How do you use foreign key reference in associated data analytics?
A foreign key is a column or group of columns in a relational database table that provides a link between data in two tables. It acts as a cross-reference between tables because it references the primary key of another table, thereby establishing a link between them. An example of this for analytics for associations is one table might have the names and email addresses of your members and then another table lists their member type, with a corresponding primary key, such as an identifying number. The foreign key would come into play in the first table with the names and email addresses of the members as the identifying number of the member types from the second table. This eliminates the need for repetition in the first table (i.e repeating the member type information) and allows each component of information to be stored only once.
While “normalized data”, or data that is split up into multiple data tables using foreign keys to prevent the repetition of data, is useful for data management and transactional processing systems, it isn’t the recommended storage structure for the data warehouse, or the more modern version, the data lake. Denormalized data, or “flat” data is when you put all of those split up data tables back together to make one big data set that includes the repeating information. With a data lake, you can import the raw normalized data into the data lake and then use ETL to create the new denomoralized data sets, which are very efficient for reporting and displaying on dashboards. With the data lake, you can easily put together visualizations for your association analytics and member engagement scoring from that denormalized data, without the need for a foreign key.
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