iDBQuery vs Count: what's the difference?
iDBQuery and Count both explore data collaboratively, but differently: Count is a data notebook and canvas where analysts combine visual SQL and charts on a shared whiteboard, while iDBQuery lets anyone ask plain-language questions and get cited answers across all sources without building a canvas or writing SQL.
Count is a genuinely nice tool for collaborative analysis. Its infinite canvas blends SQL cells, charts and notes so data teams can think through a problem visually and share the reasoning, almost like a whiteboard for data. For analyst-led, exploratory work where showing the path to an answer matters, Count is excellent.
It is, however, still an analyst's environment: someone writes the SQL and lays out the canvas.
iDBQuery is aimed at the person who just needs the answer:
- Plain language. Ask a question in words; iDBQuery writes and runs the SQL and returns a number, chart or dashboard.
- Cited answers. Each figure links to the exact source rows, so it can be verified, not just read.
- One live model across sources. It queries databases, warehouses, spreadsheets, APIs and documents together, no pipeline needed.
- Analyst agent. For open questions, it investigates autonomously and explains its reasoning with sources.
- Deploy anywhere. Cloud, VPC, air-gapped, or Desktop.
The two are complementary: Count for analysts collaborating on deep, exploratory problems, and iDBQuery for everyone else who wants a fast, trustworthy, cited answer without opening a canvas or writing a query.
Updated 2026-06-22