How can you use a bridge table in a snowflake schema?

To be fair, if you are in a situation where you have a many to many relationship between a fact and dimension table. Go back to the drawing board! You designed an incorrect dimension with non-unique keys.

There are few examples of bridge tables like: - Bridge between Customer and Address Bridge between Supplier and Address There are cases where additional group table is required. For example, Employee and Skills. Employee will have many to many relationship with skills tables. These skills can be clubbed into multiple groups. So instead of creating bridge between Employee and Skills, Bridge can be created between Employee and Skill Groups. An additional table can be generated with Skill Group Key, Skill Key and Skill list. Skill list will contain Concatenated comma separated list of skills in that group in ascending order. Bridge table will be between Employee and Skill Group

Agree that you should always try as much as possible to avoid many to many relationships, and it is recommended that you step back and look at the design again. However, there are unique cases where it is not avoidable. In a traditional datawarehouse a bridge table helps with overall performance of queries

If you are creating bridge table then create it as Type 2 SCD, e.g. a normal bridge between table 1 and table 2 will have following columns: - Table1_Key (Primary key of Table 1) Table2_Key (Primary Key of Table 2) Effective_Date Active_Indicator Table1_Key, Table2_Key, Effective_Data will form composite key. There will only be 1 current record with Active_Indicator = "Y" Some cases additional group table is required. e.g. Employee will have many to many relationship with skills tables. Skills can be clubbed into groups in an additional table with columns Skill Group Key, Skill Key and Skill list (Concatenated comma separated list of skills in that group in ascending order). Bridge table will be between Employee and Skill Group

When working with bridge tables, always ensure indexes are optimized on the joining keys to speed up query performance. Also, be cautious of Cartesian products; always validate your joins to prevent unintentional data explosions. Remember, while snowflake designs can be normalized, the end goal is efficient querying. Regularly monitor query plans and optimize as needed. Efficient design combined with vigilant monitoring ensures your warehouse remains agile and performant.

Bridge will be between lowest level dimension of hierarchy e.g. Customer and Address. Address will be further snow flaked into city, region, state and country dimensions. It is recommended to have presentation layer and have view to represent a Flat dimension with all relevant columns from snow-flaked dimensions e.g. view for Address key with Address Details, City, postal code, Region, State and Country in single view. These views can be materialized for better performance.

1. Scalable & Reusable: Many to many relationships can be easily managed by creating bridges between 2 dims, than having fixed no. inf columns in single dimension. 2. Easy Maintenance (when you need to modify a subset of data, since it is compartmentalized, lesser risk, lesser testing, easier to handle. 3. Helps in Easier Data Analysis, Bridge with groups provide more analytical information 4. Master Data And Reference Data can be managed more easily by maintaining single version of truth and these can be access by all applications within enterprise uniformly

Sometimes a bridge table will create a "semi-additive fact". This is where there is no convenient quantity or percentage to add to the calculation of the measure to apportion the total value to each item in the bridge. The lines of your report will aggregate just fine, but the total of all lines will contain duplicates and, consequently, you'll be presenting a figure much higher than you should. This can be very confusing to a report writer. Your model will stop being intuitive and errors will creep in.

Need to create long query to join multiple objects Minor performance issues and materialization may be required in few cases. Needs to educate Business and Data Engineers on these relationships and definitions A documented Conceptual Data Model and Physical Data Model helps to understand such complex Data Models easily. I strongly recommend use Snowflake Dimensional Modelling with use of Bridge Tables and Grouping Tables and create presentation layer on top of these. Conceptual Data Model and Physical Data Model and make it accessible to all Technical and Business Teams for better understanding.

I strongly recommend use Snowflake Dimensional Modelling with use of Bridge Tables and Grouping Tables and create presentation layer on top of these. Conceptual Data Model and Physical Data Model and make it accessible to all Technical and Business Teams for better understanding.

Check how it was possible to come to a many to many relationship, how you can change it to one to many. WITHOUT a brudge table. What data will you lose? Is this acceptable? If not, you likely need to change your fact table to accomodate with the correct aggregation level.

I use bridge tables in situations where you need to remodel recursive relationships that have more than two or three levels .