Duplicate data poses a common challenge in data management, whether from combining data sources, multiple purchases by a single customer, or repeated data entries. The blog post explores using Couchbase Server 2.0 to identify and retain non-duplicate documents by employing views and a combination of Ruby client and Faker gem for generating sample data. A map function is used to define duplicates based on matching first name, last name, and postal code fields, while a reduce function groups duplicate document IDs. Despite encountering a "Reduction too large" error, the blog suggests that employing N1QL in Couchbase 4.0 and later versions could offer a faster solution, although it notes that Couchbase operates as a distributed system where the performance of N1QL and client-side operations are comparable.