SAP REFERENCE GUIDE
SAP Business Data Cloud
Complete reference for activities, interviews, and certification — covering BDC architecture, components, data products, design principles, and the full SAP data ecosystem.
Fully Managed SaaS
Data Products
AI-Ready Platform
SAP BTP
Delta Sharing
Joule AI
SAP Business Data Cloud (BDC) is a fully managed SaaS solution that unifies and governs data from SAP and non-SAP applications to power advanced analytics and AI. It is not a single product — it is a platform of multiple software components working together as an AI-ready data foundation.
Core definition: BDC provides the data layer that bridges SAP applications and business AI. It is built on SAP BTP and delivers the complete data fabric — from extraction through modelling, governance, and consumption.
🤖 AI-Ready Foundation
Trusted, governed data foundation for AI development. Eliminates unreliable AI results from poor data quality.
📦 Ready-to-Use Content
Pre-built data products, intelligent dashboards, and data models for all lines of business and industries.
🔗 Unified Data
Single platform for all SAP + non-SAP data. One domain model harmonises business objects across applications.
🛡 Governed & Open
Built-in data governance, lineage, and access control. Delta Sharing enables zero-copy open ecosystem.
| Challenge | Problem | BDC Solution |
|---|
| Untrusted AI Data | Data errors, missing values, corrupt extraction — AI generates unreliable results | Managed extraction with validation. SAP certifies data quality end-to-end. |
| Complex SAP Extraction | Different SAP technologies need middleware, special skills, complex pipelines | BDC manages all extraction. SAP takes care of the data pipeline. |
| Multiple Object Definitions | Same object (Customer, Product) defined differently across SAP apps | One Domain Model — harmonises all definitions into a single business object. |
| Lost Semantics | Business meaning stripped during extraction; must be rebuilt | Extracts raw data + all semantics together. No rebuilding needed. |
| Incomplete Data Sets | 100+ SAP apps — analytics miss data from some applications | Data products cover all SAP applications. Complete picture. |
| Slow Analytics Development | Business waits months for IT to build dashboards | Pre-built Intelligent Content — ready to use immediately. |
| High Storage Costs | Traditional databases expensive at scale | Lakehouse architecture on hyperscalers. Storage decoupled from compute. |
| Poor Data Governance | Data scattered across silos, hard to track lineage and access | Centralised governance model — know where data is, who has access, full lineage. |
BDC is a multi-layer architecture on SAP BTP. Each layer plays a distinct role from source data to business consumption.
📊
Consumption Layer
SAC (dashboards, planning, Insight Apps) · Joule AI Copilot (natural language queries) · SAP AI Core (ML models) · Pre-built Data Products
↕ semantic layer (Business Builder)
🏗
Modelling Layer
SAP Datasphere (Data Builder + Business Builder) · SAP BW / BW4HANA · SAP Databricks (AI/ML, Spark) · SAP Snowflake
↕ storage
💾
Storage Layer
SAP HANA Cloud (in-memory columnar) · HANA Data Lake (lakehouse at scale on hyperscalers) · Delta Lake file format
↕ BTP Integration Suite / SLT / Event Mesh / Open Connectors
🔌
Source Systems
SAP: S/4HANA, SuccessFactors, Ariba, IBP, Concur, Fieldglass, BW · Non-SAP: Salesforce, Oracle, Databricks workspaces, REST APIs, flat files
BDC is not a single product — it is a platform. Customers choose which components they need and grow their BDC landscape as requirements evolve. SAP Datasphere is always the central component.
🤖
Joule AI Copilot
Natural language queries on data products. Assists IT developers in building intelligent content (data products, models, dashboards). Runs on BTP AI Core.
📦
Intelligent Content
Pre-built dashboards and data models for all lines of business and industries. SAP delivers and manages. Customers choose which content to install.
🏗
SAP Datasphere
Central component. Data integration + data modelling + Business Builder semantic layer. Manages analytical roles and data access control.
📊
SAP Analytics Cloud
Visualisation and planning component. Delivers pre-built dashboards (Insight Apps). Connects to Consumption Models as live data source.
🎯
SAP MDG
Master Data Governance for SAP data (ERP systems). Improves quality of master data from S/4HANA and other ERP systems.
🌐
SAP Reltio
Master data governance for non-SAP data. Extends MDG coverage to all application master data sources.
💾
SAP HANA Cloud
In-memory database. Consumes data products. Supports multi-model analytics (spatial, vector, graph, JSON). Can generate custom data products.
⚡
SAP Databricks
Embedded Databricks for data science — AI/ML, Spark, Delta Lake. Enrich SAP data using machine learning. Data products shared bidirectionally.
❄️
SAP Snowflake
Data science and ML on SAP data products. Full enterprise Snowflake feature set for SAP customers via partnership.
📚
SAP BW
Existing BW deployments can integrate with BDC. Generate data products from BW InfoProviders (CompositeProviders, DSOs). Modernisation path.
