Platforms · Databricks · Platform we build on
A lakehouse that carries the work, not another rebuild.
Databricks is one of the platforms we build on. We diagnose your estate first, keep what works, and build a governed foundation on Databricks that can carry reporting, machine learning and generative AI, with a pattern your own team runs afterwards.
Where you might be
Four places a Databricks engagement starts.
Choosing
You are deciding on a platform
The decision is made on the diagnostic: your cloud footprint, your workloads and your team. If Fabric or Snowflake fits better, we say so.
Half-migrated
Part of the estate moved, the rest is stuck
We do not restart the migration. We find the missing pattern, build it once, and let what is stuck follow it.
Not trusted
The platform exists but nobody builds on it
We trace the numbers from source to dashboard, find where they break, and keep the rest.
Not ready for AI
It serves reporting but cannot feed a model
We modernise the layer that is the constraint and carry forward the models, history and access rules that are not.
What we build on Databricks
A foundation your team can run.
Delta Lake, Unity Catalog, Databricks SQL and the platform's pipeline and model tooling are Databricks products. We design and build with them in your own Databricks workspace and cloud account.
Storage and compute
Lakehouse architecture
Delta Lake tables organised into a structure the business can find its way around, with security, governance and cost controls designed in from the start.
Ingest and transform
Batch and streaming pipelines
Pipelines that alert when they fail, built as the pattern every later pipeline follows.
Govern
Access, lineage and ownership
An access and governance model in Unity Catalog, with lineage so a number can be traced back to its source, and a named owner for each data product.
Analyse
Governed metrics for BI
A semantic layer and SQL warehouse your dashboards can trust, whether the reporting tool is Power BI, Tableau or Databricks' own.
Predict and generate
Machine learning and generative AI
Models and retrieval systems built on the same governed data, with evaluation, monitoring and a cost model before production.
Run
Unit economics and operability
Cost per run and per query made visible, and a runbook your team follows to add the next source without us.
Keep, fix or build
Seven tests on every component.
Anyone can label a broken thing "fix". The judgment is whether the warehouse is actually the problem. Each component is tested before anything is replaced:
- Reliability: does it fail, and can anyone tell when it does?
- Ownership: who is accountable for it, and do they know?
- Lineage: can a number be traced back to its source?
- Performance and scale: can it carry the workload at the volume planned?
- Security and access: can the right people and systems reach it, and only them?
- Unit economics: what does it cost per run, per query, per terabyte?
- Operability: can your team run it and extend it without us?
Related practices
The practices behind this work.
Ways in
Start with a fixed scope.
Starter service · 2–4 weeks · fixed fee
Data Platform Accelerator
One target platform, up to six source systems assessed, two representative pipelines, one governance pattern with lineage, and a runbook.
See the starter service →Starter service · 2–4 weeks
AI & Agent Pilot
One defined use case taken to a running system on your data, then measured before you scale it.
See the starter service →Free assessment
Data Maturity Assessment
Six to seven minutes, no email required, to see where your foundation breaks.
Take the assessment →