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Environments like Pandas and R support these types more elegantly. In the old times, users would manually perform dictionary encoding by creating lookup tables and translating their ids back with join operations. The category stores the actual strings, and the values stores a reference to the strings. In dictionary encoding, the data is split into two parts: the category and the values. A better solution is to dictionary encode these columns. Storing a data type as a plain string causes a waste of storage and compromises query performance. For example, a country column will never have more than a few hundred unique entries. However, often string columns have a limited number of distinct values. String types are one of the most commonly used types. The Fellowship of the Categorical and Factors. In my actual use anycodings_sql case, there are 7624 unique special codes.Pedro Holanda DuckDB - The Lord of Enums: This is for the purposes of building a anycodings_sql machine learning model. If an additional person existed that shared anycodings_sql an identical code, this obviously should use anycodings_sql the same column: id | name | age | 1577 | 2868 | 9375 | 1309 | 5240 | 2346 | 2223 How can I join and one hot encode special to anycodings_sql main to produce a table as below: id | name | age | 1577 | 2868 | 9375 | 1309 | 5240 | 2346 I have a second table special that encodes a anycodings_sql 1:M relationship: id | specialid I have a main table that looks like: id | name | age