iDBQuery vs a dbt semantic layer and metrics: how do they compare?
A dbt semantic layer is where engineers define governed metrics in code so downstream tools compute them consistently, whereas iDBQuery is the question-answering layer business users interact with — it understands schemas, writes SQL, cites figures to source rows, and can work with or without a pre-built metrics layer.
These solve different parts of the stack. A dbt semantic layer (metrics defined as code) is a governance foundation: analytics engineers codify how revenue, churn or margin are calculated so every tool downstream agrees. It doesn't, by itself, let a business user ask a free-form question. iDBQuery is the layer people actually converse with.
- Different jobs. dbt defines and governs metrics; iDBQuery answers natural-language questions and returns cited numbers, charts and reports.
- Works with your definitions. iDBQuery has its own semantic layer to interpret cryptic schemas and understand business terms, so it can answer even where no formal metrics layer exists yet.
- Cross-source. It builds one live model across databases, spreadsheets, PDFs and APIs, rather than assuming everything already flows through a single transformed warehouse.
- Cited answers. Every figure traces to its source row, so a metric can be audited to the data behind it.
- No engineering prerequisite. You don't need a mature dbt project in place to start getting answers.
They're complementary, not competing. If you've invested in dbt metrics, iDBQuery gives your team a conversational, cited way to query the data those definitions describe. If you haven't, iDBQuery still delivers answers today thanks to its own semantic understanding — without waiting on a modelling project.
Updated 2026-06-22