Delta Lake
The table format underneath the lakehouse — versioned, transactional files on object storage.
Organisations whose data currently lives as files nobody can reliably query twice.
We build and run on thisNotebooks are cheap. A governed lakehouse is the actual project.
We build and run Databricks workspaces — ingestion, Delta tables, the catalogue, the permissions and the serving layer — and can place committed spend through our partnership. It is a platform we work inside, not a licence we hand over and leave.
Not a boxed licence — we design and operate workloads here, and can place your committed spend through us.
What we work with
The products, and who each one suits.
6 listed
Unity Catalog
One place to declare what a table is, who may read it, and where it came from.
Anywhere more than one team touches the same data.
Databricks SQL
Warehouse-style SQL and dashboards over the same tables the pipelines write.
Analysts who should not need to think about clusters to answer a question.
Workflows and declarative pipelines
Scheduling and pipeline definitions, so ingestion is a defined job rather than a notebook someone remembers to run.
Teams whose data arrives because a particular person is at their desk.
Mosaic AI model serving
Fine-tuning and serving models next to the data they were trained on.
Teams that want the model and the feature tables under one set of permissions.
MLflow
Experiment tracking and a model registry, open source and built into the workspace.
Anyone who has to say which model version produced which answer.
What buying through us changes
A partnership is only worth what it changes.
Better than list on committed consumption
Databricks is bought in committed units of consumption, and a commitment prices below the public rate. We place that through our partnership and size it against measured usage rather than a forecast made in a good mood.
Named contacts inside the vendor
A route to solutions architects and account engineers when a job behaves in a way the documentation does not explain — rather than a general support queue.
Startup credits we sponsor
Databricks runs credit and programme support for early-stage companies. We prepare and sponsor the application; whether it is awarded is Databricks’ decision.
How we help
The work around the product.
- Assess whether you need a lakehouse at all, or whether a warehouse and dbt would do the same job for less money.
- Size the commitment against measured usage, then re-size it once there is a quarter of real consumption to look at.
- Migrate what you already have — Hadoop, an existing warehouse, a folder of Spark jobs — into Delta tables with the old system running until the new one is proven.
- Set up Unity Catalog deliberately: workspaces, permissions, lineage, and the data nobody should be able to read.
- Build the pipelines and the serving layer with tests and monitoring, so it survives the person who wrote it leaving.
- Hold the bill down — cluster policies, autoscaling, job clusters instead of all-purpose ones — and report monthly on what changed.
Where this is not the answer
Databricks is expensive for small data. If the whole estate fits comfortably in one Postgres instance, we will tell you to keep it there and spend the money elsewhere. We build and operate — we do not deliver Databricks certification training, and we do not issue anyone’s credentials. Where a problem is genuinely the platform’s, we escalate it to Databricks rather than bill you to work around it.
Next step
Buying Databricks? Talk to us first.
Tell us what you are trying to do and we will come back with the sizing, the licensing and the price — from a partner, within two working days.