Looker and LookML
Looker is a business intelligence platform that queries data where it already lives. LookML is its modeling language: developers define reusable dimensions, measures, joins, and access rules, then analysts explore that governed model without writing each SQL query from scratch.
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Intro
Looker and LookML
Looker is a business intelligence platform built around a semantic model. LookML, short for Looker Modeling Language, describes that model as version-controlled text. A developer defines database relationships and business calculations once. Analysts then select fields in an Explore, and Looker generates SQL for the connected database.
This design separates three concerns. The database stores and computes over the data. LookML defines how people may query it. Looker supplies the development environment, query interface, dashboards, delivery, and administration. Looker normally leaves source data in the database instead of importing it into a separate analytical store.
From a question to a result
The query path begins in an Explore. An Explore is a curated starting point built from one base view and any joined views. The field picker exposes dimensions, measures, filters, and pivots allowed by the model.
When an analyst runs a query, Looker resolves the selected fields through the LookML project. It chooses the needed views and joins, generates SQL in the connection's dialect, and sends that SQL to the database. The database executes the query. Looker then formats the returned rows for a table, visualization, Look, dashboard tile, download, or embedded application.
LookML is therefore not a data transformation engine in the same sense as an upstream pipeline. It can express SQL fragments and derived tables, but the connected database performs the query work. Warehouse performance, concurrency, permissions, and cost remain part of Looker operation.
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Sources
- https://docs.cloud.google.com/looker/docs/what-is-lookml
Supports
- LookML as the language for semantic data models
- LookML projects, model files, view files, Git version control, and the generated-SQL query path
- Separation of query structure from selected query content
- https://docs.cloud.google.com/looker/docs/lookml-terms-and-concepts
Supports
- Definitions and relationships of models, views, Explores, dimensions, and measures
- A model connection targeting one database and an Explore as a query starting point
- https://docs.cloud.google.com/looker/docs/looker-core-qs-model-data
Supports
- Official workflow for creating model and view files, an Explore, a dimension, and a measure
- https://docs.cloud.google.com/looker/docs/working-with-joins
Supports
- Join relationships and their direction from an Explore base to a joined view
- Primary-key uniqueness and non-null requirements for joined measures
- Symmetric aggregates and fanout handling
- https://docs.cloud.google.com/looker/docs/best-practices/understanding-symmetric-aggregates
Supports
- Symmetric aggregation as Looker's mechanism for correct aggregations across fanouts
- https://docs.cloud.google.com/looker/docs/dev-mode-prod-mode
Supports
- Development Mode branch isolation and Production Mode behavior
- Looker IDE integration with Git workflows
- https://docs.cloud.google.com/looker/docs/derived-tables
Supports
- Temporary and persistent derived table behavior
- Development and production PDT build behavior and operational states
- https://docs.cloud.google.com/looker/docs/aggregate_awareness
Supports
- Selection of the smallest eligible aggregate table that can answer a query accurately
- Fallback to detailed data when aggregate grain does not fit
- https://docs.cloud.google.com/looker/docs/access-control-and-permission-management
Supports
- Roles, permission sets, model sets, content access, user attributes, access filters, and access grants
- Distinct developer and SQL Runner database-access considerations
- https://docs.cloud.google.com/looker/docs/content-validation
Supports
- Detection of saved-content references to missing models, Explores, views, and fields
- Scope and risks of content changes during development and deployment
- https://docs.cloud.google.com/looker/docs/ci-sql-validator
Supports
- SQL validation of models and Explores
- Scope, fail-fast, hidden-field, and concurrency options
- https://cloud.google.com/looker
Supports
- Official Looker product homepage and landscape placement
- https://github.com/sindresorhus/awesome
Supports
- Required discovery starting point for curated ecosystem lists
- https://github.com/thenaturalist/awesome-business-intelligence
Supports
- Discovery of Spectacles, dbt, DataHub, Lightdash, and the wider BI product ecosystem
- https://docs.spectacles.dev/
Supports
- Spectacles as continuous integration tooling for Looker and LookML validation
- https://docs.getdbt.com/docs/introduction
Supports
- dbt as a system for warehouse transformations, modular data models, testing, documentation, and version-controlled deployment
- https://docs.datahub.com/docs/generated/ingestion/sources/looker
Supports
- Ingestion of Looker dashboards, charts, Explores, views, ownership, usage, and lineage
- Parsing LookML and connecting modeled assets to warehouse tables
- https://docs.lightdash.com/guides/lightdash-semantic-layer
Supports
- Shared metrics, dimensions, metadata, and relationships in the Lightdash semantic layer
- https://www.microsoft.com/en-us/power-platform/products/power-bi
Supports
- Microsoft Power BI landscape placement
- https://www.tableau.com/
Supports
- Tableau landscape placement
- https://www.qlik.com/us/products/qlik-cloud-analytics
Supports
- Qlik Cloud Analytics landscape placement
- https://www.thoughtspot.com/
Supports
- ThoughtSpot landscape placement
- https://www.sigmacomputing.com/
Supports
- Sigma landscape placement
- https://www.lightdash.com/
Supports
- Lightdash landscape placement and product homepage
- https://superset.apache.org/
Supports
- Apache Superset landscape placement and product homepage