Looker Analytics & BI on GCP | Google Cloud | Symhas
GCP · Looker · LookML · BigQuery · Semantic Layer · Embedded BI · Self-Service
Your BigQuery Data Platform Needs a Semantic Layer.
Looker Is It — When It Is Built Correctly.
Looker on BigQuery without a well-designed LookML semantic layer is just another SQL query tool — developers writing SQL, analysts copying dashboards, and executives seeing different numbers from different reports. Looker with a governed LookML model is something different: a single definition of every metric, enforced across every report every user runs.Symhas designs and deploys Looker — LookML semantic layer with governed metric definitions, Explores built for each business domain, row-level access controls enforcing data permissions, and Looker Studio or embedded Looker for dashboard delivery — so every user always sees the same numbers from the same source of truth.
LookMLGoverned semantic layer — every metric defined once in LookML, consistent across all reports and users
BigQueryLooker on BigQuery BI Engine — sub-second dashboard refresh without pre-aggregation or data extracts
Self-serviceGoverned self-service analytics — business users exploring data within LookML-defined guardrails
6wkLooker semantic layer, Explores, and dashboard migration — production-ready, fixed price
GCP certified architects available nowActive
LookMLGoverned semantic layer — every business metric defined once, consistent across all users and tools
BigQueryDirect BigQuery connection — no data extracts, no ETL into Looker, always fresh from source
Self-serviceBusiness users exploring data within governed LookML boundaries — no SQL required
6wkLooker semantic layer, Explores, access controls, and dashboard migration — fixed price
What We Deliver
Core Capabilities.
Production-Grade on GCP.

Every capability designed, deployed, and documented by Symhas GCP-certified data and AI architects. Fixed price. Production-ready.

LookML Semantic Layer DesignLookML · Views · Explores · Dimensions · Measures · Derived tables
LookML semantic layer built domain by domain — Views mapping to BigQuery tables or dbt models, Explores defining the join paths analysts use, Dimensions and Measures with consistent business definitions, and derived tables pre-computing complex calculations in BigQuery rather than in the Looker application layer.
View design — one View per BigQuery or dbt model, dimension and measure definitions aligned with business glossary
Explore design — join paths configured per business domain (Finance, HCM, SCM, Sales)
Measure governance — all key metrics (revenue, headcount, inventory position) defined once in LookML
Derived tables — PDTs or native-derived tables pre-computing complex joins and aggregations in BigQuery
LookML testing — Looker Content Validator and Spectacles automated tests on every LookML change
Every metric defined once — no divergent numbers across dashboards, no analyst redefining revenue in a custom field
Row-Level Security & Access ControlsUser attributes · Row-level filters · Group-based access · Content access · Folder permissions
Looker row-level security using User Attributes — data returned from BigQuery filtered by the authenticated user identity, department, or region, so users see only the data they are authorised to access without maintaining separate dataset copies per audience.
User Attributes — Looker user attributes assigned via SAML or OIDC from the identity provider
Row-level filters — WHERE clause filters applied to Explores based on user attribute values
Department filtering — Finance users see Finance data only, Sales sees Sales only, in the same Explore
Content access — dashboard and Look access controlled by Group and Folder permissions
Embed access — embedded Looker dashboards filtered by user context from the embedding application
One BigQuery dataset, one LookML model — filtered to each user automatically, no per-audience data duplication
Dashboard Design & MigrationLooker dashboards · Looker Studio · Migration · Alerts · Scheduling
Dashboard migration from existing tools — Power BI, Tableau, Google Data Studio — to Looker, with every dashboard rebuilt from the LookML semantic layer rather than raw SQL, scheduled delivery for operational reports, and Looker Alerts sending notifications when metrics breach configured thresholds.
Dashboard inventory and migration prioritisation — by usage frequency and business criticality
Dashboard rebuild in Looker — all existing reports rebuilt from LookML Explores, not raw BigQuery SQL
Scheduled delivery — dashboard PDF and CSV delivery to stakeholders on business cadence
Looker Alerts — threshold-based notifications delivered to email or Slack when metrics breach limits
Looker Studio integration — public-facing and lightweight dashboards in Looker Studio on BigQuery
Every existing report rebuilt on the governed semantic layer — consistent numbers, consistent definitions, consistent access
Embedded Analytics & Looker APISigned embed · SSO · iFrame · Looker API · Custom applications
Looker embedded analytics integrating Looker dashboards into existing enterprise portals, customer-facing applications, and operational systems — signed embed URLs providing authenticated, personalised access to Looker content without users needing a Looker licence.
Signed embed URL generation — authenticated access to Looker dashboards from external applications
SSO embed — user identity from the embedding application passed to Looker for row-level filtering
iFrame integration — Looker dashboards embedded in Salesforce, ServiceNow, or internal portals
Looker API — programmatic content management, user provisioning, and query execution
White-label styling — Looker dashboard styling matched to embedding application brand and theme
Looker analytics surfaced inside existing applications — no separate BI tool login, context-aware data for every user
Delivery Model
Assessment to Production.
Fixed Price. Fixed Timeline.

Four phases with go/no-go gates. Scope and price agreed before week one.

01
Semantic Layer Design & PrioritisationWeeks 1–2

BigQuery dataset and dbt model inventory. Business metric audit — all KPIs identified and canonical definitions agreed with stakeholders. LookML model structure designed. Explore scope per domain agreed. Dashboard migration backlog prioritised by usage.

