Databricks Lakehouse Implementation

Problems this addresses

A Databricks lakehouse implementation is the shared foundation for the reports and models you actually run. Finance and revenue should be able to trace a total to a table with an owner. A model should reuse that table instead of a private extract.

We connect the systems you have, check the figures before they are treated as official, and hand the result to your team. The point is trusted numbers, not a platform tour.

Each team has a private extract of revenue

The board pack, the forecast model, and a notebook each start from a different file. A lakehouse is justified when those consumers share one table and one definition. It is not justified by a diagram.

Nobody can tell whether the table is current

A stale table that still looks complete will be used in the meeting. The job that loads it needs a status a business owner can see, and a rule for what the report shows when the load fails.

Permissions are an afterthought

A wide-open schema means a region can see another region's pipeline, or a model can train on rows it should not. Unity Catalog is how we keep owner, permission, and table together for the figures in scope.

The first release tries to cover every subject

A lakehouse that starts with every source usually finishes none of them. We start with the finance or revenue decision that is stuck and add subjects only when that decision is trusted.

Hypothetical example. Recognized revenue is built in a notebook owned by one analyst and also in a finance workbook. In this hypothetical, credits are excluded in only one of them. The implementation puts one revenue table in Unity Catalog, documents the credit rule, and shows both old totals for a single month before any model is pointed at it.

What you leave with

You leave with the tables in scope, a refresh you can see, checks against the source, and a short operating guide. Reporting and models come next, on those tables.

Tables for the decisions in scope

Delta tables for the finance and revenue figures you named, with grain, date, and owner written down. Other domains wait.

Connections and a visible refresh

Loads from the systems you already run, on a calendar the meeting understands. Failure is visible. The old number is not silently replaced by a partial one.

A check before a number is called official

A comparison to the source for the figures in scope, and a note about known gaps. Analysts can query the table to repeat the check.

Handover your team can run

How to add a column, who may change a definition, and how optional Managed Data Services would watch it. We do not leave a workspace only the original author can operate.

What a Free Assessment covers

A Free Assessment asks which number is not trusted, who consumes it, and whether Databricks is already the place it should live. We name the first tables, the source they must match, and what not to migrate yet.

Bring the two files that disagree. We follow up within 2 business days. If a report tool alone would solve it, we say that.

FAQs

Is a lakehouse the same as a warehouse?

For your purpose, it is a shared, governed place for the tables reports and models use. We will use Databricks SQL and Delta tables where that fits. We will not rename a file dump and call it done.

Do you move all history in the first release?

Only the history the decision needs. A full archive can follow. Loading every year before the definition is agreed creates a large copy of the argument.

Can Power BI read these tables?

Yes. That is often how finance opens the number. The lakehouse page is the foundation. Power BI consulting is the report, if the report is the gap.

Do you sell Databricks?

No. We implement on the workspace you have, in the cloud you have. The product name is not a commercial status.