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Data Engineering

Data Mesh Architecture

Scale data across a growing organization by giving domains ownership of their data products, backed by a shared self-serve platform and governance that is automated rather than bureaucratic.

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Scale Data Ownership as Fast as Your Business Grows

Centralized data teams often become a bottleneck. Every new report, model or source joins a queue, and the central team must understand the business logic of sales, supply chain, finance and product alike. Data mesh, a sociotechnical approach described by Zhamak Dehghani, addresses this with four principles: domain-oriented ownership, data as a product, a self-serve data platform and federated computational governance.

Hanoi Technology helps organizations decide whether data mesh fits, and implements it pragmatically when it does. We define domains and data product standards, build the self-serve platform capabilities that make domain teams productive, and automate governance policies so that decentralization does not mean chaos.

The Challenge

Signs Your Central Data Model Has Hit Its Limits

These pressures typically lead organizations to consider data mesh.

The Central Team Is a Bottleneck

Requests from every department compete for the same small team. Lead times for new data stretch to months, and business units start building shadow solutions.

Domain Knowledge Gets Lost in Translation

Central engineers model data they do not use daily. Business rules are misunderstood, and fixes bounce between teams.

Unclear Ownership of Data Quality

When a number is wrong, nobody owns the fix end to end. Producers blame pipelines, pipelines blame sources and users lose trust.

Decentralization Without Standards

Some organizations already let teams build their own pipelines, but without shared tooling or rules. The result is duplicated effort, inconsistent security and incompatible data.

Business Value

What Data Mesh Can Deliver When Done Well

Faster Delivery of Domain Data

Teams closest to the data publish it themselves, without waiting for a central queue. New data products reach consumers in weeks.

Higher Data Quality at the Source

Domain owners are accountable for their data products’ quality, documentation and service levels. Problems are fixed where they originate.

Discoverable, Reusable Data Products

A catalog lists data products with owners, schemas, freshness and usage terms. Consumers find and reuse data instead of rebuilding it.

Governance That Scales Automatically

Security, privacy, interoperability and quality policies are enforced by the platform as code, so compliance does not depend on manual reviews.

Lower Cost of Duplicate Effort

Shared platform capabilities for ingestion, storage, orchestration and access remove the need for each team to build its own stack.

Clear Accountability

Every data product has a named owner, a contract and measurable service levels, making it obvious who to contact and what to expect.

Data Mesh Is an Operating Model First

Data mesh is not a product you can install. Technology enables it, but success depends on organizational choices: which domains own which data, what a data product must provide, how platform and domain teams collaborate, and which policies are global. We start with those choices, pilot with one or two willing domains, and grow from proven results.

Our Services

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

Services that cover the organizational and technical sides of data mesh.

Data Mesh Readiness Assessment

We assess your organization’s size, domain structure, data maturity and pain points to decide whether a full mesh, a hybrid model or a stronger central platform is the right answer.

  • Maturity and readiness assessment
  • Fit analysis and recommendation
  • High-level adoption roadmap

Domain and Data Product Design

We work with business and technical leaders to define domains, identify candidate data products and specify what every product must include, from schema to service levels.

  • Domain map
  • Data product canvas and standards
  • Prioritized product backlog

Self-Serve Data Platform Engineering

We build platform capabilities, such as templates for ingestion, storage, transformation, testing, publishing and access, so domain teams can create products without deep infrastructure skills.

  • Platform capability roadmap
  • Data product templates
  • Infrastructure as code and CI/CD

Federated Computational Governance

We help set up a governance group with domain representatives and translate its policies, such as classification, retention, naming and interoperability, into automated checks.

  • Governance charter
  • Policy as code
  • Automated compliance checks

Data Product Catalog and Contracts

We implement a catalog where data products are registered with owners, documentation, lineage, quality metrics and contracts that consumers can rely on.

  • Catalog implementation
  • Data contract specification
  • Quality and usage dashboards

Domain Team Enablement

We coach domain teams on building and running data products, embed engineers during early releases and create playbooks so new domains can onboard independently.

  • Training and pairing
  • Onboarding playbook
  • Community of practice setup

Our Approach

Our Pragmatic Path to Data Mesh

Start small, prove value and scale what works.

  1. 01

    Assess Fit and Goals

    We confirm whether the problems you face are ones data mesh solves, and define measurable goals such as lead time for new data products or quality incidents.

