Concept card

SAP Data Intelligence

As of 2026-09-27

Edited by Cédric-Olivier Mâry, sole editor and publication director — 27 years in the SAP ecosystem. How these pages are produced, and where automation is used.

What is SAP Data Intelligence?

SAP's containerised data orchestration and pipeline product, formerly SAP Data Hub. Its capabilities are being absorbed into SAP Datasphere — which makes it, for most estates, a migration subject rather than a target architecture.

What it is

SAP Data Intelligence — SAP Data Hub before a 2019 rename — is a containerised data orchestration product: a graphical pipeline Modeler, a metadata catalogue, and an execution runtime that runs operators on Kubernetes. It was SAP's answer to the problem of moving and processing data across SAP and non-SAP systems without routing everything through a data warehouse first, and it came in two forms: Data Intelligence Cloud, run by SAP, and an on-premise edition customers ran themselves.

Why it matters now is mostly directional. The capabilities that made Data Intelligence distinctive — pipeline orchestration, connectivity breadth, metadata cataloguing — are the same capabilities SAP has been building into SAP Datasphere as replication flows, transformation flows, task chains and the catalogue. For an estate running Data Intelligence today, the strategic question is therefore not "how do we get more out of it" but "what does each pipeline become on the other side". That question has real content: a Data Intelligence graph with custom Python operators does not map one-to-one onto a Datasphere flow, and the operators that carried business logic are exactly the ones with no direct successor.

The pattern is familiar, and worth naming. This is the same shape as the BW-to-Datasphere and BusinessObjects-to-SAC transitions: a capable product whose functions are re-expressed inside a newer, more consolidated platform, with a residue of custom work that has to be rebuilt rather than converted. The residue is where the effort and the risk sit, and it is almost never visible in a licence-level comparison.

Why it matters

374 community articles discuss it and no Academy card covered it. For estates that run it, the value is entirely in the migration inventory — and the custom operators are the part with no successor.

Key points

Formerly SAP Data Hub; renamed SAP Data Intelligence in 2019. Two forms: Cloud (SAP-run) and an on-premise edition.
A containerised orchestration product: graphical pipeline Modeler, metadata catalogue, operators executed on Kubernetes.
Its capabilities re-appear inside SAP Datasphere as replication flows, transformation flows, task chains and the catalogue.
🔴 Custom operators (Python, shell, bespoke containers) have no direct successor — they are rebuilds, not conversions.
The migration inventory that predicts effort: graph count, classified into movement · transformation · custom, plus connection coverage.
The decision is per graph — re-express now, keep running, or retire — and movement/transformation graphs largely decide themselves.
The crossover point (when the residue costs less to rebuild than another year of runtime) needs a date and an owner, or the estate stalls while other programmes take the budget.

Terms used on this page

Modeler: The graphical pipeline designer in SAP Data Intelligence, where graphs of operators are assembled.
Graph: A Data Intelligence pipeline: a connected set of operators with an execution runtime.
Operator: A pipeline step. Standard operators ship with the product; custom operators carry customer code and are the migration residue.
Metadata Explorer: The Data Intelligence catalogue and profiling surface, whose role Datasphere's catalogue now plays.
Custom operator: A Data Intelligence operator carrying customer code (Python, shell, a bespoke container). It has no Datasphere successor and is the unit of rebuild — count these before counting graphs.
Crossover point: The moment when maintaining the Data Intelligence runtime for the remaining graphs costs more than rebuilding them. It is a planning artefact, not an event: someone has to name it.

Sources

Full card available to members. What the full card adds: the full decision framework · the SAP vs Snowflake / Databricks / Fabric comparison · the common pitfalls and their fix · the cheat sheet · the architecture schemas · the code blocks · the facts worth quoting.

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