Google Cloud
Looker Analytics Services
We implement Looker as a governed semantic layer, so every dashboard, embedded report and AI agent answers from the same trusted metric definitions.
Trusted Metrics, Delivered Everywhere
Looker is Google Cloud’s business intelligence platform, built around LookML, a modeling language that defines dimensions, measures, joins and access rules in version-controlled code. Instead of each dashboard calculating revenue its own way, the semantic model holds the definition once and every query inherits it. That same model now grounds Conversational Analytics, so business users can ask questions in plain language and get answers consistent with official reports.
Hanoi Technology designs and builds Looker deployments that hold up as usage grows. We model your data in LookML with Git workflows and CI checks, design dashboards around the decisions people actually make, embed analytics into customer-facing products, and tune performance and cost on BigQuery and other warehouses. Whether you are starting fresh, migrating from another BI tool or rescuing a sprawling instance, we bring structure and engineering practice.
We work in your Looker instance and Git repository from day one, with pull requests, code review and automated validation, so every change is traceable. Business owners join regular demos to confirm that metrics and dashboards match how they think about the business. Before handover, we document modeling conventions, permission design and release procedures, and we can stay on as a managed support team for ongoing development and administration.
The Challenge
Common Looker Pitfalls
Looker rewards good modeling and punishes shortcuts; these are the issues we fix most often.
Model Sprawl
Projects grow into hundreds of overlapping Explores and views copied from one another. Users cannot tell which Explore is authoritative and developers fear changing anything. Performance and governance degrade with every copy.
Slow Dashboards and Rising Warehouse Bills
Dashboards with dozens of tiles fire unoptimized queries against large tables. Without persistent derived tables, aggregate awareness or caching policies, users wait and warehouse costs climb.
Weak Development Workflow
Changes pushed straight to production without validation break content. Missing code review, LookML tests and content validation turn every release into a risk.
AI Answers That Do Not Match Reports
Natural-language tools that bypass the semantic layer produce numbers that disagree with dashboards. Conversational analytics only earns trust when it is grounded in well-described, curated LookML.
Business Value
What a Well-Built Looker Platform Gives You
One Definition per Metric
Revenue, margin and active users are defined in LookML once and reused in every Explore, dashboard and API call, ending reconciliation meetings.
Self-Service with Guardrails
Business users explore curated datasets freely, while row-level and field-level permissions keep sensitive data restricted to the right people.
Analytics Inside Your Product
Embedded dashboards and the Looker API put branded, secure analytics in front of customers and partners without building a reporting engine yourself.
Conversational Answers You Can Trust
Conversational Analytics and Gemini in Looker let people ask questions in natural language, answered from governed LookML definitions.
Software Engineering for BI
Git-based development, pull requests and continuous integration make changes reviewable, testable and reversible.
Warehouse-Native Performance
Looker queries data in place on BigQuery and other SQL databases, so there are no extracts to refresh or copies to secure.
The Semantic Layer Is Now AI Infrastructure
Every company will soon have agents answering data questions. The ones that answer correctly will be grounded in a curated semantic model with clear descriptions and governed access. A clean LookML project is the most practical investment you can make in trustworthy AI analytics.
We therefore treat field descriptions, curated Explores and access rules as first-class deliverables, not afterthoughts, and test natural-language answers against real business questions before release.
Our Services
Hanoi Technology’s End-to-End Looker Analytics Services
From first model to embedded, AI-ready analytics, we cover the full Looker lifecycle.
Looker Implementation
We set up instances, connections, user groups and permission models, then build the first LookML project around your highest-value subject areas.
- Instance and connection setup
- Role and permission model
- Initial LookML project
LookML Modeling and Refactoring
We design views, Explores and derived tables that follow a consistent style, and refactor existing projects to remove duplication and dead code.
- LookML style guide
- Refactored model with tests
- Field descriptions and labels
Dashboards and Reporting
We run workshops to understand decisions, then design dashboards, scheduled deliveries and alerts that support them, including paginated reports where needed.
- Dashboard wireframes
- Production dashboards
- Schedules and alerts
Embedded Analytics
We embed Looker content in your web application with signed embedding or the embed SDK, applying tenant isolation and white-label styling.
- Embedding architecture
- Tenant-aware access filters
- Branded embedded experiences
Conversational Analytics Enablement
We prepare LookML for natural-language use with curated Explores, synonyms and descriptions, and configure agents that respond accurately.
- AI-ready Explore curation
- Agent configuration
- Accuracy test set
Performance, Governance and Support
We tune PDTs, aggregate tables and caching, monitor usage with System Activity, and provide ongoing development and administration.
- Performance tuning report
- Content cleanup
- Managed Looker support
Our Approach
Our Looker Delivery Approach
We model first, visualize second and automate quality throughout.
- 01
Discover Metrics and Users
We interview stakeholders, inventory existing reports and agree on the definitions of key metrics before writing any code.
Deliverables: Metric dictionary · User personas and needs
- 02
Design the Semantic Model
We map sources to views and Explores, decide on derived tables and plan access controls aligned with your data governance.
Deliverables: Model architecture · Access control design
- 03
Build in Sprints
We develop LookML in Git branches with pull requests, LookML validation and data tests, demoing working Explores and dashboards each sprint.
