Databricks consulting services
Databricks
We work on Databricks administration and the platform control plane: accounts, workspaces, Unity Catalog, identity, and the delivery paths that change them. The service is cloud-neutral. Current delivery experience and published proof are deepest on Azure.
Work with 536 Technologies from the live platform through handoff.
Where platform work gets stuck
The platform works. Changing it is the problem.
The platform team becomes the queue
Workspace, catalog, schema, identity, and permission requests collect in tickets that only a few administrators can move forward.
Governance is difficult to repeat
Access decisions and platform configuration exist in the live environment, but their intent, ownership, and review history are difficult to see together.
Departments do not have a clear path
Teams can use Databricks, but the route for requesting access, creating governed data assets, and shipping changes is unclear.
How we work
Put the right platform changes on a path your team can own.
We document the decisions, ownership, and controls your team needs to operate the platform.
- 01
Assess the live platform
Inventory the accounts, workspaces, Unity Catalog, identities, and permissions in use, then measure them against the Well-Architected pillars.
- 02
Ownership and guardrails
Define who can request, review, approve, and release each platform change.
- 03
Automation and release
Import the configuration that belongs in Terraform, define the Terraform and cloud-provider boundary, and release through CI/CD the platform team owns.
- 04
Team enablement
Give departments standards, examples, and a documented way to work with the platform.
What your team keeps
Leave your team a platform they can own.
We work in the live delivery flow and leave a path the platform team can run.
- A prioritized Well-Architected finding list for the existing platform.
- A clear operating model for workspaces, Unity Catalog, identities, and permissions.
- Version-controlled automation for the Databricks configuration that belongs in code.
- Standards, examples, and runbooks the data platform team can operate without us.
Common questions
Before we start.
Does every Databricks object belong in Terraform?
No. We codify configuration when version control and review improve the operating model. Native Databricks controls remain in place where they are the better control surface.
Can you start with an existing workspace?
Yes. The work starts from the live environment. We inventory and import the resources in scope before proposing a future-state model.
Do you work beyond the Databricks workspace?
Yes. On Azure, production Databricks depends on networking, identity, security, observability, and deployment controls. We work across those boundaries when they are part of the bottleneck.
Do you work on AWS or Google Cloud Databricks?
The method is cloud-neutral, and current delivery experience and published proof are deepest on Azure. We take qualified AWS and Google Cloud conversations when the platform problem fits.
A scoped way to begin
Databricks Well-Architected Assessment
Review an existing Databricks platform against the seven Well-Architected pillars: operational excellence; security, privacy, and compliance; reliability; performance efficiency; cost optimization; data and AI governance; and interoperability and usability. The findings become a prioritized remediation and implementation plan.