Platform

Databricks Consulting

Use Databricks when it is the right tool to organize data for reporting and machine learning, so the outcome is trusted numbers, not a platform tour.

Platform names describe tools we work with. Not a partnership or certification list.

What we typically cover

Organized data your reports and models can share
Refresh and checks you can see
Problems held back from published numbers
Handoff for analytics, with optional ongoing care

Problems this addresses

Databricks is the right tool when the people who prepare numbers and the people who build models need the same tables, with a catalog that says who owns them. It is the wrong tool when the only pain is a weekly finance workbook and a simpler report would do.

We start from the decision that is stuck: a revenue total, a forecast input, a retention view. Unity Catalog, Delta tables, and SQL warehouses matter because they keep that number in one place analysts can query and a model can reuse. They are not a tour of the product.

Finance and the model team each keep a copy

Revenue is exported for a notebook, then exported again for a board pack. The two files diverge and the meeting argues about the file, not the business. A shared table with a named owner stops that, if the definition of revenue is agreed first.

A failed job still publishes a number

If a refresh fails and the report still looks finished, finance will use it. Jobs need a visible success or failure before the executive figure updates. The operating question is who gets told, and whether the old number stays up with a clear stamp.

Analysts cannot see the table the model uses

A SQL warehouse is how finance and revenue analysts inspect the same rows a model was trained or scored on. If they need an engineer to export a file, the check never happens on the morning it matters.

The platform is ahead of the decision

Teams sometimes adopt Databricks because a program said so, then look for a use. We will say when Power BI or Fabric is the shorter path to a trusted pack, and when Databricks is already the place your data and models live.

Hypothetical example. A RevOps lead and a data scientist both report pipeline coverage. In this hypothetical, one file drops stalled opportunities and the other keeps them. The Databricks work starts by writing the inclusion rule, putting it on one table, and showing both old totals beside it for a single week. It does not start by moving every historical notebook.

What you leave with

You leave with organized tables your reports and models can share, a refresh you can see, and a handover your analysts can run. Optional ongoing care sits with Managed Data Services if you want it.

Tables with an owner and a definition

The figures in scope, usually finance and revenue first, live as Delta tables with the inclusion rules written down. Unity Catalog is how we keep the name, the owner, and the permission together.

A SQL path for the people who check the number

Analysts get a warehouse or SQL access so they can trace a total without waiting on a notebook export. The point is the check, not a new query tool for its own sake.

A job that fails in the open

Scheduled refresh for the tables in scope, with a status a business owner can understand. A miss does not silently become the number in the deck.

A boundary around machine learning

Models are in scope only when a real decision needs one. Tracking sits with the model when we build it. We do not add a model to justify the platform.

What a Free Assessment covers

A Free Assessment looks at the number you do not trust, where it is calculated today, and whether Databricks is already the system those tables should live in. We name the first table, who owns the definition, and what can stay in a report tool.

If Databricks is not the shortest path, we say so. Bring one disputed total and the two files that disagree. We follow up within 2 business days.

Request a Free Assessment

FAQs

Is Databricks required for Power BI?

No. Many Power BI models are served without Databricks. Use Databricks when shared tables, a catalog, and models need the same data. Use Power BI alone when the pain is the report and the model behind it.

Which cloud do you work in?

The one you already have. Databricks runs on Azure, AWS, and Google Cloud. We work in your account. We do not ask you to move clouds so the logo matches a diagram.

Will this replace our warehouse?

Only if that warehouse is why the numbers cannot be trusted, and only for the workload we scope. A first delivery is usually a small set of finance or revenue tables, not a rewrite of every subject area.

Do you sell Databricks?

No. Your subscription stays yours. We implement the tables, access, refresh, and handover in scope. Naming the product is not a claim of a special commercial relationship.