02
LookML Build & GovernanceWeeks 3–4

LookML Views and Explores built for Finance, HCM, SCM, and Sales domains. Metric definitions reviewed and approved by business owners. Row-level security User Attributes configured. Derived tables for complex measures deployed. LookML tests written and CI/CD pipeline configured.

03
Dashboard Migration & User TestingWeeks 5–5

Priority dashboards rebuilt from LookML Explores. Row-level access tested with representative users from each department. Scheduled deliveries and Alerts configured. Looker Studio dashboards for lightweight use cases deployed. User acceptance testing completed.

04
Go-Live & Analytics Team CertificationWeek 6

Looker production environment live. All migrated dashboards validated. Analytics team certified on LookML development and Explore administration. Self-service training delivered. Symhas moves to advisory.

Retail · Looker BI Platform500+ Location Retailer.
One Definition of Revenue. 500 Locations in Looker. Self-Service Analytics Delivered.

The national retailer had finance, supply chain, and commercial teams running separate BI tools with different definitions of core metrics — Revenue was calculated differently in Tableau (commercial), in Excel (finance), and in the data team Python notebook. Quarterly reconciliation took 3 days.

Symhas built a Looker semantic layer on BigQuery — LookML Explores for Finance, SCM, and Commercial domains with one canonical definition of Revenue, Gross Margin, and Inventory Position, row-level security filtering each team to their region and category, and 47 dashboards migrated from Tableau and Google Data Studio.

1Definition of every metric
47Dashboards migrated to Looker
3days→0Quarterly reconciliation eliminated
6wkSemantic layer to go-live
Discuss Your Programme
What was delivered

Looker Semantic Layer — Retail Production Deployment

LookML — 12 Views, 4 Explores (Finance, SCM, Commercial, Ops), 340 dimensions and measures defined
Metric governance — Revenue, Gross Margin, Inventory Position each defined once, approved by CFO and COO
Row-level security — 6 region User Attributes, 8 category User Attributes, filtering applied in all Explores
47 dashboards migrated from Tableau and Google Data Studio, all rebuilt from LookML Explores
Scheduled delivery — 12 operational dashboards delivered as PDF to regional managers every Monday 07:00
Looker Alerts — 24 metric alerts configured: OOS rate, margin threshold, and inventory days on hand

“We had three different definitions of revenue across three teams. Every board meeting started with 20 minutes of reconciliation. After Looker, there is one number. The conversation is now about the business, not about whose spreadsheet is right.”

— CFO, National Retailer

GCP Services Deployed
The Specific GCP Services
We Configure for This Capability.
GCP
Looker (Google Cloud)

Enterprise BI platform — LookML semantic layer, Explores, dashboards, scheduling, and alerts.

LookML model architecture
Explore design per domain
Dashboard build and migration
Scheduling and alerts
GCP
LookML Semantic Layer

Governed metric definitions — Views, Explores, Dimensions, Measures, and derived tables.

View and Explore design
Canonical metric definitions
Derived table development
LookML CI/CD testing
GCP
Looker Row-Level Security

User Attribute-based data access — department, region, and category filtering in all Explores.

User Attribute configuration
Row-level filter implementation
Group and Folder permissions
Embed SSO filtering
GCP
Looker Embedded Analytics

Signed embed and API — Looker dashboards in enterprise portals and applications.

Signed embed URL generation
SSO embed configuration
iFrame integration
Looker API automation
GCP
Looker Studio

Lightweight public and operational dashboards — Looker Studio on BigQuery for non-Looker users.

Looker Studio connector to BigQuery
Dashboard design
Sharing and access controls
Scheduled refresh configuration
GCP
BigQuery BI Engine

Sub-second dashboard performance — BI Engine reservations caching Looker queries on BigQuery.

BI Engine reservation sizing
Table-level BI Engine acceleration
Query performance validation
Cost vs performance optimisation
Why Symhas
GCP Expertise Built from Production Deployments.
Metric Definitions Agreed Before LookML Is WrittenLookML built before business stakeholders have agreed on metric definitions is rebuilt after go-live. Symhas runs a metric governance workshop before writing a line of LookML — canonical definitions approved by finance, commercial, and operations owners before the semantic layer is built.
Row-Level Security Tested With Real Users Before Go-LiveRow-level security that is not validated with actual user accounts produces either data leakage or access failures at launch. Symhas tests row-level access with representative accounts from every user group before go-live.
LookML CI/CD From the First CommitLookML without automated testing accumulates broken content as the model grows. Symhas configures Spectacles or Looker Content Validator in CI/CD — every LookML change tested before it reaches production.
Dashboards Rebuilt From LookML, Not Ported as SQLMigrating existing dashboards as raw SQL queries embedded in Looker bypasses the semantic layer entirely. Every dashboard Symhas builds uses LookML Explores exclusively — no raw SQL in production dashboards.
BI Engine Reservations Sized Before Dashboard Go-LiveLooker dashboards on BigQuery without BI Engine reservations degrade under concurrent user load. Symhas profiles dashboard query patterns and sizes BI Engine reservations before the first production user session.
Analytics Team Owns LookML Before HandoverBy handover your analytics team adds new LookML dimensions, builds new Explores, and manages dashboard access independently. Certified on LookML development workflow before Symhas steps back.
Next Step
Tell Us How Many Definitions of Revenue You Currently Have.
We Will Build the Looker Semantic Layer That Makes It One.
A 30-minute Looker assessment with a Symhas BI architect. We will review your BigQuery datasets, existing dashboards, and metric governance gaps — and design the LookML semantic layer before the engagement price is agreed.No commitment. No pitch deck. An honest conversation about your data and AI ambitions.