Platforms
Snowflake Data Cloud Engineering
We build Snowflake platforms that are governed, cost-efficient and ready for AI, from first migration through data products, sharing and agent-powered analytics.
Build on Snowflake With Discipline From Day One
Snowflake separates storage from compute and lets teams scale warehouses independently, which makes it easy to start and easy to overspend. The platform now spans much more than a cloud data warehouse: Openflow for ingestion, Snowpark for Python and Java processing, Horizon for governance and the catalog, Apache Iceberg tables for open storage, Snowflake Postgres for transactional workloads, Cortex AI for models and search, and agent experiences that let business users work with governed data in natural language.
Hanoi Technology works with Snowflake on AWS, Azure and Google Cloud. We migrate legacy warehouses, design account and role structures, build modeled data layers with dbt, set up governance and data sharing, and deliver AI applications on Cortex. Every engagement includes cost monitoring, because a well-designed Snowflake platform should make spending predictable.
The Challenge
Common Snowflake Challenges
Snowflake removes infrastructure work but introduces new design and cost decisions.
Credit Consumption Surprises
Oversized warehouses, missing auto-suspend settings and inefficient queries can multiply spend. Without resource monitors and tagging, nobody can say which team or workload is responsible.
Role Sprawl and Unclear Access
Ad-hoc grants accumulate as teams grow. Before long, auditors find users with access to sensitive tables they never needed, and fixing roles risks breaking production jobs.
Lift-and-Shift Migrations
Copying old warehouse designs and stored procedures into Snowflake as-is carries over the same problems. Performance and maintainability improvements never materialize.
AI Pilots Without Semantics
Natural language querying fails when tables lack clear definitions, relationships and metrics. Business users lose trust after a few wrong answers.
Business Value
What a Well-Designed Snowflake Platform Delivers
Predictable Spend
Right-sized warehouses, resource monitors, budgets and query optimization keep consumption aligned with value. Costs are visible by team and use case.
Clean, Tested Data Models
dbt models with tests and documentation turn raw data into trusted data products. Changes go through version control and review.
Governance Built In
Horizon features such as tags, masking and row access policies, and access history make security consistent. Sensitive data is protected without blocking analysts.
Open Storage Options
Apache Iceberg tables let other engines read and write the same data. You avoid lock-in for datasets shared across platforms.
Secure Data Sharing
Partners, subsidiaries and customers can access live data through secure sharing and listings. Nobody emails CSV files or maintains fragile FTP exports.
AI Close to the Data
Cortex AI functions, Cortex Search and agents run inside Snowflake’s security boundary. Unstructured documents and structured data can be combined without exporting them.
Semantics Are the Bridge to AI
Natural language analytics and data agents depend on well-defined metrics, relationships and business terms. Investing in semantic views and documentation is what turns a Snowflake warehouse into something an AI assistant can query correctly, and it also makes every human analyst more productive.
Our Services
Hanoi Technology’s End-to-End Snowflake Services
We cover Snowflake architecture, data engineering, governance, analytics and AI.
Architecture and Account Setup
We design accounts, databases, schemas, warehouses and role hierarchies, then deploy them as code. Environments, naming conventions and network policies are agreed up front.
- Reference architecture
- Role and access model
- Infrastructure as code with Terraform
Warehouse Migration to Snowflake
We migrate from on-premises or cloud warehouses such as Teradata, Oracle, SQL Server, Redshift or Synapse. Code is converted, optimized and validated with parity tests before cutover.
- Migration inventory and wave plan
- Converted and optimized code
- Parity test results
Data Pipelines and Modeling
We build ingestion with Openflow, Snowpipe or tools such as Fivetran, and transformations with dbt or Snowpark. Pipelines have tests, documentation and alerting.
- Ingestion pipelines
- dbt project with tests
- Pipeline monitoring
Governance and Data Sharing
We implement classification, tagging, masking and row access policies, and set up secure data sharing or Marketplace listings where needed. Access reviews become repeatable.
- Governance policies
- Data classification
- Sharing and listing setup
Cost Optimization
We analyze query history and warehouse usage, right-size compute, tune clustering and set resource monitors and budgets. Recommendations are ranked by expected savings.
- Cost baseline and analysis
- Optimization actions
- Ongoing cost dashboards
Cortex AI and Data Agents
We build semantic views, Cortex Search services and agents that answer questions over structured and unstructured data. Each solution is evaluated on real business questions before rollout.
- Semantic views and metrics
- Cortex Search services
- Agent evaluation results
Our Approach
How We Deliver Snowflake Projects
We combine a solid foundation with early, visible business results.
- 01
Discover and Baseline
We review current data sources, workloads, costs and pain points, and agree on priority use cases with business owners. A cost baseline is captured for later comparison.
Deliverables: Current-state assessment · Prioritized use cases
- 02
Design the Foundation
Account structure, roles, environments, governance policies and deployment automation are designed and implemented. This is reused by every later project.
