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Data Engineering

Data Lakehouse Services

Unify your data lake and data warehouse on open table formats, so BI, data science and AI work from one governed copy of data.

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One Platform for Analytics, Data Science and AI

Many organizations run a data lake for raw files and machine learning alongside a separate warehouse for reporting, copying data between them and maintaining two security models. A lakehouse removes that split. Open table formats such as Delta Lake and Apache Iceberg add ACID transactions, schema enforcement, time travel and efficient updates to low-cost object storage, so the same tables can serve SQL dashboards, Spark pipelines and model training.

Hanoi Technology designs and builds lakehouses on Databricks, Microsoft Fabric, Snowflake with Iceberg tables, AWS and Google Cloud. We implement layered bronze, silver and gold architectures, unified catalogs and access policies, and the table maintenance practices that keep performance high as data grows.

The Challenge

Problems a Lakehouse Is Built to Solve

Signs that a split lake and warehouse architecture is holding you back.

Data Swamps Without Structure

Files land in the lake with no consistent schema, ownership or documentation. Analysts cannot find reliable data, and the lake becomes a dumping ground rather than an asset.

Duplicate Copies and Pipelines

The same data is copied from lake to warehouse and into extracts for data science. Every copy adds cost, latency and a chance for numbers to diverge.

Inconsistent Security and Governance

Permissions on files, warehouse tables and notebooks are managed separately. Proving who can access sensitive data becomes guesswork.

Poor Performance on Raw Files

Querying millions of small files or unpartitioned data is slow and expensive. Without compaction, clustering and statistics, dashboards time out.

Business Value

Benefits of a Well-Designed Lakehouse

One Copy of Data for Every Workload

BI, ad hoc SQL, streaming and machine learning read the same governed tables. Fewer copies mean lower cost and consistent results.

Open Formats, No Lock-In

Delta Lake and Apache Iceberg are open, so multiple engines such as Spark, Trino, Snowflake and Fabric can read the same data. You keep control of your storage.

Reliable, Reproducible Data

ACID transactions, schema enforcement and time travel prevent partial writes and let you reproduce any past state for audits or model retraining.

Unified Governance and Lineage

A single catalog manages permissions, tags, masking and lineage across tables, files, models and dashboards. Compliance reviews become simpler.

Cost-Efficient Storage at Scale

Object storage keeps years of detailed history affordable, while compute scales independently and only when needed.

Faster Path to AI

Curated silver and gold tables, feature tables and document volumes sit next to each other, ready for machine learning and retrieval-augmented generation.

Architecture Matters More Than the Logo

A lakehouse succeeds or fails on design decisions: how layers are defined, how tables are partitioned or clustered, how quality is enforced between layers and who owns each data product. We bring a tested blueprint and adapt it to your domains, so the platform stays organized when the tenth team joins, not just the first.

Our Services

Hanoi Technology’s End-to-End Data Lakehouse Services

We cover lakehouse design, build, migration and operation.

Lakehouse Architecture and Blueprint

We design storage accounts or buckets, catalogs, schemas, environments, networking and compute policies, along with naming conventions and layer definitions that every team follows.

  • Reference architecture
  • Layer and naming standards
  • Environment and workspace design

Medallion Pipeline Development

We build ingestion into bronze, cleansing and conformance into silver, and business-ready aggregates and dimensional models in gold, using Spark, SQL, Delta Live Tables or dbt.

  • Bronze ingestion pipelines
  • Silver cleansing and deduplication
  • Gold data products and marts

Unified Catalog and Governance

We implement Unity Catalog, AWS Lake Formation, Microsoft Purview or Polaris-based catalogs with role-based access, row filters, column masks, tags and automated lineage.

  • Catalog and permissions model
  • PII classification and masking
  • Lineage and audit configuration

Performance and Table Optimization

We configure clustering, partitioning, compaction, statistics and data retention so queries stay fast and storage stays clean as volumes grow.

  • Clustering and partitioning strategy
  • Automated maintenance jobs
  • Query performance benchmarks

Lake and Warehouse Consolidation

We migrate existing data lakes, Hive tables and warehouse workloads into the lakehouse, converting formats and repointing consumers with validated results.

  • Migration inventory and waves
  • Format conversion to Delta or Iceberg
  • Consumer repointing and validation

Lakehouse for BI and AI Enablement

We connect Power BI, Tableau or Looker through SQL endpoints and semantic models, and prepare feature tables and document volumes for machine learning and GenAI teams.

  • SQL warehouse and semantic layer
  • Feature and vector-ready tables
  • Self-service onboarding guide

Our Approach

How We Deliver a Lakehouse

Blueprint first, then a working slice, then scale across domains.

  1. 01

    Assess Workloads and Data

    We review current lake, warehouse and data science workloads, data volumes, formats and access patterns to decide what the lakehouse must support first.

