The text outlines a comparison between Couchbase and MongoDB in terms of their capabilities for handling analytics workloads, emphasizing how each system has evolved to address complex data processing needs. Couchbase, originally a NoSQL system designed for extreme scalability, has developed distinct engines for query and analytics, both utilizing the N1QL language. These engines cater to operational and analytical workloads, respectively, following a multi-dimensional architecture that includes massively parallel processing (MPP) strategies to manage large datasets. In contrast, MongoDB has introduced analytic nodes within its clusters that use its MongoDB Query Language (MQL) and are designed to manage both operational and analytical queries using the same query processing methods. Key differences highlighted include their approaches to indexing, query optimization, parallel processing, and support for windowed aggregate functions, with Couchbase offering more advanced features for analytics. MongoDB supports external data processing via integration with additional data sources and S3, whereas Couchbase's capabilities include analyzing data from multiple clusters and supporting external JSON, CSV, and TSV data in S3. Both systems support integration with business intelligence tools like Tableau and Knowi, but differ in their data visualization features and subquery handling.