A single student can enroll in multiple courses, and each course can have multiple students.

Use Case:

Many-to-Many relationships are especially useful in scenarios involving shared entities, such as users and roles, transactions across different platforms, or joint ownership of assets.

Cross Filter Direction

Each model relationship is defined with a cross-filter direction. Your setting determines the direction(s) that filters will propagate. The possible cross filter options are dependent on the cardinality type.

Cross filter direction in Power BI controls how filters are applied between two related tables. The direction determines which tables can filter the other. Power BI supports Single and Both cross filter directions.

  1. Single Cross Filter Direction

In this mode, filtering flows from one table to the other, but not the other way around. This is typical in a One-to-Many or Many-to-One relationship, where filters usually flow from the “one” (lookup) table to the “many” (fact) table.

Example:

In a model where Products and Sales are related in a One-to-Many relationship, filtering the Products table affects the Sales table, but not vice versa.

Use Case:

Single-direction filtering works well for most cases where you are summarizing data or drilling down into details (e.g., filter Products to see related Sales).

  1. Both Cross Filter Direction (Bi-Directional)

In Both direction filtering, filters can flow in both directions between tables. This is often used in complex models with multiple related tables, where you need data in both tables to influence each other.

Example:

In a model where Products, Sales, and Categories are all related, setting up bi-directional filtering allows filters from Sales to flow back to Products and Categories as needed.

Use Case:

Bi-Directional filtering is useful for many-to-many relationships and complex data models. However, it can have performance impacts, especially on large datasets, as it introduces more complexity into your data model.


Priyanka P Amte

Leave a Reply

Your email address will not be published. Required fields are marked *