Service

Infrastructure

Data Architecture.

Data without structure is noise. With the right architecture, it becomes your company's most valuable asset. The difference between the two situations isn't technological — it's a matter of design.

What is data architecture

Data architecture is the set of design decisions that determine how data is stored, moved, transformed and consumed within an organisation. Poor design causes analytics projects to fail not for lack of data, but because that data isn't available in the right format, quality or timing.

Data Warehouse & Data Lake

Design and construction of a central data repository adapted to the volume, velocity and variety of your information.

ETL / ELT Pipelines

Automation of data flow from sources to destination, with transformations, validations and error handling.

Data Governance

Defining owners, quality policies, data dictionary and lineage. The right data, for the right person, at the right time.

Cloud Migration

Moving on-premise infrastructure to cloud platforms (AWS, GCP, Azure) with minimal operational disruption.

How we do it

01

Current state audit

We map existing data sources, current flows, systems involved and friction points. We don't design from scratch without understanding what exists.

02

Architecture design

We propose the target architecture: which technologies, which integration patterns and which data model best suits your needs and your team.

03

Implementation and migration

We build the infrastructure, migrate historical data and launch pipelines with quality testing and monitoring in place.

04

Documentation and handover

We deliver complete documentation and train your team so they can operate and extend the architecture independently.

Who this service is for

Companies with fragmented data

Organisations where data lives in silos — CRM, ERP, spreadsheets, local databases — with no unified flow.

Companies in growth phase

Businesses that have outgrown their current infrastructure and need to scale without losing data lineage or quality.

Teams wanting advanced analytics

Companies that want predictive models or powerful dashboards but whose current data infrastructure doesn't support it.

Companies migrating to the cloud

Organisations wanting to move their data systems to AWS, GCP or Azure in an organised, low-risk way.

Frequently asked questions

Do I need an internal data engineering team to work with you?

It isn't essential. We work with both companies that have internal technical teams and companies without data profiles. In the latter case, we design architectures that can be operated by non-specialist profiles and document everything so the learning curve is minimal.

How long does it take to get a data architecture operational?

It depends on complexity and the starting point. A mid-scope architecture project — five to ten data sources, dimensional model, automated pipelines — is typically operational in eight to twelve weeks. More complex projects including cloud migration may require three to six months.

What technologies do you use?

We have no vendor preference. We recommend the technology that best fits your needs, budget and team: BigQuery, Snowflake, Redshift, dbt, Airflow, Fivetran, Stitch, among others. The criterion is always the problem to solve, not a favourite tool.

Can you work on an existing architecture we already have?

Yes. We start with a current state audit to understand what works, what doesn't and what needs to change. We don't start from scratch unless necessary. The goal is to improve what already exists incrementally, not tear it down and begin again.

Want to know if your data architecture is fit for purpose?

We audit your current infrastructure at no commitment and tell you what we'd change and why.

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