Deliverables: Versioned LookML · Sprint demos
- 04
Validate and Optimize
We reconcile numbers with source systems and legacy reports, then tune query performance and warehouse cost.
Deliverables: Reconciliation sign-off · Performance baseline
- 05
Launch and Enable
We roll out to user groups with training, office hours and usage monitoring, then iterate based on adoption data.
Deliverables: Training materials · Adoption dashboard
Use Cases
Looker Use Cases
Concrete scenarios where Looker delivers measurable value.
Executive KPI Hub
A leadership team reviews revenue, bookings and retention on one dashboard where every figure traces back to an approved LookML definition.
Customer-Facing Analytics
A SaaS company embeds per-tenant usage dashboards in its product, letting customers explore their own data without seeing anyone else’s.
Store and Region Performance
A retailer gives regional managers self-service Explores with row-level security, so each sees only their stores while sharing common metrics.
Marketing Attribution Reporting
A digital team combines GA4, ad spend and CRM data in BigQuery and analyzes channel performance in Looker on a single cost and conversion model.
Ask-Your-Data for Operations
Operations managers use Conversational Analytics to ask about delayed shipments and get answers drawn from the same Explores as the weekly report.
BI Consolidation
A company replaces several departmental BI tools with Looker, migrating priority reports and retiring duplicate data extracts.
Technology
Looker Technology Stack
The tools we use around Looker for modeling, delivery and operations.
- Looker
- LookML
- Looker Studio
- Conversational Analytics
- Gemini in Looker
- Looker API
- Looker Embed SDK
- BigQuery
- Spectacles
- Git
- GitHub Actions
- Dataform
- dbt
- Cloud SQL
- Snowflake
- Knowledge Catalog
Platforms
Deliver Looker Analytics on Your Preferred Platform
Why Hanoi Technology
Why Organizations Choose Hanoi Technology
Senior, Cross-Functional Teams
Strategy, data, AI, cloud and engineering specialists work as one team, so nothing is lost between hand-offs.
Proven Delivery
More than 50 projects delivered for clients in North America, Europe and Asia-Pacific.
Radical Transparency
A written update every week and a demo of working software every sprint, so you always know where things stand.
Cost-Effective Global Delivery
Experienced engineers from our Hanoi delivery hub, with working-hour overlap for every major time zone.
Success Story · Real estate
Property discovery and lead conversion for a US real estate marketplace
A US marketplace connecting buyers, sellers and agents had plenty of traffic but few enquiries. We built personalized property discovery, AI valuation and a one-tap route to contact an agent.
Read the Full StoryMore Success Stories
E-commerce and retail
+32%conversion ratePersonalized shopping experience for a US fashion retailer
A fast-growing US fashion and lifestyle retailer had plenty of traffic but a 2.1% conversion rate. We combined a recommendation engine with a simpler path from discovery to checkout.
Resources
Featured Resources
Industry trends and practical guidance to help you get more value from data, AI and technology.
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CSRD After the Omnibus: What the Revised ESRS Mean for Reporting From 2027
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OpenTelemetry in 2026: Building a Vendor-Neutral Observability Strategy
OpenTelemetry has become the default way to collect telemetry. Here is what changed in 2026 and a practical roadmap for a vendor-neutral observability stack.
Build Analytics People Trust
Whether you are new to Looker or untangling an existing instance, we can help. Book a free consultation and get a practical plan within days.
Looker Analytics FAQ
What is the difference between Looker and Looker Studio?
Looker is the enterprise BI platform with the LookML semantic layer, granular permissions, embedding and APIs. Looker Studio is a lighter, free-to-start reporting tool suited to quick, ad hoc reports. Many organizations use both: Looker for governed metrics and Looker Studio for lightweight reporting, and Looker Studio can connect to Looker models so that even quick reports reuse the governed definitions.
How long does a Looker implementation take?
A first production subject area, such as sales or finance, typically takes six to ten weeks including discovery, modeling, dashboards and training. Broader rollouts across several departments are delivered incrementally over a few months. The biggest variables are data readiness in the warehouse, the number of metrics that need agreement, and how much existing reporting must be reconciled.
Do we need BigQuery to use Looker?
No. Looker queries many SQL databases, including Snowflake, Amazon Redshift, PostgreSQL and SQL Server. BigQuery is a natural fit because of its scale and integration with Google Cloud services, and some newer Looker AI features work best with Google Cloud data. We assess your current warehouse and recommend whether to stay, migrate or run a hybrid.
How do you keep Looker costs and query costs under control?
We design persistent derived tables and aggregate awareness for common queries, set datagroups and caching policies, limit expensive Explores and monitor usage through System Activity. On BigQuery we also review partitioning, clustering and reservation strategy. We share a regular report showing heavy queries and dashboards so you can see where spend comes from.
Can you migrate our reports from Tableau or Power BI?
Yes. We start by inventorying existing workbooks and usage, retire what nobody uses, and rebuild priority content on a LookML model rather than copying calculations one-to-one. That approach usually produces fewer, better dashboards. We run old and new side by side until the business signs off on the numbers.
Is Conversational Analytics ready for business users?
It can be, when the underlying model is prepared. Answers are only as good as the Explores, field descriptions and synonyms behind them. We curate AI-ready Explores, build a test set of real questions with expected answers, measure accuracy, and roll out to a pilot group first. That gives you evidence before broad release.