Modern Data Platforms
Lakehouse architecture on open formats, with governance, cataloguing and cost controls in place from the first table.
Talk this throughData platforms
Foundations sized for what you actually have
A lakehouse built for a hundred-person company should not look like one built for a bank. We right-size the platform, then make the migration boring.
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Delivery leads online — talk to a human, not a bot3Clouds: AWS, Azure, GCP
0Big-bang cutovers
OpenTable formats throughout
Reference platformOpen formats throughout
100% delivery success — delivered worldwide across seven regions
AWS
Microsoft Azure
Google Cloud
Databricks
Snowflake
dbt
Power BI
Looker Studio
Azure Synapse
MongoDB
Python
LangChain
AWS
Microsoft Azure
Google Cloud
Databricks
Snowflake
dbt
Power BI
Looker Studio
Azure Synapse
MongoDB
Python
LangChain
What you get
Modernisation without a rewrite year
Legacy platforms rarely get replaced in one move, and the attempts that try usually stall. We run the old and the new in parallel, migrate by domain, and prove each cut before the next one.
- Open table formats so you are never locked to one engine
- Domain-by-domain migration with reconciliation at every step
- Cost model built before the build, tracked after it
- Infrastructure as code, environments reproducible from scratch
- Access, retention and residency designed in, not retrofitted
Landing to servingContracted at every hop
Capabilities
Platform work we take on
On AWS, Azure or Google Cloud, with the tooling you can staff for.
Data Warehousing
Snowflake, BigQuery, Redshift or Fabric — modelled properly, with warehouse sizing and query tuning that keeps the bill honest.
Talk this throughData Lakes
Cheap durable storage with catalogue and access control, so raw data is an asset rather than an unindexed liability.
Talk this throughData Modernisation
Getting off legacy stacks by domain, in parallel, with reconciliation — never a single weekend cutover.
Talk this throughCloud Services
Landing zones, networking, IAM and infrastructure as code, delivered by engineers who will still be on the call during cutover.
Talk this throughBy the numbers
Platform work in numbers
Cost is a design constraint here, not something discovered on the first invoice.
0Clouds we deliver on: AWS, Azure and Google Cloud
0+Sources consolidated onto a single platform
0%Infrastructure reproducible from code
0Big-bang migrations — we move domain by domain
Delivery model
How the work actually runs
One delivery model, whatever the engagement. You always know what is shipping this week and what it is worth.
01
Discovery
We map your sources, decisions and blockers, understand the business goal, and agree what success looks like before anything is built.
02
Solution design
We design the architecture, data models and use cases — right-sized to your team and your cloud — and cost the work before you commit.
03
Development
Weekly sprints against a visible backlog: pipelines, models, dashboards and applications, all in version control and yours from day one.
04
Testing & validation
Every pipeline and model is tested — with frameworks like PyTest and Great Expectations — so the numbers are trustworthy before they ship.
05
Deployment & training
We deploy into your cloud and train the people who will run it, so the capability lands with your team, not just our code.
06
Support & optimisation
Observability, alerting and continuous improvement keep the system fast, accurate and cost-efficient long after go-live.
Proof
Recent work
Marketing · EMEA
Marketing and CRM data unified across 20+ EMEA countries
A digital-transformation programme that consolidated marketing and CRM data across the region on Azure and Power BI, with a custom lead-management automation built on Power & Logic Apps.
80%Manual workload cut
20+EMEA countries
FMCG · Nestlé
Workforce analytics for Nestlé’s supply & demand planning
HR data marts on SAP feeding automated daily ingestions through AWS Glue and Databricks, on a medallion architecture, so planners could see available resource against production targets.
DailyAutomated ingestions
MedallionData architecture
Data strategy · Germany
One source of truth from 50+ scattered sources
Data strategy and cloud architecture on Google Cloud, ingesting 50+ sources through Stitch, modelled in dbt and surfaced to the CEO and Head of Product in Looker Studio.
50+Sources unified
ExecLive dashboards
FAQ
Questions buyers actually ask
Straight answers. If yours is not here, ask us on a call — we will tell you if we are the wrong fit.
Ask a questionIt depends on the shape of your workload and who you can hire. If most of your work is SQL and BI, one answer tends to win; if it is heavy on ML and unstructured data, the other does. We will show you the cost model for both.
Yes, and sometimes you should — data residency, existing hardware and predictable workloads all argue for it. We deliver hybrid architectures and will say so when the cloud case is weak.
Domain by domain. A first domain usually lands in six to ten weeks; a full estate is measured in quarters. You get value from the first domain rather than at the end.
Access control, retention, lineage and audit are part of the build, not a later phase. We work to recognised security and data-governance practice and will map to your specific regime.
Next step
Ready to find out what your data is worth?
Thirty minutes with a delivery lead, not a salesperson. You leave with a point of view on your highest-value use case and what it takes to ship it.