In the exploration of data modeling with Couchbase's N1QL, key considerations include representing document types and modeling relationships between documents. While existing JSON data can be queried without changes, optimizing the data model for N1QL can enhance query efficiency. Document types play a crucial role by moving responsibilities of identification and retrieval into the document itself, allowing for more efficient indexing and querying. Unlike SQL's use of "FROM" to narrow query scope, N1QL uses document types within a bucket to distinguish data. Relationships between documents, previously managed by embedding, can now be handled using JOINs in N1QL, facilitating one-to-one, one-to-many, and many-to-many relationships. Overall, while N1QL introduces some new practices, it allows developers to maintain much of their existing data structures, with the potential for improved document shapes emerging through further exploration and community sharing.