If you’ve done data modeling in SAP BW/4HANA or SAC, the first reaction to Palantir Foundry’s Ontology is usually “isn’t this basically an InfoCube?” The skeleton really is similar. But the two platforms were built to solve different problems from the start, and that difference runs through the whole architecture. This post is for practitioners with SAP BW experience: a concept mapping table plus a manufacturing (equipment management) example to lay out where the two platforms differ and how they coexist.
Why Compare the Two Platforms
Organizations that run SAP as their system of record usually hit the same question first when adopting Palantir Foundry: “Is this replacing SAP BW, or doing something else entirely?” Short answer: it’s not a replacement. SAP keeps its role as the enterprise single source of truth for finance and consistency, while Foundry typically sits on top as a hybrid layer for operations, workflow, and AI. Understanding that structure starts with the identity difference between the two platforms.
Comparison Basis: Identity and Core Assets
| SAP BI (BW/4HANA · SAC · BO) | Palantir Foundry | |
|---|---|---|
| Identity | An enterprise reporting/analytics-centric data warehouse | An operational platform spanning analysis, decisioning, and execution (writeback) |
| Core assets | InfoObject, ADSO, CompositeProvider, BEx | Dataset, Pipeline, Ontology (Object Type / Link Type / Action Type) |
| Strength | Native ERP (ECC/S4) integration, financial consistency | Ontology-based semantic model, full data lineage, AIP integration |
The most important row here is identity. SAP BI is a platform for “viewing” data — it replicates and aggregates ERP data for dashboards, while actual execution (approving a purchase order, assigning equipment) requires going back to an ERP screen; the two stay separate systems. Foundry, by contrast, is a platform for “acting on” data. Action Types let you change object state directly and write back to the source system, so analysis → decision → execution happen inside a single canvas.
Data Architecture Flow Comparison
| Stage | SAP BW | Foundry |
|---|---|---|
| Source | ECC/S4 | Connector |
| Extraction | Extractor | Sync (Snapshot/Incremental) |
| Staging | PSA/ADSO | Processed (Pipeline) |
| Integration | Composite | Output (Dataset) |
| Endpoint | Reporting (SAC/BEx) | Ontology + apps (Workshop) |
The core difference sits at the endpoint. BW’s endpoint is a “query (report)”; Foundry’s endpoint is a meaningful “operational object (Ontology).” A BW pipeline’s final output is a report meant for humans to read; a Foundry pipeline’s final output is an Object Type that Workshop apps and AIP agents both reuse — for lookup and for execution.
Concept Mapping Table (Rosetta Stone)
The fastest way to carry your SAP BW experience into Foundry is this mapping table.
| SAP Legacy (AS-IS) | Palantir Foundry (TO-BE) |
|---|---|
| [Extraction structure] DataSource | Data Connection Source |
| [Load execution] InfoPackage (Full/Delta) | Sync (Snapshot/Incremental) |
| [Storage unit] ADSO | Dataset (Parquet-based) |
| [Analytical model] InfoCube | Denormalized Output + Ontology |
| [Master data] InfoObject (e.g. 0MATERIAL) | Ontology Object Type |
| [Transform logic] Transformation / AMDP | Pipeline Builder / PySpark |
The experience of having built pipelines carries over as-is. What changes is the tool’s vocabulary — and one query paradigm.
Query Paradigm: From Star Schema to an Ontology Network
SAP BW’s star schema is a rigid, physical join structure optimized for query performance — a Fact Table (e.g., production output) sits at the center, with Dimensions like time, org unit, material, and customer radiating outward.
Foundry uses a flexible object graph that models business meaning directly on top of denormalized pipeline output. Object Types like Customer, Order, and Material connect to each other via Link Types on top of Parquet-based Datasets — instead of the concept of a Dimension, the “Object” itself becomes a first-class citizen.
Four Principles of Ontology Design
When moving SAP data into a Foundry Ontology, four rules come up again and again in practice.
| Principle | Description |
|---|---|
| 1. Noun → Object Type | Define a countable entity (e.g., worker, equipment, order) |
| 2. Properties + PK | Define the Properties that describe the entity, and the unique value (Primary Key) that identifies each row |
| 3. Verb → Link Type | Define a directional relationship in the form “[A] does [X] to [B]” |
| 4. Cardinality → Implementation | 1:N is a foreign key (FK); N:M is promoted to an independent “Link Object” |
The formula for deriving a business sentence is simple: a worker (noun) operates (verb) equipment (noun).
