Data teams building a governed semantic layer
Define reusable business entities, joins, measures, dimensions, time logic, security, documentation, exploration, and metrics in LookML.
LookML semantic modelling, Explores, dimensions and measures, dashboards, embedding, Git workflow, permissions, deployment, governance, and operations
Looker development and modelling services
Rokad designs, builds, migrates, governs, and operates Looker across LookML semantic models, Explores, dashboards, embedding, Git delivery, security, and Google Cloud integration.
Platform fit / 01
Designed for teams with a specific platform requirement.
Looker places a governed LookML semantic layer between the warehouse and analytical experiences. Rokad designs models, views, Explores, joins, dimensions, measures, derived tables, aggregate awareness, access filters, dashboards, embedding, Git workflows, environments, deployment, performance, and ownership as software-backed analytics products.
Organisations scaling self-service analytics
Provide curated Explores, fields, dashboards, folders, permissions, schedules, alerts, and user guidance without duplicating SQL logic.
Product teams embedding governed analytics
Integrate Looker content, queries, identity, permissions, row security, APIs, themes, monitoring, and lifecycle into applications.
Implementation risks / 02
The platform problems Rokad is prepared to solve.
LookML models expose warehouse structure without business usability
Views, joins, fields, labels, descriptions, drill paths, defaults, time logic, and curated Explores do not match user questions.
Persistent derived tables create hidden pipeline responsibilities
Triggers, dependencies, rebuilds, indexes, failures, freshness, warehouse cost, and recovery are not operated explicitly.
Git workflow exists but analytical releases remain uncontrolled
Branches, validation, content dependencies, model changes, permissions, schedules, dashboards, and deployment lack evidence and ownership.
Platform capabilities / 03
What Rokad can implement and operate.
01
Looker instance, project, model, view, Explore, content, group, role, usage, migration, performance, and governance assessment
02
LookML projects, models, views, joins, dimensions, dimension groups, measures, parameters, sets, drill fields, and documentation
03
Explores, extensions, refinements, access filters, user attributes, row security, field controls, aggregate awareness, and reusable semantics
04
SQL and native derived tables, persistent derived tables, datagroups, triggers, dependencies, indexing, freshness, and cost controls
05
Dashboards, Looks, folders, boards, schedules, alerts, actions, data delivery, user experience, and adoption workflows
06
Embedded analytics, signed or cookieless patterns where appropriate, APIs, SDKs, identity, themes, extensions, and application integration
07
Git, development and production modes, validation, CI, content validation, deployment, permissions, monitoring, support, and managed operation
Implementation system / 04
The architecture behind a dependable platform delivery.
LookML semantic layer
Projects, models, views, joins, Explores, dimensions, measures, calculations, parameters, security, documentation, and ownership.
Analytical experiences
Explores, dashboards, Looks, drills, folders, schedules, alerts, deliveries, embedding, themes, extensions, and user guidance.
Development and release system
Git, branches, validation, testing, content dependencies, environments, deployment, model versions, review, and rollback.
Looker operations
Queries, PDTs, schedules, failures, permissions, usage, performance, warehouse cost, incidents, support, and lifecycle.
Use cases / 05
Where this platform creates practical leverage.
Looker semantic-model implementation
Build governed LookML models and Explores for shared business entities, measures, dimensions, security, documentation, and self-service.
Looker project modernisation
Refactor duplicated views and measures, improve joins and Explores, stabilise PDTs, add validation, govern releases, and optimise queries.
Embedded Looker analytics
Integrate authenticated analytics into customer, partner, or employee products with row security, APIs, themes, and telemetry.
Looker migration
Move warehouse connections, LookML, content, users, groups, roles, schedules, permissions, embedding, and operational workflows safely.
Architecture / 06
Platform-specific engineering decisions and boundaries.
Explores are curated analytical products
Design grain, joins, fields, defaults, labels, descriptions, drills, performance, security, and ownership around user decisions.
LookML releases follow software-delivery discipline
Review branches, validate models and content, test representative queries and security, document changes, deploy, and monitor impact.
PDTs are operated as data pipelines
Define triggers, freshness, dependencies, indexes, rebuild, failure, backfill, warehouse usage, monitoring, and consumer impact.
