How itsakshatgm/google-cloud-data-modeling works
This repository is a LookML-only semantic layer playground for Looker. It does not contain an application runtime; instead, it shows how model files and view files are structured to control how warehouse data is exposed to business users.
1. Repository role
The project is organized as a set of production-style LookML patterns for enterprise analytics modeling. The “system boundary” is the Looker semantic layer: raw/warehouse data is transformed into views, then assembled into explores in model files.
2. Top-level layout
The repo is split into three example tracks:
- lookml-snippets/ for modularity and inheritance patterns
- usability-optimization/ for governance and analyst-facing UX controls
- caching-and-datagroups/ for model behavior around freshness/caching
3. Core semantic-layer flow
The data flow is: warehouse tables → LookML views (*.view.lkml) → LookML model (*.model.lkml) → Looker explores and user-facing fields. The repository is about shaping that last layer, not about ETL or application logic.
4. Modularity / inheritance track
lookml-snippets/ demonstrates reuse through LookML inheritance (extends) and base definitions. It contains shared patterns centered on users, events, location, and a training_ecommerce.model.lkml model that ties those definitions together.
5. Model/view separation
The view files in lookml-snippets/ and usability-optimization/ represent the field-level contract: dimensions, measures, labels, groups, and hidden fields live there. The model files define which views are exposed, how explores are composed, and how the business-facing navigation is organized.
6. Usability optimization track
usability-optimization/ is focused on making the semantic layer safer and easier to use. It includes users.view.lkml, order_items.view.lkml, and a model file, suggesting a domain where field exposure, naming, and explore composition are tuned for non-technical users.
7. Governance and privacy boundary
This track explicitly uses LookML controls such as hidden fields and field exclusion to restrict sensitive attributes. The important architectural boundary is that privacy is enforced at the semantic layer, not left to downstream dashboard authors.
8. Business-facing organization
The README indicates use of labels and grouping to separate domains such as Inventory and Marketing. That means the model is not just translating schema; it is actively designing the analyst experience by grouping fields and curating which explores appear together.
9. Field abstraction and reuse
The snippets and optimization examples rely on LookML field references and derived logic rather than duplicating SQL. This keeps business rules in one place and lets the same semantic definitions cascade through multiple explores or views.
10. Caching and datagroups track
caching-and-datagroups/ is a separate model example focused on Looker model behavior around cache invalidation and data freshness. Architecturally, this is the layer where query reuse and refresh policy are controlled for performance and consistency.
11. External technology dependency
The only runtime platform implied here is Looker, with the warehouse side referenced in the README as BigQuery. The repository’s artifacts are LookML files, so the key integration point is Looker’s compiler and query generator, not custom application code.
12. How to draw the architecture
A good diagram would show three pattern families feeding into the same Looker semantic layer:
- reusable view definitions and inheritance
- governed/user-friendly model compositions
- cache/freshness policy examples
All of them sit between the warehouse and the end-user exploration experience.
Main entry points: model/training_ecommerce.model.lkml Training model · caching-and-datagroups/training_ecommerce.model.lkml Cache model · users.view.lkml Users view · events.view.lkml Events view
Components 16
Semantic layer
Looker semantic-layer boundary between warehouse data and analyst-facing explores.
- Warehouse · Underlying warehouse tables referenced by LookML views.
- Looker · Compiler and query engine for the semantic layer.
- Semantic layer · The LookML layer that defines views, models, and explores.
- Explores · Business-facing entry points assembled from models and views.
- Analysts · End users who explore curated fields and governed metrics.
LookML snippets
Reusable modeling patterns for inheritance and shared definitions.
- Training model · Snippet-track model that ties reusable view patterns together.
lookml-snippets/model/training_ecommerce.model.lkml - Users view · Reusable user definitions for inheritance and field reuse.
lookml-snippets/users.view.lkml - Events view · Event-centric field definitions used by the snippet track.
lookml-snippets/events.view.lkml - Location view · Location-related fields shared across snippet patterns.
lookml-snippets/location.view.lkml - Extends · Inheritance pattern used to reuse base view definitions.
Usability optimization
Governance and analyst-friendly field curation patterns.
- UX model · Model focused on field exposure and analyst-facing organization.
usability-optimization/model/training_ecommerce.model.lkml - Users view · Governed user fields with hidden and curated attributes.
usability-optimization/users.view.lkml - Order items · Business-facing order item fields and metric definitions.
usability-optimization/order_items.view.lkml - Field controls · Hidden fields, labels, and groups used to constrain analyst access.
Caching and datagroups
Freshness and cache behavior patterns in Looker models.
- Cache model · Model that demonstrates datagroup and freshness behavior.
caching-and-datagroups/training_ecommerce.model.lkml - Datagroups · Cache invalidation and refresh policy for model queries.
Connections 21
- Warehouse → Users view: source data · Warehouse tables are exposed through LookML views.
- Warehouse → Events view: source data · Warehouse tables are exposed through LookML views.
- Warehouse → Location view: source data · Warehouse tables are exposed through LookML views.
- Warehouse → Users view: source data · Warehouse tables are exposed through LookML views.
- Warehouse → Order items: source data · Warehouse tables are exposed through LookML views.
- Users view → Extends: inherits · Snippet views demonstrate LookML inheritance and reuse.
- Extends → Training model: assembled by · Reusable definitions are tied together in the snippet model.
- Events view → Training model: exposed in · The snippet model assembles shared views into explores.
- Location view → Training model: exposed in · The snippet model assembles shared views into explores.
- Users view → Field controls: governed by · Hidden fields and curated labels restrict and shape access.
- Order items → Field controls: governed by · Field exposure and grouping are tuned for usability.
- Field controls → UX model: composed into · Governance rules are applied through the usability model.
- Training model → Semantic layer: part of · Model files are part of the semantic layer boundary.
- UX model → Semantic layer: part of · Model files are part of the semantic layer boundary.
- Cache model → Datagroups: uses · The caching track defines freshness and invalidation policy.
- Datagroups → Looker: configured in · Datagroup behavior is evaluated by Looker.
- Semantic layer → Explores: publishes · Models and views become user-facing explores and fields.
- Explores → Analysts: consumed by · Analysts interact with curated explores and fields.
- Cache model → Semantic layer: part of · Caching behavior is modeled within the semantic layer.
- Looker → Semantic layer: hosts · Looker compiles and serves the LookML semantic layer.
- Semantic layer → Warehouse: queries · The semantic layer queries warehouse data through Looker.