Platforms
Databricks Lakehouse and AI Engineering
We design, build and run Databricks platforms where governed data, reliable pipelines, analytics and AI agents share one foundation instead of five disconnected tools.
Build a Databricks Platform Your Teams Can Trust
Databricks has grown from a managed Spark service into a full data intelligence platform: Delta Lake and Apache Iceberg tables, Unity Catalog for governance, Lakeflow for ingestion and pipelines, Databricks SQL for warehousing, AI/BI dashboards and Genie for business users, Mosaic AI for models, Agent Bricks for building agents, and Lakebase for operational Postgres workloads. The breadth is powerful, but it also means architecture choices made early shape cost and governance for years.
Hanoi Technology works with Databricks on AWS, Azure and Google Cloud. We set up workspaces and Unity Catalog correctly from the start, migrate legacy warehouses and Hadoop jobs, build medallion pipelines that are tested and observable, and take machine learning and generative AI use cases from notebook to production with evaluation, monitoring and cost controls.
The Challenge
Where Databricks Projects Go Wrong
The platform is flexible, which makes discipline in design and operations essential.
Notebook Sprawl
Teams start with interactive notebooks and never move logic into versioned, tested code. Production depends on a notebook a single person understands, and a small change breaks a downstream dashboard.
Late or Partial Unity Catalog Adoption
Workspaces that started with the legacy Hive metastore carry inconsistent permissions and no lineage. Migrating later is harder than designing catalogs, schemas and access groups up front.
Unpredictable Compute Spend
All-purpose clusters left running, oversized jobs and missing tagging make the monthly bill hard to explain. Finance teams start questioning the platform even when the value is real.
AI Prototypes That Never Ship
Retrieval and agent demos look impressive but lack evaluation sets, guardrails and monitoring. Without them, nobody is willing to put the agent in front of customers or employees.
Business Value
What You Gain From a Well-Built Databricks Platform
One Governed Catalog
Unity Catalog governs tables, files, models, functions and agents with fine-grained permissions and lineage. Auditors and engineers look at the same source of truth.
Reliable, Declarative Pipelines
Lakeflow pipelines with expectations catch bad data at ingestion. Failures are visible and recoverable instead of silently corrupting reports.
Warehouse Performance on Open Formats
Databricks SQL serves BI tools from Delta or Iceberg tables. You avoid copying data into a separate proprietary warehouse.
Self-Service for Business Users
AI/BI dashboards and Genie spaces let business teams ask questions in natural language using curated semantics. Analysts spend less time on repetitive requests.
Production-Grade AI
Mosaic AI and Agent Bricks provide model serving, evaluation and governed tool access. Agents are measured against quality benchmarks before and after release.
Cost Visibility and Control
System tables, budgets, tagging and cluster policies make spend attributable by team and workload. Optimization becomes an ongoing practice rather than a crisis response.
Governance First, Then Scale
The fastest Databricks programs are the ones that settle catalog design, identity, environments and deployment automation before building dozens of pipelines. That foundation takes weeks, not months, and it is what makes adding the next hundred tables or the first AI agent routine instead of risky.
Our Services
Hanoi Technology’s End-to-End Databricks Services
Our Databricks services cover the platform, the data and the AI built on top of it.
Lakehouse Architecture and Setup
We design workspaces, catalogs, environments, networking and identity integration, and deploy them with infrastructure as code. Cluster policies and tagging are set from day one.
- Reference architecture
- Terraform workspace and catalog setup
- Cluster policies and tagging standards
Unity Catalog Migration and Governance
We migrate from the Hive metastore, define access groups, apply row and column level controls, and enable lineage and audit. Sensitive data is classified and masked.
- Catalog and permission model
- Migration runbook
- Audit and lineage dashboards
Data Engineering With Lakeflow
We build ingestion and transformation pipelines using Lakeflow Connect, Lakeflow Declarative Pipelines and Jobs, organized into bronze, silver and gold layers. Code lives in Git and deploys through Databricks Asset Bundles.
- Medallion pipelines with expectations
- CI/CD with Asset Bundles
- Pipeline monitoring and alerts
Warehouse and Hadoop Migration
We move workloads from legacy warehouses, Hadoop or on-premises Spark to Databricks. SQL and jobs are converted, tested for parity and cut over in waves.
- Workload inventory and wave plan
- Converted code and parity tests
- Decommissioning checklist
Analytics, BI and Genie
We model gold-layer data products, connect Power BI or Tableau through Databricks SQL, and configure AI/BI dashboards and Genie spaces with curated instructions and metrics.
- Dimensional and semantic models
- SQL warehouse sizing
- Genie spaces with tested questions
Machine Learning and AI Agents
We build feature pipelines, train and register models with MLflow, deploy serving endpoints and create retrieval and agent applications with Agent Bricks and Vector Search. Every release is evaluated against a defined test set.
- MLflow model lifecycle
- Agent evaluation sets
- Serving endpoints with monitoring
Our Approach
How We Deliver on Databricks
We move from foundation to value in short, demonstrable increments.
- 01
Assess and Prioritize
We review current data platforms, workloads, skills and costs, then select two or three use cases with clear business owners. Success measures are agreed before build starts.