Quality and governance / 07
Production controls are part of the implementation.
One governed metric definition
Business entities, dimensions, measures, time logic, filters, currency, ownership, and semantic contracts are defined once and tested.
Versioned analytical delivery
Models, reports, dashboards, permissions, data sources, environments, tests, deployment, and rollback follow controlled lifecycle practices.
Usable and trustworthy analysis
Freshness, performance, accessibility, row-level security, lineage, documentation, adoption, and decision workflows are measured.
Delivery / 08
A controlled path from assessment to operation.
Assess
Clarify the business outcome, current systems, platform constraints, data, integrations, risks, ownership, and measurable acceptance criteria.
Design
Define the platform architecture, workflow or storefront model, extensions, integrations, security, environments, and migration sequence.
Implement and validate
Build in controlled increments with testing, stakeholder review, observability, documentation, and platform-specific quality controls.
Launch and operate
Deploy safely, transfer ownership, monitor production behaviour, support users, and improve the implementation using operational evidence.
Typical platform deliverables
Looker project, LookML, Explore, content, role, permission, PDT, performance, usage, and risk assessment
Semantic, Explore, security, content, embedding, Git, deployment, and operating architecture
Production LookML projects, models, views, Explores, dimensions, measures, PDTs, and documentation
Dashboards, Looks, schedules, alerts, folders, embedding, APIs, and application integrations
Validation, CI, deployment, permissions, monitoring, performance, warehouse-cost, and incident controls
Analyst, LookML developer, administrator, governance, operator, and handover documentation
Engagement models / 09
Use the delivery structure that matches the platform work.
Assessment and roadmap
A bounded review of the current platform, requirements, gaps, risks, architecture, and an executable next-stage plan.
Fixed-scope implementation
A defined integration, migration, storefront, application, workflow, or platform outcome with explicit acceptance criteria.
Embedded platform specialists
Specialists working alongside internal product, engineering, operations, marketing, data, or enterprise teams.
Managed platform evolution
Ongoing maintenance, releases, integrations, support, optimisation, governance, and roadmap execution after launch.
Related platforms and services / 10
Compare adjacent platforms or continue into the wider system.
01
Power BI
Microsoft semantic modelling, DAX, reports, apps, Fabric, gateways, security, and capacity operations.
02
Tableau
Visual analytics, published data sources, dashboards, embedding, permissions, and platform operations.
03
Metabase
Open-source or managed self-service analytics, models, metrics, dashboards, embedding, and governance.
01
Data engineering
Pipelines, platforms, warehouses, analytics engineering, BI, and governed data operations.
02
Cloud and DevOps
Cloud architecture, delivery automation, observability, security, reliability, and platform operation.
03
Software development
Custom applications, backends, integrations, APIs, marketplaces, and enterprise systems.
FAQ
Looker development and modelling services
Platform scope, ownership, licences, data, integrations, security, migration, and long-term operation are clarified before delivery.
01Can Rokad build or refactor LookML models?
Can Rokad build or refactor LookML models?
Yes. We design projects, views, joins, Explores, dimensions, measures, security, PDTs, aggregate awareness, documentation, tests, validation, and deployment workflows.
02Can Rokad improve slow Looker queries?
Can Rokad improve slow Looker queries?
Yes. We analyse Explore joins, field selection, SQL, warehouse design, PDTs, aggregates, caching, filters, concurrency, schedules, and user patterns.
03Can Looker be embedded into a SaaS product?
Can Looker be embedded into a SaaS product?
Yes. We design tenant identity, authorisation, row security, content, themes, APIs, SDKs, query performance, telemetry, usage, and support.
04Can Rokad operate Looker after launch?
Can Rokad operate Looker after launch?
Yes. Managed services can cover LookML, validation, PDTs, queries, dashboards, schedules, permissions, users, embedding, performance, incidents, and new analytical products.
Looker · Business intelligence
Use LookML to create a governed analytical interface, not another layer of hidden SQL.
Rokad can design the semantic model, build Explores and dashboards, implement embedding and Git delivery, and operate performance and governance.
Contact / 05
Bring us the difficult technology problem.
Tell us what you need to build, improve, procure, deploy, or operate. We will respond with a practical next step.