Google Cloud
GCP Smart Analytics Services
We combine Google Cloud’s data and AI services into end-to-end analytics platforms that ingest data in real time, govern it centrally and turn it into decisions and automated actions.
Analytics That Moves at the Speed of Your Business
Google Cloud’s smart analytics portfolio covers every stage of the data lifecycle: Pub/Sub and Datastream to capture events and database changes, Dataflow and Dataproc to process them, BigQuery and BigLake to store and query them, Knowledge Catalog to govern them, Looker to explain them, and BigQuery ML, Gemini models and data agents to predict and act. Each service is strong on its own, but the value comes from combining them into a coherent architecture that suits your data volumes, latency needs and team skills.
Hanoi Technology designs and builds those architectures. We decide which workloads need streaming and which are fine in batch, choose between SQL-first and code-first processing, set up governance and observability, and deliver dashboards, predictive models and AI-assisted analysis that business teams actually use. Every component is deployed as code and monitored, so the platform keeps working as data grows.
We are pragmatic about technology choices. If your team writes SQL, we lean on BigQuery, Dataform and continuous queries; if it already runs Spark or Beam code, we use Dataproc or Dataflow. Every design includes a running-cost estimate, data quality checks and clear ownership of each dataset, so the platform remains understandable and affordable as more teams and use cases are added over time.
The Challenge
What Holds Analytics Back
Common blockers we see in analytics programs on Google Cloud and elsewhere.
Insights Arrive Too Late
Daily batch pipelines mean fraud, stockouts or service issues are spotted the next morning. By then, the moment to act has passed. Many teams know streaming would help but lack the skills to build and operate it.
Too Many Tools, Too Little Integration
Separate ingestion tools, schedulers, notebooks and BI platforms create gaps where data quality and ownership get lost. Troubleshooting a broken report means checking five systems.
Models That Never Leave the Notebook
Data scientists build promising models, but without feature pipelines, deployment and monitoring they rarely reach production. The business sees prototypes instead of results.
Low Trust in Data
Missing documentation, unclear lineage and inconsistent quality checks make users doubt the numbers. They fall back on spreadsheets, and the platform loses its purpose.
Business Value
What Smart Analytics on GCP Delivers
Real-Time Visibility
Pub/Sub, Dataflow and BigQuery streaming deliver events to dashboards and alerts within seconds.
One Governed Data Foundation
BigQuery and BigLake provide a single lakehouse for structured and unstructured data, governed through Knowledge Catalog.
Predictive and Prescriptive Insight
BigQuery ML and Gemini Enterprise Agent Platform add forecasting, classification and recommendations to standard reporting.
Natural-Language Analytics
Data agents and Conversational Analytics in Looker let business users ask questions and receive governed answers.
Fully Managed Operations
Serverless services remove cluster management, patching and capacity planning from your team’s workload.
Pay for What You Use
Autoscaling and usage-based pricing keep costs proportional to the value delivered, with FinOps controls on top.
From Dashboards to Decisions
The next stage of analytics is not more charts. It is systems that detect a change, explain why it happened and recommend or trigger the next action. Google Cloud now provides the building blocks for that, from continuous queries to data agents, and we help you put them to work responsibly.
We start these projects with narrow, well-defined decisions, such as reordering stock or flagging a delayed shipment, where the action, its owner and the cost of a wrong recommendation are all clear.
Our Services
Hanoi Technology’s End-to-End GCP Smart Analytics Services
Our smart analytics services cover architecture, engineering, intelligence and operations.
Analytics Architecture and Strategy
We assess your current data landscape and design a reference architecture on Google Cloud, choosing services for ingestion, processing, storage, governance and consumption based on latency, volume and skills.
- Reference architecture
- Service selection rationale
- Implementation roadmap
Streaming and Real-Time Analytics
We build event pipelines with Pub/Sub and Dataflow, capture database changes with Datastream, and use BigQuery continuous queries for real-time transformations and alerts.
- Streaming pipelines
- Real-time dashboards
- Event-driven alerts
Batch and Lakehouse Engineering
We implement BigQuery and BigLake lakehouses with Dataform transformations, Dataproc Serverless for Spark workloads and Cloud Composer orchestration.
- Lakehouse data layers
- Tested transformation pipelines
- Orchestrated workflows
Business Intelligence
We deliver Looker semantic models and dashboards, plus Looker Studio reports for lighter needs, built on curated datasets.
- Semantic model
- Dashboards and reports
- Self-service enablement
Machine Learning and AI Analytics
We build forecasting, anomaly detection and classification models with BigQuery ML or Gemini Enterprise Agent Platform, and deploy data agents for conversational analysis.
- Production ML models
- Model monitoring
- Data agent pilots
Data Governance and Observability
We set up Knowledge Catalog metadata, lineage, quality scans and pipeline monitoring with Cloud Monitoring and alerting.
- Catalog and glossary
- Quality and freshness checks
- Observability dashboards
Our Approach
How We Build Smart Analytics Platforms
We deliver in increments, each one useful on its own.
- 01
Define Decisions and Data
We start from the decisions the business needs to make and the data, latency and accuracy each one requires.
Deliverables: Decision and KPI map · Data source inventory
- 02
Design the Architecture
We select services and patterns, define security and governance, and estimate running costs.
