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Services · Enterprise data architecture

A data architecture your teams can actually build on.

Target architecture and engineering standards for platforms that serve analytics, experimentation and AI, with every decision written down so several teams can work from it.

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Four layers, one set of rules
Sourcesevents,files, APIs1Ingestbatch andstreaming2Storewarehouse,lakehouse3ServeBI, ML,APIs4write-back to operational systemsContractsLineageOwnershipAccessPolicies flow down, lineage flows up. Every layer has an owner.
15architecture decisions for one iGaming data group
6areas, from the warehouse to governed AI
Ongoingengagement, running since 2024
Technologies behind this work
Platforms
Apache Kafka
Apache Airflow
ClickHouse
Apache Iceberg
PostgreSQL
Snowflake
BigQuery
Databricks
Trino
Apache Spark
Apache Flink
Apache Hive
Governance & delivery
dbt
Looker
Metabase
Apache Superset
Grafana
OpenTelemetry
Terraform
Kubernetes
Docker
Python
GitHub Actions
Datadog
What is included

Three areas, from decision to roadmap.

The output is a set of decisions people can act on, not a diagram.

Decide

Choose with the trade-offs on paper.
  • Target architecture and platform choices
  • Architecture decision records
  • Build-versus-buy and vendor evaluation

Standardize

One set of rules for every team.
  • Data contracts and schema versioning
  • Governance, lineage and ownership
  • Security and access controls

Make it deliverable

Turn the design into work.
  • Roadmaps and acceptance criteria per team
  • Provider and source onboarding plan
  • Governed integration of LLMs
Typical situations

When teams come to us.

Alignment

Product, data, engineering and security all want different things

We turn the cross-team constraints into shared decisions, so the teams stop re-arguing the same questions.

Migration

A legacy warehouse has to move without losing history

We plan the historical migration, the cut-over and the checks that prove the numbers still match.

AI readiness

Leadership wants AI on top of the data, and nobody trusts the data yet

We put governance, lineage and access control in place first, then connect the models to governed data.

What you get

Deliverables you will keep.

Concrete artifacts, documented and handed over.

A target architecture for the platform
Architecture decision records for the key choices
Engineering standards and data contracts
A roadmap with acceptance criteria per team
A governance and access model
A plan for onboarding sources and providers
How to start

Three ways to begin.

Larger work starts with a short paid discovery.

All offers
Discovery

Short paid discovery

Map the system, the risks and the constraints. Ends with a clear plan, not a strategy deck.

A few working days to a few weeks
Engagement

AI / Data / Cloud Modernization Engagement

Modernize workflows, data, AI systems and cloud architecture.

Custom scope
Engagement

Product Engineering Support

Senior engineers across frontend, backend, cloud, QA and AI/data, month by month.

Monthly
Proof

Work you can open and check.

All case studies
15
decisions, written down
iGaming enterprise data platform
Cross-team constraints turned into implementable roadmaps and acceptance criteria across warehouse, clickstream, experimentation and governed AI.
1+ TB
per day, in real time
Urban-Intel data platform
The scale case: a platform processing more than a terabyte of data every day.
Where this ends

Not quite what you need?

Ready to build the pipelines?Data engineeringRebuilding an existing system?Application & legacy modernizationNeed a second opinion first?Consulting & product design
FAQ

Questions about enterprise data architecture.

Do you also build the platform?
Yes, or we hand the architecture to your team. Many engagements start with the architecture and continue with the build.
Can you work under NDA?
Yes. Our largest data-architecture case is published anonymously because the client asked for it.
How long does it take?
A first architecture and decision set usually fits a two-to-six-week engagement. Larger platforms are scoped custom after a short discovery.

Tell us what the platform has to serve.

Describe the teams, the data and the decisions that are stuck. We reply with a clear next step, usually within 24 hours.

Discuss your projectBook a routing call