Deliverables: Current-state assessment · Prioritized use case list
- 02
Lay the Foundation
Workspaces, Unity Catalog, identity, networking and CI/CD are deployed as code. Environments for development, testing and production are separated.
Deliverables: Infrastructure as code · Governance model
- 03
Build the First Data Products
We deliver pipelines and gold tables for the first use cases, with expectations, documentation and lineage. Business users review results in sprint demos.
Deliverables: Production pipelines · Documented data products
- 04
Add Analytics and AI
Dashboards, Genie spaces, models or agents are built on the governed data products. Evaluation and monitoring are part of the definition of done.
Deliverables: BI and Genie deliverables · Model or agent with evaluation report
- 05
Operate, Optimize and Expand
We tune compute, set budgets and alerts, train your team and plan the next wave. Ongoing support is available as a dedicated team or managed service.
Deliverables: Cost optimization report · Runbooks and training
Use Cases
Databricks Use Cases We Build
Typical projects combine data engineering with analytics or AI on the same platform.
Customer 360 and Personalization
Events, transactions and CRM data are unified into customer profiles in the lakehouse. Recommendation models and segments feed marketing and ecommerce channels.
Demand Forecasting
A distributor trains forecasting models per product and region using historical sales, promotions and external signals. Planners see forecasts and confidence ranges in their BI tool.
Streaming Operations Analytics
Delivery or logistics events are processed in near real time to monitor service levels. Operations teams get alerts when routes or depots fall behind.
Document Intelligence and RAG
Contracts, manuals and policies are parsed, chunked and indexed with Vector Search. An internal agent answers questions with citations and respects Unity Catalog permissions.
Legacy Warehouse Consolidation
Several departmental warehouses and Hadoop jobs are consolidated into one governed lakehouse. Licensing overlap disappears and analysts work from consistent definitions.
Natural Language Analytics
Sales leaders ask questions about pipeline and revenue in a Genie space backed by curated metrics. Analysts review and improve answers instead of writing every query by hand.
Technology
Databricks Technologies We Work With
We use native Databricks capabilities first and integrate the wider ecosystem where it adds value.
- Unity Catalog
- Delta Lake
- Apache Iceberg
- Lakeflow
- Databricks SQL
- AI/BI Genie
- Mosaic AI
- Agent Bricks
- MLflow
- Vector Search
- Lakebase
- Databricks Asset Bundles
- Apache Spark
- Terraform
- dbt
- Power BI
Platforms
Deliver Databricks on Your Preferred Platform
- DatabricksUnified lakehouse for data engineering, analytics and AI.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 · E-commerce and retail
Personalized 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.
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.
Real estate
90%pricing accuracyTransparent AI property valuation for a multi-city marketplace
A growing real estate marketplace wanted buyers to trust its prices. We built a valuation engine and property pages that compare the estimated value with the asking price.
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.
Make Databricks Deliver Real Business Value
Book a free consultation to review your Databricks plans or current workspace. We reply within 24 hours and can recommend concrete next steps for governance, pipelines or AI.
Databricks FAQ
Is Hanoi Technology an official Databricks partner?
We do not claim official Databricks partner status. We are an engineering team that builds on Databricks for clients across cloud providers. Licensing, consumption and vendor support remain between you and Databricks or your cloud marketplace. Our focus is architecture, implementation, migration and operations, and we give independent advice when a different tool would serve a specific requirement better.
Which cloud should we run Databricks on?
Databricks runs on AWS, Microsoft Azure and Google Cloud with largely the same features. The best choice usually follows where your data, identity provider and other workloads already live, along with existing cloud commitments. We avoid recommending a cloud move just for Databricks. If you are multi-cloud, Unity Catalog and open table formats help keep governance consistent across environments.
How long does it take to get a production Databricks platform?
A governed foundation with workspaces, Unity Catalog, CI/CD and environments can typically be built in a few weeks. The first production data products follow in subsequent sprints, depending on source complexity and data quality. Large migrations from legacy warehouses or Hadoop run in waves over several months. We agree milestones up front and show working results in regular sprint demos.
How do you control Databricks costs?
We apply cluster policies, auto-termination, job clusters instead of all-purpose clusters for scheduled work, serverless compute where it fits, and tagging that maps spend to teams. System tables and budgets give ongoing visibility. We also review query patterns and table layouts, because poorly partitioned or unoptimized tables drive unnecessary compute. Cost reviews are part of every managed engagement.
How do you make AI agents on Databricks safe for production?
Every agent gets an evaluation set built with business experts, covering correct answers, refusals and edge cases. We use Unity Catalog permissions so the agent sees only data the user may see, add guardrails and logging through serving and gateway features, and monitor quality after release. Releases are gated on evaluation results, so improvements are measured rather than assumed.
Can you migrate our existing warehouse to Databricks?
Yes. We inventory tables, views, stored procedures, jobs and reports, convert code with a mix of automated tools and manual review, and run parity tests comparing outputs between old and new systems. Workloads move in waves grouped by dependency, with rollback plans for each wave. Decommissioning the old platform is planned explicitly so you stop paying for both.