Setting up Azure Databricks without regretting it later
A Databricks workspace takes an afternoon to create. The decisions around it, such as how catalogs map to teams and environments, how identities flow in from Entra ID and how the network is locked down, take longer and are expensive to change once data and people depend on them. We help you get those right in the first weeks.
When you need this
- You are starting on Azure Databricks and want a platform, not a collection of notebooks
- You run older workspaces with the Hive metastore and need to move to Unity Catalog
- Security or audit has asked how data access is controlled, and the honest answer is "it depends on the workspace"
- Several teams want to use Databricks and you need one setup that works for all of them
How we work
We start with a short design phase: your environments, teams, source systems and compliance requirements. From that we agree on a target setup and deploy it with infrastructure as code, so it is repeatable and reviewable. Your team builds alongside us from day one, so they can extend the platform without us.
What we see go wrong
Catalogs that mirror the org chart.
Reorganizations happen more often than data moves. Catalogs that follow environments and data domains age better.
Permissions granted to people instead of groups.
It works for ten users and becomes unauditable at a hundred. Groups synced from Entra ID, with automation for the rest, keep access reviewable.
Networking left for later.
Private endpoints and VNet injection are much easier to set up before data lands than to retrofit.
Clicked-together workspaces.
Without Terraform or bundles, dev, test and prod drift apart within months.
What you get
- A documented target architecture for workspaces, catalogs, networking and identities
- Infrastructure as code for the platform, in your repo and your pipelines
- Unity Catalog with a group and permission model your security team can review
- A first pipeline running end to end, as a template for the next ones
- A handover your own engineers can work from
Other stages
02 · Grow
Pipelines that hold up in production
Blueprints for robust, scalable ingestion, new sources added as you need them, and data products that turn new data ideas into something people use.
More about Grow03 · Run
Lean operations, continuous value
Adopt new Databricks features with intent, fix data quality and pipeline issues fast, and keep the platform current without a big ops team.
More about Run
Let's talk about your lakehouse.
Thirty minutes, no slides. Bring the question that keeps coming back.