Sigma Computing (warehouse BI)
Sigma Computing is a cloud business-intelligence tool that presents a familiar spreadsheet-like grid while compiling actions into SQL that runs live in the connected cloud warehouse. It avoids data extracts by pushing computation down to the warehouse. This page describes its data model and privacy posture even-handedly, without ranking it against other BI tools.
What this means
Sigma offers a spreadsheet-style grid where filters, formulas, and pivots are translated into SQL that runs in the connected cloud warehouse. Users work in a familiar interface while computation happens in the warehouse.
Because it queries live rather than importing data, dashboards reflect current warehouse state and there is no separate extract to refresh or secure.
Data model and posture
The model is warehouse-native: Sigma reads tables and views and compiles user actions to SQL, so the source of truth and the compute stay in the warehouse.
Since queries run with warehouse credentials, access is governed by warehouse grants and row-level security rather than by copies of data. Privacy posture is shaped by warehouse permissions and how datasets are modeled, not by a local extract.
- Spreadsheet-style grid compiled to SQL
- Live queries; no data extract
- Warehouse stays the source of truth
- Warehouse grants govern access
How it appears in analytics and logs
Sigma in a stack means BI interactions compile to warehouse SQL executed live, so data stays in the warehouse and freshness reflects the warehouse rather than a separate extract.
Diagnostic use case
Use Sigma when analysts want spreadsheet-style exploration directly over warehouse data, so live queries replace extracts and the warehouse stays the single source of truth.
What WebmasterID can help detect
WebmasterID event data exported to a warehouse can be explored in Sigma; the spreadsheet layer is downstream of the collection WebmasterID performs.
Common mistakes
- Assuming queries are cached when they run live and cost warehouse compute.
- Relying on Sigma permissions instead of warehouse row-level security.
- Treating it as a collector rather than a warehouse query layer.
Privacy and accuracy notes
Because Sigma queries the warehouse live, warehouse grants and row-level security govern who sees what. This is educational, not legal advice.
Related pages
- ThoughtSpot (search-driven BI)
ThoughtSpot is a business-intelligence platform whose primary interaction is search: users type or speak a question and it generates a query against a governed semantic model, returning charts and tables. It connects live to cloud warehouses or its in-memory engine. This page describes its data model and privacy posture even-handedly, without ranking it against other BI tools.
- 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.
- Warehouse-native analytics
Warehouse-native analytics is an approach where the data warehouse (BigQuery, Snowflake, Redshift, Databricks) is the source of truth, and analytics tools query that data in place rather than copying it into a separate vendor store. You own the schema and computation; tools sit on top. It trades plug-and-play convenience for control, joinability, and avoiding data duplication.
- Web analytics
First-party data you can model in warehouse BI.
Sources and verification notes
- Sigma Computing — DocumentationVendor docs on live warehouse querying and the grid model.
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.