Holistics (modeled SQL BI)
Holistics is a business-intelligence platform built around a reusable data-modeling layer, where datasets, relationships, and metrics are defined (including as code) so business users can explore consistent definitions. This page describes its data model and privacy posture even-handedly, without ranking it against other BI tools.
What this means
Holistics centers on a data-modeling layer where datasets, relationships, and metrics are defined and reused, including a code-based modeling option, so reports draw on shared definitions instead of repeated ad-hoc queries.
Business users then explore those modeled datasets, and the platform compiles their choices into warehouse SQL.
Data model and posture
The model is a semantic layer: dimensions, measures, and relationships mapped to warehouse tables, queried live when users explore. Consistency comes from the shared definitions rather than per-report SQL.
Because queries hit the connected warehouse, access is governed there, and modeled permissions can further scope datasets. Privacy posture depends on warehouse grants and how the model is exposed.
- Reusable, code-capable modeling layer
- Datasets and metrics defined once
- Exploration compiled to warehouse SQL
- Warehouse grants govern data access
How it appears in analytics and logs
Holistics in a stack means a modeling layer maps business datasets and metrics to SQL, so exploration queries the warehouse through shared definitions rather than ad-hoc SQL.
Diagnostic use case
Use Holistics when you want a governed semantic layer over SQL so analysts define datasets and metrics once and business users explore them consistently.
What WebmasterID can help detect
WebmasterID event data in a warehouse can be modeled and explored in Holistics; the BI layer is downstream of WebmasterID's collection.
Common mistakes
- Defining metrics per report instead of in the shared model.
- Assuming the tool stores data rather than querying the warehouse.
- Overlooking warehouse grants when sharing datasets.
Privacy and accuracy notes
Queries run against the connected warehouse, so its grants and any row-level rules govern access. This is educational, not legal advice.
Related pages
- 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.
- Looker BI and the LookML model
Looker is a business-intelligence platform from Google Cloud built around a governed semantic modeling layer called LookML. Rather than extracting data, it generates SQL that runs in your connected database. This page describes its modeling approach and privacy posture even-handedly, distinct from the separate Looker Studio reporting tool.
- 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.
- Web analytics
First-party data you can model in BI.
Sources and verification notes
- Holistics — DocumentationVendor docs on the modeling layer and exploration.
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.