Evidence (code-based BI reports)
Evidence is an open-source business-intelligence framework where reports are written as Markdown files containing SQL queries and templated charts, then built into a static data application. It treats BI as version-controlled code. This page describes its data model and privacy posture even-handedly, without ranking it against other BI tools.
What this means
Evidence reports are Markdown documents that embed SQL queries and chart components; the framework runs the queries and renders the prose and charts together into a static data application.
Because reports are files, they live in version control and follow a software workflow — branches, review, and deployment — rather than being edited in a hosted UI.
Data model and posture
The model is file-based: each page declares its own SQL against a connected source, and the build pipeline executes and renders results. There is no separate metrics catalog by default; logic lives in the queries.
Queries connect to data sources with their own credentials, so access governance sits at the source. Privacy posture depends on those connections, what is built into static output, and deployment choices.
- Reports as SQL + Markdown files
- Built into a static data app
- Version-controlled, review-friendly workflow
- Source credentials govern data access
How it appears in analytics and logs
Evidence in a stack means reports are SQL-and-Markdown files compiled to pages, so the analytics output is a version-controlled data app rather than a hosted dashboard tool.
Diagnostic use case
Use Evidence when you want BI reports authored as code — SQL plus Markdown — so dashboards live in a repository, get code review, and deploy like a static site.
What WebmasterID can help detect
WebmasterID event data in a warehouse can be reported with a code-based tool like Evidence; the report layer is downstream of collection and modeling.
Common mistakes
- Baking sensitive query results into public static output.
- Expecting a hosted dashboard editor instead of a code workflow.
- Forgetting that source credentials govern what the build can read.
Privacy and accuracy notes
Queries run against connected data sources at build or query time, so source credentials and grants govern access. This is educational, not legal advice.
Related pages
- Lightdash (BI on dbt metrics)
Lightdash is an open-source business-intelligence tool that turns dbt models and their metric definitions into explorable dashboards, so metrics live in version-controlled code rather than the BI tool. This page describes its data model and privacy posture even-handedly, without ranking it against other BI tools.
- Observable (data visualization notebooks)
Observable is a reactive notebook environment for data exploration and visualization, centered on JavaScript and the D3/Plot ecosystem. Cells re-run automatically when their inputs change, which suits interactive, visual analysis. It is oriented to building and sharing visualizations and dashboards from data you load or fetch, rather than collecting analytics itself.
- Metabase open-source BI
Metabase is an open-source business-intelligence tool that connects to databases and warehouses, letting users build questions, dashboards, and charts without necessarily writing SQL. It is self-hostable, with a managed cloud option. This page describes its data model and privacy posture even-handedly, without ranking it against other BI tools.
- WebmasterID docs
Event data you can query in code-based reports.
Sources and verification notes
- Evidence — DocumentationOpen-source docs on SQL+Markdown reports and builds.
Last reviewed 2026-06-24. Facts are checked against primary/official sources where available; uncertain specifics are marked “Data not yet verified” rather than guessed.