Semantic Layer for Looker: Extending LookML Beyond Looker

A semantic layer for Looker extends the governance and consistency that LookML provides within Looker to the rest of your analytics ecosystem. While Looker pioneered the semantic layer concept with LookML, organizations using multiple analytics tools need metric definitions that work everywhere - not just in Looker Explores and Dashboards.

Looker's LookML is arguably the most sophisticated BI-native semantic modeling language. The question is not whether LookML is good enough, but whether your organization needs metric consistency beyond Looker's boundaries.

LookML: Looker's Native Semantic Layer

What LookML Provides

LookML offers robust semantic modeling:

• Views: Define tables with dimensions and measures
• Explores: Create curated interfaces for business users
• Derived Tables: Build complex transformations
• Access Filters: Implement row-level security
• Modeling Patterns: Reusable templates and extensions

This is comprehensive for Looker users.

Where LookML Stops

LookML definitions exist within Looker:

• SQL users cannot import LookML measures
• Other BI tools cannot reference LookML definitions
• AI systems cannot directly leverage LookML semantics
• Applications embedding analytics need separate implementations

The semantic knowledge captured in LookML remains siloed.

When External Semantic Layers Make Sense

Multi-Tool Organizations

If your company uses Looker alongside other tools, you face a choice:

1. Maintain separate definitions in each tool
2. Designate one tool's definitions as authoritative and manually sync
3. Implement a tool-agnostic semantic layer

Option 3 provides consistency without manual synchronization burden.

AI and LLM Integration

AI systems querying data benefit from semantic layers:

• LookML is not designed for LLM consumption
• External semantic layers often have AI-friendly APIs
• Metric definitions need explicit documentation AI can use

If AI-powered analytics is a priority, evaluate semantic layers designed for AI integration.

Embedded Analytics Outside Looker

When embedding analytics in products:

• Looker's embedding works well for Looker visualizations
• Non-Looker components need alternative metric access
• A universal semantic layer serves both embedded and internal use cases

SQL Power Users

Data analysts who prefer SQL:

• Looker's SQL Runner uses LookML context
• Direct warehouse queries do not benefit from LookML
• An external semantic layer can expose metrics via SQL interfaces

Integration Patterns

Pattern 1: External Layer as Source of Truth

The semantic layer is authoritative; LookML references it:

Data Warehouse → Semantic Layer → LookML Views → Looker Explores

Implementation:

• Semantic layer materializes governed tables
• LookML views reference these tables
• Complex logic lives in semantic layer
• LookML handles Looker-specific presentation

Pattern 2: LookML as Source of Truth with Export

LookML remains authoritative; definitions are exported:

LookML → Export/Sync → External Semantic Layer → Other Tools

Implementation:

• LookML contains canonical definitions
• Automated processes export to external layer
• Other tools consume the external layer
• Changes flow from LookML outward

Pattern 3: Federated Definitions

Different layers own different metric types:

• Semantic layer owns cross-tool metrics
• LookML owns Looker-specific metrics
• Clear governance for which metrics belong where

This requires careful coordination but allows specialization.

Implementation Considerations

Evaluating the Need

Before adding complexity, assess:

1. Tool inventory: What tools besides Looker need governed metrics?
2. User base: Who queries data outside Looker?
3. AI plans: Will AI systems need metric access?
4. Consistency problems: Are there current metric inconsistencies?

If Looker is truly your only consumption layer, LookML may suffice.

Avoiding Definition Duplication

The worst outcome is maintaining parallel definitions:

Bad:

Semantic Layer: Revenue = SUM(amount) WHERE status = 'complete'
LookML: measure: revenue { sql: SUM(${amount}) ;; filters: [status: "complete"] }

These will drift and cause confusion.

Better - Option A (External layer authoritative):

Semantic Layer: Revenue = ... (full definition)
LookML: measure: revenue { sql: ${semantic_layer_revenue_table.revenue} ;; }

Better - Option B (LookML authoritative):

LookML: measure: revenue { ... (full definition) }
External Layer: synced from LookML automatically

Handling LookML-Specific Features

Some LookML features do not have external equivalents:

• Explores: Curated navigation exists only in Looker
• Drill fields: Looker-specific interactivity
• Liquid templating: Dynamic LookML generation
• Datagroups: Looker caching management

These can remain in LookML even with an external semantic layer handling core metrics.

Migration Approach

Phase 1: Inventory and Assessment

Document current state:

• Catalog all LookML views and measures
• Identify which metrics are cross-tool relevant
• Map current inconsistencies with other tools
• Determine governance gaps

Phase 2: Architecture Decision

Choose your integration pattern:

• External layer as source of truth
• LookML as source with export
• Federated ownership

Document the decision and rationale.

Phase 3: Core Metric Migration

Start with high-value cross-tool metrics:

1. Define in semantic layer with full documentation
2. Update LookML to reference semantic layer
3. Validate Looker results match expectations
4. Connect other tools to semantic layer
5. Verify consistency across tools

Phase 4: Governance Establishment

Create ongoing processes:

• Metric change workflow
• Synchronization monitoring
• Cross-tool validation
• Documentation maintenance

Looker's Evolving Semantic Strategy

Google is expanding Looker's semantic capabilities:

Looker Semantic Layer API

Enables external access to LookML definitions:

• Other tools can query Looker's semantic model
• Reduces the need for external layers in some scenarios
• Evaluate current capabilities against your needs

BigQuery Integration

Looker and BigQuery integration deepens:

• Shared semantic definitions between platforms
• Potential simplification for Google Cloud users
• Consider roadmap when planning architecture

Universal Semantic Layer Initiative

Google's vision for cross-tool semantic consistency:

• May reduce need for third-party semantic layers
• Evaluate feature availability and maturity
• Consider hybrid approaches during transition

Best Practices

For Organizations Keeping LookML Primary

• Invest in LookML quality and documentation
• Explore Looker's semantic API for external access
• Automate exports to other systems
• Monitor for definition drift

For Organizations Adding External Semantic Layers

• Minimize LookML logic duplication
• Reference semantic layer from LookML where possible
• Maintain clear ownership boundaries
• Document why definitions live where they do

For All Organizations

• Avoid manual synchronization between systems
• Automate validation that definitions match
• Create clear governance accountability
• Plan for evolution as tools and capabilities change

The goal is consistent metrics everywhere, whether that is achieved through LookML extensions, external semantic layers, or a thoughtful combination. Choose the architecture that fits your tool ecosystem and organizational needs.