Databricks
As of 2026-08-19
What is Databricks?
Databricks is a data and AI platform built around the **lakehouse** idea: one storage layer holding open table formats, with engines for SQL, streaming, data science and machine learning reading the same tables instead of each keeping a copy.
What it is
Databricks is a data and AI platform built around the lakehouse idea: one storage layer holding open table formats, with engines for SQL, streaming, data science and machine learning reading the same tables instead of each keeping a copy. For most of its life it was, from an SAP practice's point of view, a neighbouring stack — something the customer also owned, that someone else administered.
That changed when SAP embedded it in Business Data Cloud. BDC ships a Databricks capability inside the SAP estate, and the significance for a consultant is not the technology: it is that the boundary moved. Questions that used to be answered with "that lives on the data platform side" now land inside an SAP conversation, and the consultant in the room is expected to have a view.
What it is actually good at, stated plainly. Databricks earns its place where the work is large-scale transformation, streaming ingestion, and machine learning on data that is not only SAP — the workloads a semantic modelling layer is not designed to carry. SAP Datasphere earns its place where the work is business semantics, governed reuse and a modelling paradigm SAP data already speaks. Neither replaces the other, and an architecture that treats the choice as either/or usually ends up rebuilding the semantic layer by hand in notebooks.
The joint is the data, not the tool. What makes the pairing workable is open sharing of tables between the two sides rather than another copy of the data: the SAP side keeps the semantics and the governance, the lakehouse side keeps the scale and the ML surface. The failure mode worth naming is duplication — the moment the same business entity is defined once in a semantic model and again in a notebook, the two definitions drift, and the question "which number is right" has no owner.
Where a consultant's value sits. Not in operating clusters. It sits in being the person who can say which questions belong on which side, what crosses the boundary, and who owns the definition when it does. That is a semantics and governance conversation, and it is the one an SAP analytics consultant is better placed to hold than anyone else in the room.
Why it matters
- Since BDC embedded it, Databricks is no longer a stack an SAP consultant can decline to have a view on. 53 firm profiles in this directory name it in their own material — and the decision it forces (semantics here, scale there, who owns the definition) is exactly the one that has no owner by default.
Key points
- Lakehouse platform: one open-format storage layer, several engines reading the same tables.
- SAP embedded a Databricks capability in Business Data Cloud — the boundary moved into the SAP estate.
- It carries large-scale transformation, streaming and ML on non-SAP-only data; Datasphere carries business semantics and governed reuse.
- Neither replaces the other; treating the choice as either/or usually means rebuilding semantics by hand in notebooks.
- The failure mode is DUPLICATION of a business definition across both sides — after which 'which number is right' has no owner.