When moving from a relational database like Microsoft SQL Server to a document database like Couchbase, it's essential to consider data modeling. A key aspect of this process is understanding how to model data using denormalization or referencing, which can be used to differentiate documents within a bucket. The choice between these approaches depends on the application's needs and how data reads and writes are handled. Document databases offer flexibility in retrieving data through N1QL queries, but indexing is crucial for performance. Meaningful keys and discriminator fields can also be used to differentiate documents and improve readability and referenceability. Ultimately, modeling data in a document database requires a thoughtful approach, considering factors such as scalability, speed, and application interactions.