Hex (collaborative data notebooks)
Hex is a collaborative data workspace built around notebooks that combine SQL, Python, and no-code cells, with the ability to publish results as interactive data apps. It targets analysts and data scientists working over warehouse data, blending exploratory analysis with shareable outputs. It reads from connected sources rather than collecting data itself.
What this means
Hex notebooks chain cells that can be SQL queries, Python code, or visualizations, with results flowing between them. Notebooks can be published as interactive 'data apps' with inputs and controls, so a single analysis becomes a shareable tool.
Like other notebook platforms, the data lives in connected warehouses or databases; Hex is the analysis, collaboration, and publishing layer.
What to weigh
Hex suits analysts and data scientists who want code plus collaboration and the option to ship interactive apps. For non-technical, point-and-click dashboards a traditional BI tool may fit better; the two address different audiences.
- Notebooks mixing SQL, Python, and no-code cells
- Publish analyses as interactive data apps
- Reads connected sources; does not collect data
Where it fits
It fits the exploration-and-app layer of a warehouse stack. Consistent upstream modeling keeps notebook results aligned with other reporting; the notebook code itself governs correctness.
How it appears in analytics and logs
Hex outputs reflect the SQL and Python in the notebook against connected sources; wrong results trace to the code or model, not collection.
Diagnostic use case
Use Hex for collaborative, code-and-SQL exploration over warehouse data when you also want to publish interactive apps from the same notebook.
What WebmasterID can help detect
WebmasterID is a first-party measurement tool; this page explains Hex's notebook model so you can see how exported analytics data is explored and turned into apps.
Common mistakes
- Expecting Hex to collect data rather than query sources.
- Using notebooks where non-technical dashboards are needed.
- Letting notebook logic diverge from shared definitions.
Privacy and accuracy notes
Hex queries data from sources you connect; exposure depends on those sources and access controls. This is factual, not legal advice.
Related pages
- Mode (SQL and notebooks for BI)
Mode is a collaborative analytics and BI platform that lets analysts query a warehouse with SQL, then explore and visualize results in notebooks (Python/R) and shareable reports. It targets analyst-driven, code-friendly analysis on top of warehouse data, sitting between raw SQL and self-serve dashboards. It reads from connected data sources; it does not collect data itself.
- 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.
- MotherDuck and DuckDB analytics
DuckDB is an open-source, in-process analytical (OLAP) database — it runs inside your application or notebook with no server, executing fast columnar SQL over local files or data frames. MotherDuck is a cloud service built on DuckDB that adds hosted storage and hybrid local-plus-cloud query execution. Together they target analytical SQL that runs close to where you work.
- Web analytics
First-party web measurement overview.
Sources and verification notes
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.