iDBQuery vs Amazon QuickSight Q: what's the difference?
Amazon QuickSight Q adds natural-language Q&A to AWS QuickSight and is a solid fit if you are already on AWS and set up its topics; iDBQuery is not tied to AWS, unifying many sources into one live model, citing every figure to its row, and deploying anywhere including fully air-gapped.
QuickSight Q brings natural-language questions to Amazon QuickSight, AWS's cloud BI service. You prepare data (often loaded into its SPICE engine), define topics so Q understands your fields, and users can then ask questions in words. For teams standardised on AWS, it is a convenient, native way to add NL analytics.
iDBQuery is cloud-agnostic and source-agnostic:
- Not tied to one cloud. iDBQuery connects to your databases, warehouses, Excel, CSV, Sheets, PDFs, folders (with OCR) and REST APIs wherever they live, and unifies them into one live model.
- Cited to the row. Every figure links back to the exact source rows, so answers are auditable.
- Query in place. There is no requirement to load data into a proprietary in-memory store first; iDBQuery runs against your sources directly.
- Deploy anywhere. Cloud, your own VPC, a Desktop app, or fully air-gapped, not confined to one provider's environment.
QuickSight Q is a reasonable choice when your data and stack already live in AWS and you are happy to model topics for it. iDBQuery fits when your data is spread across clouds and file types, when you need every number cited to its source, or when deployment has to stay inside your own walls.
Updated 2026-06-22