Deliverables: Foundation deployed as code · Governance model
- 03
Build Data Products
We ingest priority sources and build tested dbt models for the first use cases. Business users review outputs during sprint demos.
Deliverables: Production data products · Data documentation
- 04
Enable Analytics and AI
BI tools are connected, semantic views defined and, where relevant, Cortex agents or search services deployed and evaluated.
Deliverables: BI integrations · AI features with evaluation
- 05
Operate and Optimize
We monitor pipelines and spend, tune performance and hand over runbooks. Ongoing support is available as a dedicated team or managed service.
Deliverables: Runbooks · Monthly optimization reviews
Use Cases
Snowflake Use Cases We Build
Common projects that deliver value quickly on Snowflake.
Finance and Revenue Reporting
ERP, billing and CRM data are combined into governed revenue and margin models. Finance teams close faster with consistent numbers across reports.
Customer Data Platform on Snowflake
Customer events and transactions are unified in Snowflake and audiences are activated in marketing tools. Data stays in your account instead of a separate CDP silo.
Partner Data Sharing
A distributor shares live sell-through data with suppliers through secure shares. Suppliers query current figures instead of waiting for monthly spreadsheets.
Contract and Document Search
Cortex Search indexes contracts and policy documents stored in Snowflake. Staff ask questions and receive answers with links to the source clauses.
Natural Language Sales Analytics
Sales managers ask questions about pipeline and bookings through a Snowflake agent backed by semantic views. Analysts review tricky questions and improve definitions.
Legacy Warehouse Retirement
A company moves its on-premises warehouse to Snowflake in waves, then decommissions the old hardware and licenses. Reports keep running throughout the transition.
Technology
Snowflake Technologies We Use
We combine native Snowflake features with proven ecosystem tools.
- Snowflake
- Snowpark
- Snowflake Openflow
- Snowpipe
- Snowflake Horizon
- Apache Iceberg
- Snowflake Postgres
- Cortex AI
- Cortex Search
- Streamlit in Snowflake
- dbt
- Fivetran
- Terraform
- Apache Airflow
- Power BI
- Tableau
Platforms
Deliver Snowflake on Your Preferred Platform
- SnowflakeElastic cloud data platform for analytics and data sharing.Explore
- AWSBuild, migrate and run data and AI workloads on Amazon Web Services.Explore
- Microsoft AzureModernize data, analytics and AI within your Azure tenant.Explore
- Google CloudBigQuery, Looker and Vertex AI for data-driven organizations.Explore
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 · Fintech
All-in-one digital banking app for a traditional bank
A traditional financial institution wanted to compete with neobanks for mobile-first customers. We replaced a set of separate banking, investing and payment apps with one product that people use every day.
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.
Real estate
+38%lead conversion rateProperty 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.
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.
Get More Value From Every Snowflake Credit
Book a free consultation to discuss migration, governance, cost or AI on Snowflake. We reply within 24 hours and can review your current setup with you.
Snowflake FAQ
Is Hanoi Technology an official Snowflake partner?
We do not claim official Snowflake partner status. We are an independent engineering team that builds on Snowflake for clients. Your Snowflake contract and support remain directly with Snowflake or your cloud marketplace. We focus on architecture, migration, pipelines, governance, cost optimization and AI applications, and we will tell you honestly when another tool fits a requirement better.
How do you estimate Snowflake costs before migration?
We analyze your current workloads: data volumes, query patterns, concurrency, schedules and growth. From that we model warehouse sizes and run times for typical workloads, plus storage. During a pilot, we measure actual consumption on representative queries and refine the estimate. Cost controls such as auto-suspend, resource monitors and budgets are part of the design rather than added later.
How long does a Snowflake migration take?
Smaller migrations with a few dozen pipelines and reports can complete within a few months. Large enterprise warehouses with thousands of objects and stored procedures are migrated in waves over a longer period. The main variables are code complexity, data quality, testing requirements and the number of downstream consumers. We provide a wave plan with milestones after the assessment phase.
Should we use Snowflake native tables or Apache Iceberg tables?
Native tables are simplest and offer the most automated optimization. Iceberg tables make sense when other engines, such as Spark or Databricks, need to read or write the same data, or when you want to keep storage in an open format under your control. Many platforms use both: native tables for internal models and Iceberg for shared or very large datasets.
How do you make Cortex agents give accurate answers?
Accuracy depends on semantics. We create semantic views that define metrics, dimensions and relationships, document business terms and build an evaluation set of real questions with expected answers. Agents are tested against that set before release and monitored afterward. Questions the agent gets wrong are reviewed and used to improve definitions, which raises accuracy steadily over time.
Can you support our Snowflake platform long term?
Yes. We offer dedicated teams and managed services covering pipeline operations, new data products, access requests, cost reviews and platform upgrades. You receive weekly written progress updates and regular sprint demos. Many clients use us for a migration first and then keep a smaller team for ongoing development and optimization.