Deliverables: Architecture design · Cost estimate
- 03
Deliver a First Use Case
We build one use case end to end, from ingestion to dashboard or model, to prove the architecture with real data.
Deliverables: Working use case · Performance results
- 04
Expand and Industrialize
We add sources and use cases, introduce CI/CD, testing and monitoring, and document operating procedures.
Deliverables: Additional use cases · DataOps pipeline
- 05
Operate and Evolve
We support the platform, review cost and performance monthly, and add new capabilities such as AI agents as they mature.
Deliverables: Managed operations · Quarterly roadmap updates
Use Cases
Smart Analytics Use Cases
Scenarios we design Google Cloud analytics platforms for.
Real-Time Fraud Signals
A payments company streams transactions through Pub/Sub and Dataflow, scores them with a model and flags suspicious patterns to analysts within seconds.
Demand Forecasting
A retailer uses BigQuery ML time-series models on sales, promotion and weather data to plan inventory by store and week.
IoT and Fleet Monitoring
A logistics operator ingests vehicle telemetry continuously and alerts dispatchers to delays, idling and maintenance needs.
Product Usage Analytics
A software company analyzes in-app events in BigQuery to understand feature adoption and predict which trial accounts will convert.
Voice of the Customer
A service team uses Gemini functions in BigQuery to classify and summarize reviews and support tickets, then tracks themes in Looker.
Operations Data Agent
Managers ask a data agent why yesterday’s fulfillment rate dropped and receive an answer drawn from governed operational data.
Technology
Smart Analytics Technology Stack
Google Cloud services we combine based on your needs.
- BigQuery
- BigLake
- Pub/Sub
- Dataflow
- Datastream
- Dataproc Serverless
- Dataform
- Cloud Composer
- Knowledge Catalog
- Looker
- Looker Studio
- BigQuery ML
- Gemini Enterprise Agent Platform
- Conversational Analytics
- Cloud Monitoring
- Terraform
Platforms
Deliver GCP Smart 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 · Food delivery
AI dispatch and live tracking for a food delivery startup
A fast-growing urban food delivery startup was losing customers to slow deliveries at peak times. We built AI order dispatching and connected restaurants, drivers and customers in real time.
Read the Full StoryMore Success Stories
Logistics
−25%operational costsRoute optimization and fleet visibility for a regional logistics company
A regional delivery company handling thousands of shipments a day was planning routes and dispatching by hand. We built dynamic route optimization, a fleet dashboard and a driver app.
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.
FSMA 204 After the Delay: Using Shipment Monitoring to Get Traceability-Ready by 2028
The FDA Food Traceability Rule deadline moved to July 2028, but the data work is large. Here is how to use shipment monitoring and event data to get ready without wasting the extra time.
CSRD After the Omnibus: What the Revised ESRS Mean for Reporting From 2027
The Omnibus and the simplified ESRS reshaped EU sustainability reporting in 2026. Here is who is in scope, what changed in the standards and how to adapt your data and process.
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.
Put Your Data to Work on Google Cloud
Share the decisions you want to improve and we will outline an analytics architecture to support them. Book a free consultation today.
GCP Smart Analytics FAQ
Do we need streaming analytics?
Only for decisions that lose value with delay, such as fraud detection, dispatch, inventory alerts or live customer experiences. Many reporting needs are well served by hourly or daily batch processing, which is simpler and cheaper to operate. We assess each use case against its latency requirement and recommend streaming only where it clearly pays off. The architectures we design allow you to add streaming later without rebuilding.
Dataflow or Dataproc: which should we use?
Dataflow is a fully managed, serverless service for Apache Beam pipelines and is a strong choice for unified streaming and batch processing with autoscaling. Dataproc runs Apache Spark and Hadoop ecosystem tools, including a serverless option, and suits teams with existing Spark code or skills. For many SQL-based transformations, Dataform on BigQuery is simpler than either. We choose based on existing code, team skills and workload shape.
How do you make analytics trustworthy?
We define metrics in a semantic layer, test transformations with every change, run data quality and freshness checks, and record lineage and ownership in Knowledge Catalog. Dashboards display data freshness so users know how current the numbers are. When a check fails, alerts go to the owning team before users notice. Trust grows when issues are caught and explained quickly.
Can business users ask questions in natural language?
Yes, with preparation. Conversational Analytics in Looker and data agents on Google Cloud can answer plain-language questions, but answer quality depends on curated data, clear field descriptions and good semantic definitions. We start with a pilot on a well-modeled subject area, test answers against a set of real questions, and expand once accuracy is proven.
What does a smart analytics platform cost to run?
Running costs depend on data volume, query patterns, streaming throughput and the number of users. Serverless services charge by usage, so a well-designed platform scales cost with value. We provide a cost estimate during design, implement partitioning, incremental processing and budgets, and review spend monthly. Billing data is part of the platform so you see costs by pipeline and team.
How do you work with our in-house data team?
We can lead delivery, work as an embedded extension of your team, or coach your engineers while they build. Work happens in your repositories and projects, with code reviews, documentation and sprint demos. Weekly written updates keep stakeholders informed. By the end of the engagement your team should be able to operate and extend the platform independently, or we can continue as a managed service.