iDBQuery vs Looker: what's the difference?

Looker's strength is governance: a central LookML model that enforces consistent metric definitions. The cost is that an engineer has to build and maintain that model, and exploration is bounded by what's been defined. iDBQuery lowers the barrier to a single question.

• No modeling layer to stand up. Connect MySQL, Postgres, MongoDB, spreadsheets, PDFs or APIs and ask right away.
• Plain language over LookML. No new language to learn or maintain.
• One live model across everything, built automatically — not a single governed warehouse you must feed first.
• Cited answers. Each number traces to its source row, giving auditability without a formal metrics layer.
• A semantic layer when you want consistency. iDBQuery can learn and pin definitions for cryptic schemas, so meaning stays stable — without a full LookML build.

Looker suits organizations that want every metric centrally governed and are willing to invest in modeling. iDBQuery suits teams that need answers across messy, varied sources today. Used together, Looker holds the governed core while iDBQuery handles the long tail of ad-hoc and cross-source questions.