    Deliverables: Workload assessment · Platform and format recommendation

  2. 02

    Define the Blueprint

    We agree on layers, catalogs, ownership, security and quality rules, and set up infrastructure as code for repeatable environments.

    Deliverables: Lakehouse blueprint · Infrastructure as code

  3. 03

    Deliver a First Data Product

    One priority domain flows from bronze to gold and into a dashboard or model, with tests, lineage and access controls working end to end.

    Deliverables: Working end-to-end slice · Quality and lineage in place

  4. 04

    Scale to More Domains

    Additional sources and teams onboard using templates and the blueprint, with sprint demos and regular architecture reviews.

    Deliverables: Reusable pipeline templates · Onboarded domains

  5. 05

    Optimize and Operate

    We tune table layouts and compute policies, monitor cost and freshness, and support your teams or operate the platform as a managed service.

    Deliverables: Cost and performance dashboards · Operations runbooks

Use Cases

Lakehouse Use Cases

Where a lakehouse delivers the clearest advantage.

Unified Analytics and ML Platform

Analysts query gold tables in SQL while data scientists train models on silver data in the same platform. Both use one set of definitions and permissions.

Streaming and Batch on the Same Tables

Event streams append to Delta or Iceberg tables in near real time while batch jobs correct and enrich history. Dashboards show fresh data without separate pipelines.

Regulatory Reporting with Time Travel

Finance and risk teams reproduce exactly what a table contained at quarter end. Audit questions are answered by querying a past version.

Customer Data Consolidation

Merge CRM, web events, transactions and support data into governed customer tables. Marketing segments and churn models draw from the same source.

Document and Unstructured Data for GenAI

Store PDFs, images and transcripts with metadata and permissions beside structured data. Retrieval pipelines index only content users are allowed to see.

Data Sharing with Partners

Share governed tables with subsidiaries or partners without exporting files. Recipients query live data under controlled permissions.

Technology

Lakehouse Technologies

Open formats, engines and catalogs we design with.

  • Databricks
  • Delta Lake
  • Apache Iceberg
  • Unity Catalog
  • Delta Live Tables
  • Microsoft Fabric OneLake
  • Snowflake Iceberg Tables
  • AWS Lake Formation
  • Amazon S3
  • Azure Data Lake Storage
  • Google Cloud Storage
  • Apache Spark
  • Trino
  • dbt
  • Microsoft Purview
  • Power BI

Platforms

Deliver Data Lakehouse 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.

+32%conversion rate

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 Story

More Success Stories

Explore All Success Stories

Resources

Featured Resources

Industry trends and practical guidance to help you get more value from data, AI and technology.

Build a Lakehouse That Serves Every Team

Tell us how your lake, warehouse and data science work today. We will reply within 24 hours and book a free consultation to outline a lakehouse blueprint for your organization.

Book a Free Consultation

Data Lakehouse FAQ

What is the difference between a data lake, a warehouse and a lakehouse?

A data lake stores raw files cheaply but offers weak guarantees for consistency and performance. A warehouse provides fast, reliable SQL on structured data but is less suited to unstructured data and machine learning. A lakehouse adds warehouse-style transactions, schema enforcement and indexing to open files in object storage, so one platform can serve both analytics and AI without copying data.

Should we choose Delta Lake or Apache Iceberg?

Both are mature open table formats with ACID transactions, schema evolution and time travel. Delta Lake is native to Databricks and Microsoft Fabric; Iceberg has broad support across Snowflake, AWS, Google Cloud and engines such as Trino. Interoperability features increasingly let one engine read the other. We recommend based on your primary platform, the engines you need to support and your catalog strategy.

What is the medallion architecture?

It is a layering pattern. Bronze holds raw data as received, kept for auditability and reprocessing. Silver holds cleaned, deduplicated and standardized data that conforms to agreed schemas. Gold holds business-ready data products, aggregates and dimensional models for reporting and applications. Quality checks between layers stop bad data from reaching users, and any gold table can be rebuilt from bronze.

Can a lakehouse replace our existing data warehouse?

Often, yes, particularly when SQL performance, BI connectivity and governance meet your requirements, which modern lakehouse engines usually do. Some organizations keep a warehouse for specific workloads and use the lakehouse for everything else. We benchmark your key queries and reports on the lakehouse before recommending consolidation, and migrate in validated waves.

How do you govern access in a lakehouse?

We use a unified catalog, such as Unity Catalog, Lake Formation or Purview, to manage permissions at catalog, schema, table, column and row level, with tags for sensitive data and dynamic masking. Access is granted to groups from your identity provider, every query is audited, and lineage shows how data flows to reports and models. Policies are defined as code where possible.

What does a lakehouse project cost?

Costs depend on the number of sources and domains, data volumes, migration scope, governance requirements and whether you need streaming. Platform consumption is separate and scales with use. We typically start with a blueprint and first data product, which gives a reliable basis for estimating the rest, and we design compute policies and table maintenance to keep running costs predictable.