Data products are at the heart of BDC. They are curated business data + metadata extracted from SAP and non-SAP applications, packaged as business-ready, self-describing, discoverable assets with defined ownership and SLA.
Four Mandatory Characteristics
📊 Business Data Sets
Contains transaction or master data from one or more tables/views — forming a meaningful, complete data set with all essential fields.
📝 Well Described
Includes associated metadata providing full semantics (meaning) of the data. Business meaning is never lost.
⚡ Easily Consumable
Securely shared with data platforms and BI tools without physically copying data. Zero-copy via Delta Sharing.
🔍 Discoverable
Exposed via BDC Cockpit catalog. Business users can search and find data products based on their connected source systems and entitlements.
Data Model vs Data Product — Key Distinction
🟠 Data Model — Technical Layer
Tables, views, joins, star schema in Data Builder.
Consumer: data engineers and architects.
Question: "How is the data structured?"
Coverage: one SAP app's technical schema.
Technical Foundation only
🔵 Data Product — Business Layer
Data Model + Business Builder semantics + metadata + SLA + ownership + SAC story.
Consumer: business users directly.
Question: "What business question does this answer?"
Coverage: business domain (may span multiple apps).
Business-ready Governed Complete stack
Data Packages
Data products are grouped by Data Packages. Instead of activating individual data products one by one, activate the entire Data Package — all related products install together. Managed and discoverable from the BDC Cockpit.
SAP-Managed Business Data Products (Examples)
💰 Finance P&L
Pulls from S/4HANA. GL Account, Cost Centre, Profit Centre BEs. Pre-built Insight App included. Finance team uses immediately.
👥 Headcount
Pulls from SuccessFactors. Employee, Org Unit dimensions. Headcount Fact Model + Consumption Model + SAC Insight App.
💳 Spend
Pulls from Ariba + S/4HANA. Cross-app data product. Vendor, Category dimensions. Procurement team uses directly.
1️⃣ Ready-to-Use Intelligent Content
Pre-built AI-infused dashboards and underlying data models for all domains and industries. Business users start immediately — no IT development cycle.
2️⃣ Data Product Economy
Treats data as a product — cleaned, refined, packaged, business-ready. Data products are discoverable and highly reusable. Can be enriched with AI/ML and fed back to the catalog.
3️⃣ One Domain Model
Harmonises different definitions of the same business entity across 100+ SAP apps. Business user sees a single "Customer" regardless of which source app it came from.
4️⃣ Open Data Ecosystem
Delta Sharing standard for zero-copy, bidirectional data sharing. Partners: Databricks, Snowflake, and more. SAP data accessible across entire ecosystem without creating copies.
Delta Sharing
- Open standard technology implemented throughout BDC
- Zero-copy approach — single version of data shared by all consumer applications
- No data duplication when sharing between BDC and partner platforms
- BDC governs all sharing — only permission-granted consumers can access
- BDC tracks which consumers are sharing which data products
SAP BTP is not one of the products you use — it is the cloud operating system everything runs on, connects through, and is secured by.
⚙️ Runtime Platform
Datasphere, SAC, AI Core, Joule all run ON BTP as managed services. BTP provides compute, scaling, networking, and SLA guarantees.
🔗 Integration Pipe
BTP Integration Suite + SLT carries data from S/4HANA, SuccessFactors, Ariba into BDC. Event Mesh for event-driven triggers. Open Connectors for 150+ non-SAP adapters.
🔐 Identity & Security
BTP Identity Authentication Service (IAS) = SSO across all products. Identity Provisioning (IPS) = user sync. One login for SAC, Datasphere, Databricks, BDC Cockpit.
🤖 AI & Extensibility
Joule runs on BTP AI Core. Custom ML models on BTP AI Core or Databricks. Custom apps deployed on Cloud Foundry / Kyma runtimes.
BTP's Role Per Component
| Component | What BTP Provides |
|---|
| SAP Datasphere | Runs on BTP. HANA Cloud is a BTP service. Compute, storage scaling, network security. |
| SAP Analytics Cloud | Hosted on BTP. SSO via IAS. APIs for live Datasphere query. |
| SAP BW / BW4HANA | Integration Suite bridges BW InfoProvider data into Datasphere. BTP = modernisation pathway. |
| SAP Databricks | Data products shared via Delta Sharing. BTP manages identity context and data governance boundary. |
| SAP sources (S/4HANA etc) | Integration Suite + SLT replicates data. Event Mesh carries business events. API Management governs APIs. |
| Non-SAP sources | Open Connectors + iFlows transform and route non-SAP data into Datasphere local tables. |
| Joule | Runs on BTP AI Core. BTP manages LLM infrastructure, security, and identity-based data access control. |
Organisations with existing SAP BW investments can integrate BW with BDC — protecting their investment while gaining access to BDC's modern data product economy and AI capabilities.
