Differences in implementation, that is, the presentation (structure) of data in tables.
A simple database is usually structured in normalized tables to minimize redundancy and optimize write operations to the table. This can be achieved by dividing large tables into smaller and less redundant tables, so that data of the same type is isolated in one place, so that adding, deleting and modifying a field can be done in only one table. Smaller tables are then joined to each other through certain relationships between them (this is done using foreign keys), which leads to many connections between tables when retrieving data.
On the other hand, the data warehouse is structured only for read operations, so the datawarehouse accepts some level of redundancy in the data as it speeds up reading. Datawarehouse data is usually structured in the so-called Starschema approach through the use of dimensional modeling. This means that you have 1 large table (Facttable) with all the relevant records and measures (total sales fx in $), and then many small tables (called dimension tables) that describe the values in the fact table. Dimensiontables can be something like Date, SalesCountry, SalesPerson, Product, etc., which describe the sales volume from the fact table. Dimension tables are then linked to facts with foreign keys, thereby creating a star-shaped figure with facts in the middle and all dimension tables around it in a circle that communicates with it.
NB: This is a very simple introduction, and you should, of course, refer to some datawarehouse data material to read more detailed information. Look for books by Ralph Kimball and Bill Inmon, they are gurus in the datawarehouse field.
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