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Many-to-Many Relationships & How to Be in a Good Relationship

The Many Faces of Data Relationships

While having a many-to-many relationship may sound steamy to some of you, when it comes to the data in your tables it can get complicated. That is, unless you understand the different scenarios, their resolutions, and how to build a good relationship with your data. Just as a general overview, a relationship specifies the logic used to combine data from one or more tables. You create relationships by connecting fields between two or more tables, and this determine what data is reflected in a dashboard widget (such as a pivot table).  There can be 3 types of relationships:

  • One-to-One relationship - In this scenario, the field used to connect both tables only has unique values in every row.
  • One-to-Many relationship - In this scenario one table holds unique values for every row, but the other table holds duplicate values for any or all of the corresponding values in the first table.
  • Many-to-Many relationship - In this scenario, on both sides of the table there are duplicated values, causing excessive calculations for every query run against it

Understanding a M2M Relationship and Its Consequences

Definition: You can consider the general rule that when a field from two or more tables contains the same value, and these values are duplicated in both tables, a connection has been made based on this field a many-to-many relationship is created.

We know, it’s confusing, so here’s a business case example: a hotel can have a data table with reservation data and a table with payment data. In both tables the name of the guest is stored. A guest can have multiple reservations under their name, as well as multiple payments for their stay recorded in their name. If a relationship between the reservation and payment table was created based on the guest’s name, a many-to-many relationship would be created (as the guest’s name appears multiple times in each table).

Many-to-Many Relationship

Negative Consequences: The problem with this kind of relationship is that it can create complex data sets which either: do not return the correct results, or use excessive computing resources and don’t return any results. Both scenarios lead to data havoc such as creating duplicates, incorrect results, and performance lags, which is why many people try to avoid many-to-many relationships all together.

Resolutions: There are several methods to resolve or bypass a many-to-many relationship that should be chosen based on the business model and the logic of the business questions at hand. But, the first step is to test to see if you are dealing with a many-to-many relationship (see next section), and then according to the schema logic, apply the best resolution. 

Determine What Kind of Relationship Your In

You can easily test if a relationship is Many-to-Many by checking the data modeling of the relationship and determining the exact number of unique and duplicate values on each side of the relationship.

  1. If you get the same value for both the unique and duplicate values, then there is no duplication, and this relationship is either a One-to-Many or a One-to-One. Continuing with our business case, this would mean that all Guest IDs appear only once--making all values unique. You can stop investigating at this stage given that even if the other side of the relationship has duplicate values for Guest ID, you’ll still be dealing with a One-to-Many relationship--where the unique values are the ‘Reservations’ side, and the duplicate values are on the ‘Payments’ side.
  2. If the number of duplicate values are higher than the number of unique values, then this side of the relationship has duplicated values and you will need to investigate the other side of the relationship:
  • If the other side of the relationship yields unique values, this is a one-to-many relationship.
  • If not and there are more than two tables connected to this relationship, we've got a many-to-many relationship on our hands.

The Best Ways to Resolve M2M Relationships

Read more here...

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Tags: BI, Big, Data, cloud, data, tables


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