Cardinality Implementation Examples
1:N relationship (no extra dataset needed) — one worker handling many orders just needs an FK (assignee ID) on the Order side.
N:M relationship (promoted to an independent dataset) — workers and equipment relate many-to-many (one worker handles several machines, one machine is shared across shifts by several workers). Here you create a separate Link Object, “Equipment Assignment,” carrying FKs (employee ID, equipment ID) plus properties of the relationship itself — assignment date, shift, run time.
Real-World SAP Data Mapping
An example of mapping actual SAP tables into an Ontology.
| SAP Raw Data | Ontology Semantics |
|---|---|
| EQUI (EQUNR) | [Object PK] Equipment.EquipmentID |
| AUFK (AUFNR) | [Object PK] Order.OrderNumber |
| LFA1 (LIFNR) | [Object PK] Supplier.SupplierCode |
| AFRU (RUECK/PERNR/AUFNR) | [Link Object] EquipmentAssignment |
Operating rule: Semantic field renaming (e.g., EQUNR → EquipmentID) happens only at the top-level Ontology layer, never in an intermediate pipeline. Renaming fields mid-pipeline breaks lineage tracing and scatters the same source field under multiple names.
Put together, workers, suppliers, and equipment connect through Order as a hub, and N:M Link Objects like Equipment Assignment and Equipment Maintenance Contract are added on top — turning siloed tables into a single digital-twin ecosystem that mirrors how the business actually operates.
Beyond Read: Governed Writes via Actions
Where BW/SAC stops at “viewing,” Foundry goes a step further with Action Type. An Action has four stages.
- Parameters (input) — initial values entered by a user or an AI agent
- Rules (validation/permissions) — data-consistency checks and a permission gate
- Effects (write) — actually creating, updating, or deleting the object
- Functions (extensions) — logic computation, approval workflows, webhook notifications
For example, in a Workshop screen for assigning equipment operators, a user picks a worker, shift, and date, then clicks “Run.” That button triggers the creation of a safe N:M object (Equipment Assignment) that has passed the rule gate (permissions and consistency checks). SAP BW/SAC has no equivalent layer — a BEx query ends at read, and execution always has to go back to an ECC/S4 transaction screen.
AIP & Agent Integration: AI as an Actor
| Foundry AIP Agent | SAP Joule & AI Core | |
|---|---|---|
| How it works | A closed loop of read → analyze → act (calling an Action) | A sidecar to the existing screen |
| Role | An operational actor grounded in the Ontology that calls Actions itself, without hallucination | Simple natural-language Q&A and an assistant role |
The difference comes from Ontology including Action Type, not just Object Type. Because the “actions” an agent can call are already defined inside the Ontology, an AIP agent doesn’t stop at generating an answer — it can actually execute a verified Action.
Situational Guide: Coexistence, Not Replacement
| Situation | Recommended direction |
|---|---|
| Financial close, reports where accounting consistency is paramount | Keep SAP BW/SAC as the single source of truth |
| Operational decisions that require tying together multiple systems (ERP, MES, IoT) | Model it as an integrated Foundry Ontology |
| Cases where a lookup needs to flow straight into execution (assignment, approval, purchase order) | Implement writeback via Foundry Action Type |
| Wanting an AI agent to work with SAP data in natural language | Keep SAP as the source and bridge it through a Foundry Ontology semantic layer |
It comes down to four points.
- The skeleton is similar — BW experience is a strong asset for learning Foundry.
- The purpose diverges — read-centric (SAP) vs. integrated read+write (Foundry).
- Ontology is the watershed — the moment Actions attach to Objects, you get an “actionable digital twin.”
- Full coexistence — SAP’s source-of-truth reliability and Foundry’s operational flexibility aren’t a replacement relationship; they connect through an Ontology bridge.
Wrap-up
SAP BW/SAC and Palantir Foundry aren’t competitors — they’re two layers with different roles. SAP protects finance and consistency as the enterprise single source of truth, while Foundry sits on top, integrating multiple systems through an Ontology to unify lookup, decisioning, and execution. The modeling instincts you built in BW carry over directly, so the fastest path in is the Rosetta Stone: translate InfoObject to Object Type, Transformation to Pipeline Builder, and go from there.