You’ve picked Databricks, or you’re close. Now the question is who sets it up. Databricks is flexible, and that cuts both ways. The right partner leaves you with a governed lakehouse your team can run and afford. The wrong one leaves you with oversized clusters and pipelines nobody understands. Here’s what to check before you sign.

What should you look for in a Databricks partner?

1. Official Databricks partnership. Confirm the company is listed as a Databricks partner. It shows a working relationship with Databricks and access to training and support.

2. Certified engineers on your project. Ask how many of the engineers assigned to you hold Databricks certifications, not just how many exist in the company.

3. Relevant lakehouse projects. Look for projects in your industry or with your data sources, such as ERP, IoT or healthcare records.

4. Governance experience. Unity Catalog, access control, lineage and data quality make a platform trustworthy. Ask how they set them up.

5. Cloud depth. Databricks runs on Azure, AWS and Google Cloud. The partner should know networking, identity and security on your cloud.

6. Cost discipline. Ask how they track and reduce compute cost. A good partner treats cost as a design requirement from week one.

7. Knowledge transfer. Your team should be able to run and extend the platform. Ask how documentation, training and handover work.

Where Databricks projects actually get hard

For a human-services organization handling protected health information, the hard part wasn’t the pipelines. It was building an environment the security team would sign off. We followed Databricks’ Security Reference Architecture and built a fully private Azure setup: hub-and-spoke networking, private endpoints, Private DNS Zones and VPN-only access with no public exposure. Unity Catalog handled governance, lineage and role-based access.

Two things mattered as much as the architecture. Before the project, data scientists worked in local environments with no shared workspace, so collaboration was slow and error-prone. Now they work in one governed environment, with an MLOps pipeline for retraining and deploying models. We also trained the client’s teams on Databricks practices and secure data handling, so they can extend the platform themselves. Ask any partner how they’ll handle both.

What should the first 90 days look like?

A partner who knows Databricks well can describe the first three months in concrete terms. If the answer is vague, treat that as a signal. A typical sequence looks like this:

Regulated environments stretch the foundation phase, because private networking and security reviews take longer. That’s normal. A partner who promises production data in week two is skipping governance.

Questions to ask in the first meeting

• Can you show a lakehouse architecture you delivered for a similar company?

• How do you structure the medallion layers (bronze, silver, gold) and naming conventions?

• How do you set up Unity Catalog, workspaces and environments for development, test and production?

• How do you handle data quality testing and pipeline monitoring?

• What CI/CD approach do you use for notebooks, jobs and infrastructure?

• What did your last project cost to run per month, and how did you reduce it?

Red flags to watch for

• Only sales people in the meetings, and no access to the engineers.

• A full-platform fixed quote without any assessment of your data sources.

• No mention of governance, security or cost until you ask.

• Everything built in notebooks with no version control or deployment pipeline.

None of these on its own rules a partner out. Two or more together usually mean the platform will look fine in the demo and cost you later in rework, surprise compute bills or a handover that never really happens.

What does a Databricks engagement typically cost?

These are typical ranges, not quotes. Regulated data, many source systems and private networking push you toward the upper end. Rates and budgets vary by engagement, so we don’t publish a price list. We scope each project and give you a direct quote. For a first estimate, multiply team size by duration by your partner’s rate, and remember that Databricks and cloud compute come on top of the partner’s fees.

Ask how the budget splits between platform setup and the first use case. A partner that spends everything on infrastructure leaves you with a platform and nothing running on it.

If you’re still choosing a platform, compare Microsoft Fabric and Databricks first. If you’re moving off a legacy warehouse, work through our data platform migration checklist.

How RUBICON helps with Databricks platforms

We’re a Databricks Partner and a Microsoft Solutions Partner for Cloud & AI Platforms, certified to ISO 27001:2022 and ISO 9001:2015. We’ve built data platforms on Azure for chemical and consumer goods companies, and a HIPAA-aligned data intelligence platform on Azure and Databricks.

Our data engineering team usually starts with a short assessment of your sources and goals, which ends with a scoped estimate and a direct quote. If you’re comparing partners, we’re happy to walk through a past architecture with you.

Frequently asked questions