Two Integration Modes
One-time Onboarding
Generate data products from BW InfoProviders (CompositeProviders, DSOs) as a one-time migration exercise. BW data becomes BDC data products.
Continuous Pipeline
Existing BW extractions continue to run and feed BDC data products with live, continuously updated data. BW stays as source of record.
Path to Modernisation
Existing SAP BW
On-premise or BW4HANA
→
Lift to BDC private cloud
SAP manages infrastructure
→
Generate data products
From BW InfoProviders
→
Share with Databricks/Snowflake
ML, AI agents, analytics
- Lift BW to BDC private cloud → SAP manages provisioning and infrastructure
- BW-generated data products stored in modern lakehouse architecture (hyperscalers)
- Immediate storage cost reduction vs traditional BW database storage
- BW data products shared with Datasphere to combine with standard + non-SAP data products
HANA Cloud is both the database engine underlying Datasphere AND a standalone component that can be added to a BDC landscape for advanced multi-model analytics and custom AI agents.
Deployment Options
🟢 Greenfield
New HANA Cloud instance provisioned as part of BDC landscape. Clean start on modern in-memory platform.
🔵 Brownfield
Existing HANA Cloud instance integrated into BDC landscape. Reuse existing investment within BDC governance.
Multi-Model Analytics
🗺 Spatial
Geometric data (points, lines, polygons). Combine spatial with transactional data. Real-time geographic analysis, proximity calculations.
🔢 Vector
Store and search unstructured data (text, documents, images) combined with structured data. Supports AI/LLM augmentation.
📄 JSON Document
Store, manage, query semi-structured JSON data. Deeply nested structures like logs and sensor data.
🕸 Property Graph
Shortest path, centrality, clustering algorithms. Used for structural relationship analysis.
🧠 Knowledge Graph
Contextual queries based on meaning — not just structure. Semantic relationship traversal.
🔗 Relational
Standard columnar HANA database — star schema, calculation views, information modelling. Foundation for all other models.
Advanced Analytics Capabilities
- AutoML — automated end-to-end ML model build, train, deploy using PAL and APL libraries
- Generative AI — HANA Cloud as essential data source to augment LLMs for accurate natural language query results
- Predictive Analytics Library (PAL) — built-in ML algorithms for data scientists
- Smart Data Integration (SDI) — connect to any SAP or non-SAP data source directly
- Delta Share — receive BDC data products and generate custom ones back to the shared catalog
Critical Facts to Remember
CERT BDC Definition
Fully managed SaaS on BTP. Unifies SAP + non-SAP data. Not a single product — a platform of components. Datasphere is always the central component.
CERT Data Model vs Data Product
Data Model = technical layer (tables, views, star schema). Data Product = complete stack: data model + semantics + governance + SAC story. Same tools — different completeness.
CERT 4 Design Principles
1. Ready-to-use Intelligent Content. 2. Data Product Economy. 3. One Domain Model. 4. Open Data Ecosystem (Delta Sharing).
CERT Data Product Characteristics
Always: Business Data Sets + Well Described + Easily Consumable + Discoverable. Grouped in Data Packages. Managed from BDC Cockpit.
CERT BTP's Role
Runtime platform + integration pipe + security layer + AI runtime. Everything in BDC runs ON BTP and connects THROUGH BTP.
CERT Delta Sharing
Zero-copy approach. Open standard. Single version of data shared by all consumers. No data duplication. Permission-controlled by BDC.
Component Quick Reference
| Component | Role in BDC | Primary User |
|---|
| Joule | AI Copilot — natural language on data products + assist in building content | Business users + IT developers |
| Intelligent Content | Pre-built dashboards and data models by SAP | Line-of-business users |
| Datasphere | Central data integration + modelling + governance hub | Data engineers, architects |
| SAC | Visualisation, planning, Insight Apps | Business analysts |
| SAP MDG | Master data quality for SAP ERP data | Data stewards |
| SAP Reltio | Master data quality for non-SAP data | Data stewards |
| HANA Cloud | In-memory database + multi-model analytics + custom data products | Data engineers, data scientists |
| SAP Databricks | AI/ML, Spark, data science on SAP data products | Data scientists |
| SAP Snowflake | Data science and ML on SAP data products | Data scientists |
| SAP BW | Legacy DWH modernisation path into BDC data products | BW developers, architects |
The Transformation Triangle — LeanIX + Signavio + Cloud ALM
🔵 SAP LeanIX
Enterprise Architecture Management. Maps IT landscape — apps, capabilities, interfaces, lifecycle. Answers "what apps do we have and what's our target architecture?"
🔴 SAP Signavio
Business Process Intelligence. Process mining from event logs — actual vs designed flows. Answers "how do our processes actually run and where are the problems?"
🟢 SAP Cloud ALM
Application Lifecycle Management. Manages implementation projects and system operations. Free with Enterprise Support / RISE / GROW with SAP. Successor to Solution Manager.