Unwieldy aggregation pipelines for complex analytical queries
7/10 HighMongoDB's aggregation framework becomes brittle and unmaintainable for complex analytical queries. Pipelines require hundreds of lines of transformations that break easily when document structure changes. Teams often export data to SQL databases or data warehouses to handle reporting that would be simple SQL joins, adding operational overhead.
Sources
Collection History
Business apps rarely stop at 'get by id.' They grow into multi-filter lists, admin dashboards, reports, exports, and 'can we sort by X then Y?' asks. With DynamoDB: You sort only within a partition. Filters happen after item selection. Cross-attribute predicates need GSIs, denormalized views, or both.
One enterprise deployment attempted to use MongoDB for a financial reporting system requiring complex calculations across multiple collections. The aggregation pipelines became so complex they were unmaintainable, query performance was 10x slower than equivalent SQL, and the team spent 3 months rewriting everything in PostgreSQL.