Foundations · Service 08

Modern Data Architecture.

Lakehouse, event-driven, data mesh — secure, flexible architectures that serve as the foundation for data and analytics today and as you grow.

20+
Reference architectures
5
Patterns supported
6–10 wks
Target-state design
Cloud-agnostic
Vendor stance
Overview

Architecture is a decision record, not a diagram.

The best architectures are traceable: every component choice tied to a specific requirement, a specific trade-off, a specific owner. We work that way — ADRs you can point at in two years and understand why.

We cover cloud, on-premises and hybrid — including regulated environments (public sector, financial services, housing) where sovereignty matters.

Patterns we apply.

01

Lakehouse

Medallion architecture on Databricks / Fabric / open-format — the default for most new builds.

02

Event-driven & streaming

Kafka / Confluent / Kinesis / Event Hubs — for real-time, CDC, product telemetry.

03

Data mesh

Domain-owned data products, federated governance. For large, heterogeneous orgs where central teams bottleneck.

04

Hub-and-spoke warehouse

Where conformity and finance-grade governance outweigh domain autonomy.

05

On-prem / hybrid

Where regulation or sovereignty demands it. We've delivered full on-prem Databricks and open-format stacks.

06

AI / agent architecture

Retrieval, model hosting, observability, guardrails — as first-class architecture concerns.

How we work

Structured design, reviewable decisions.

1
Weeks 1–2

Requirements

Functional, NFR, regulatory, commercial constraints surfaced and weighted.

2
Weeks 3–5

Options & trade-offs

2–3 candidate architectures with ADRs, cost models, risk profile.

3
Weeks 6–8

Target-state

Final architecture, migration path, standards, patterns library.

4
Ongoing

Stewardship

Architecture council, pattern updates, exception management.

Deliverables

  • Target-state architectureDiagrams, component rationale, NFR evidence.
  • Architecture Decision RecordsTraceable, reviewable, durable.
  • Reference implementationsWorking templates, not PowerPoint.
  • Standards & patternsNaming, interfaces, quality gates, governance hooks.
  • Migration pathFrom current to target with sequenced interim states.
  • Architecture council charterGovernance for architecture change.
Technology

Tools & frameworks we use.

Databricks
Snowflake
Microsoft Fabric
Delta Lake
Apache Iceberg
Kafka
Confluent
Kinesis
Event Hubs
Airflow
dbt
Terraform
C4 Model
ArchiMate
In production

A real engagement.

Case study

Accounting network — data mesh across 12 firms.

A federated data mesh gave each member firm domain autonomy while surfacing shared metrics to network leadership. Onboarding a new firm now takes 4 weeks, down from 6 months.

Read full case study
12
Member firms
6mo → 4wk
Firm onboarding
38
Data products
100%
Governance coverage
FAQ

Common questions.

Lakehouse or data mesh?+
Not mutually exclusive. Most clients run a lakehouse platform organised along mesh principles. Pure mesh is rare and hard.
Do we need to re-architect to adopt AI?+
Usually no — retrofit retrieval, observability and governance onto what you have. Full re-architecture only if the foundations aren't there.
How do you handle on-prem constraints?+
Open formats (Iceberg, Delta), open engines (Databricks on-prem, Trino, Spark), self-hosted identity and observability. Same architecture, different deployment target.
What about legacy systems?+
Strangler patterns, CDC-based integration, parallel-run. Legacy exits when it's unused, not when we declare it dead.
Ready when you are

Put your data to work.

Book a free 30-minute consultation with a senior Databuzz consultant.