How does iDBQuery query nested JSON or MongoDB documents?
iDBQuery understands nested and semi-structured data — MongoDB documents, JSON columns and files — and can reach into embedded fields and arrays to answer questions, then join that data with your relational sources in the same model. You ask in plain language and it handles the path into the nested structure.
Not all data is neat rows and columns. Document databases and JSON payloads nest fields inside fields and hold arrays of sub-records, which trips up tools built only for flat tables. iDBQuery is designed to work with both.
- Reads nested structure. It can navigate into embedded objects and arrays — an `address.city` inside a customer document, or line items nested in an order — to pull out the values your question needs.
- Native document sources. MongoDB connects directly, and JSON files and JSON columns in relational databases are handled too, so semi-structured data isn't a second-class citizen.
- Blends with relational data. Because everything folds into one live queryable model, you can join a MongoDB collection to a SQL table and answer across both in a single question.
This matters for modern application data, where event logs, product catalogues, and user profiles often live as documents. As with structured sources, iDBQuery shows how it reached the answer and cites the underlying records, so even data pulled from deep inside a nested document is traceable back to its origin. You get the flexibility of document data without having to flatten it yourself first.
Updated 2026-06-22