    Deliverables: Readiness assessment · Goal and metrics definition

  2. 02

    Design Domains and Standards

    We map domains, define data product standards and decide which governance policies are global and which are local.

    Deliverables: Domain map · Data product standard · Governance model

  3. 03

    Build the Minimum Viable Platform

    We deliver the essential self-serve capabilities needed by the first domains, rather than a complete platform up front.

    Deliverables: Platform MVP · Templates and documentation

  4. 04

    Pilot with Lighthouse Domains

    One or two motivated domains publish their first data products with our engineers alongside them, and consumers start using them.

    Deliverables: First data products live · Lessons learned and refinements

  5. 05

    Scale and Federate

    We onboard further domains, automate more governance and hand ownership of platform and standards to your teams.

    Deliverables: Onboarding waves · Automated governance · Operating model handover

Use Cases

Where Data Mesh Fits

Organizational situations in which the approach tends to pay off.

Multi-Brand Retail Groups

Each brand owns its sales, inventory and customer data products on a shared platform. Group finance combines them through standard contracts.

Financial Services with Many Business Lines

Lending, payments and wealth teams publish governed data products. Risk and compliance consume them with consistent definitions and lineage.

Platform and Marketplace Companies

Product teams that own services also own the data products those services generate. Analytics stays aligned with fast-moving product changes.

Post-Merger Data Landscapes

Acquired companies keep their systems but publish standardized data products. Group reporting works without forcing an immediate full migration.

Manufacturing and Supply Chain Networks

Plants, procurement and logistics own operational data products. Planning teams combine them for end-to-end supply chain visibility.

Organizations Scaling AI

Domain teams publish feature-ready data products with clear semantics. Data science teams build models faster on trusted inputs.

Technology

Technologies for a Self-Serve Data Platform

Data mesh is technology-agnostic; these are tools we commonly combine.

  • Databricks Unity Catalog
  • Snowflake
  • Microsoft Fabric
  • Google BigQuery
  • dbt
  • Apache Kafka
  • DataHub
  • Collibra
  • Microsoft Purview
  • Atlan
  • Terraform
  • Apache Airflow
  • Dagster
  • Great Expectations
  • Open Policy Agent
  • Delta Sharing

Platforms

Deliver Data Mesh 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.

+162%monthly active users

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 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.

Find Out Whether Data Mesh Is Right for You

Tell us how your data teams are organized and where delivery slows down. We will reply within 24 hours and book a free consultation to assess fit and outline a pilot.

Book a Free Consultation

Data Mesh FAQ

Is data mesh right for every organization?

No. Data mesh tends to pay off in larger organizations with several distinct business domains, a central data team that has become a bottleneck and domain teams willing to own data. Smaller companies or those with one dominant domain often get more value from a well-run central platform with clear ownership. Our readiness assessment gives you an honest recommendation, including hybrid options.

What exactly is a data product?

A data product is a dataset, or a set of related datasets, published by a domain for others to use, managed with a product mindset. It has a named owner, documented schema and meaning, quality checks, defined freshness and availability, access policies and a way for consumers to discover it. It is versioned and changes are communicated, just like an API.

Does data mesh mean every team builds its own platform?

No, quite the opposite. A central platform team provides shared, self-serve capabilities such as storage, compute, pipelines, catalogs, access control and monitoring. Domain teams use those capabilities to build their data products without managing infrastructure. This avoids duplicated effort and keeps security and interoperability consistent across the organization.

How is governance handled in a data mesh?

Through federated computational governance. A group with representatives from domains, the platform team, security and legal agrees on global policies such as classification, privacy, naming, interoperability and quality thresholds. Those policies are then implemented as automated checks in the platform, so compliance is verified continuously rather than through manual approval meetings.

How long does a data mesh adoption take?

Data mesh is a multi-year evolution, but value should appear early. A readiness assessment and domain design typically take a few weeks, a minimum viable platform and the first lighthouse domain a few months. Further domains then onboard in waves. We set measurable goals at the start, and report progress against them in weekly written updates and regular demos.

Can we implement data mesh on our existing platform?

Usually, yes. Data mesh does not require a specific product. Lakehouse and warehouse platforms such as Databricks, Snowflake, BigQuery or Microsoft Fabric can support domain workspaces, data sharing, unified catalogs and fine-grained access. We assess what your current platform supports and fill gaps with catalog, contract and policy tooling where needed.