Introduction
Let’s go over some of the most important terms you’ll encounter while working with SAP Business Data Cloud. Understanding these foundational concepts will help you navigate the platform more confidently and communicate more effectively with both business and technical users.
Data Products
What it is:
Pre-packaged, business-ready datasets that are curated and governed for reuse across applications, dashboards, and AI models.
Why it matters:
Data Products eliminate the need to wrangle data from multiple systems manually. They include not just the data itself but also business context, metadata, and usage rules.
Analogy:
Think of Data Products like meal kits. Everything you need—ingredients, instructions, and measurements—is bundled together, cleaned, and ready to use.
Types of Data Products:
- SAP-managed: Provided and maintained by SAP
- Customer-managed: Created by your organization for internal needs
- Partner-managed: Built by SAP partners for industry-specific use cases
Data Packages
What it is:
Groups of related Data Products bundled together for easier installation and discovery.
Why it matters:
Instead of searching for individual Data Products one by one, users can deploy entire packages related to a business domain—such as Finance, Sales, or HR.
Analogy:
If Data Products are meal kits, Data Packages are themed meal plans—like “Quick Dinners” or “Italian Favorites.”
Data Models
What it is:
Logical frameworks that connect multiple Data Products to answer business questions or power dashboards and applications.
Why it matters:
Data Models define the relationships and logic behind your data, making it usable in meaningful ways for reporting, forecasting, and AI.
Analogy:
If Data Products are ingredients, the Data Model is the recipe—telling you how to mix them together to create a useful insight.
Dashboards
What it is:
Visual interfaces that allow users to interact with and explore data using charts, KPIs, filters, and AI-driven recommendations.
Why it matters:
Dashboards are often the end destination for data in BDC. They let users quickly assess performance, forecast outcomes, and explore details.
Analogy:
Like a car dashboard, they display important signals at a glance—sales performance, customer satisfaction, financial health, and more.
Unified Semantic Layer
What it is:
A business-friendly layer that sits on top of technical data models to make data understandable and consistent across tools and teams.
Why it matters:
The semantic layer ensures that everyone in the organization is looking at the same definitions—such as “Revenue” or “Active Customer”—regardless of the tool they’re using.
Analogy:
It’s like a shared dictionary for your data—so that “Sales” means the same thing in a dashboard as it does in an AI model.
Intelligent Applications
What it is:
Pre-built, AI-powered business apps created by SAP to solve specific use cases like forecasting, financial planning, and customer analysis.
Why it matters:
These apps allow non-technical users to work with advanced analytics and AI models without needing to build anything from scratch.
Analogy:
They’re like pre-assembled apps you can plug into your business environment—similar to an app store for enterprise data insights.
SAP Business Data Cloud Cockpit
What it is:
The central control panel for BDC, where admins and power users manage installations, permissions, integrations, and deployment.
Why it matters:
It simplifies the management of everything inside Business Data Cloud—from deploying Intelligent Applications and Data Packages to configuring user access.
Analogy:
Think of it as your operations console for managing your data landscape within BDC.
Delta Sharing
What it is:
Delta Sharing is an open, REST-based protocol developed by Databricks that enables secure, real-time data exchange without requiring full data replication.
Why it matters:
Within SAP Business Data Cloud, Delta Sharing enables:
- “Zero‑copy” sharing between SAP Datasphere and SAP Databricks—data lives in the same object store and is accessible across services without costly duplication.
- Secure and efficient data access: policies can restrict access at the column or partition level, and updates are shared incrementally via deltas.
- Cross-platform AI/analytics: Databricks users (inside or outside BDC) can access SAP Data Products natively—no ETL is needed—streamlining the use of live SAP data in advanced computing environments.
How it works:
A Delta Sharing server provides pre-signed access to Delta Lake storage files, enabling high-volume, parallel reads directly from cloud storage.
Analogy:
Think of it like giving someone a secure, direct line to your library (data lake) instead of photocopying every book for them before they read it.
Summary
These core concepts are the building blocks of SAP Business Data Cloud. As you explore the platform, you’ll see how these terms interconnect—from raw data in your source systems to AI-powered applications that deliver insights to your business.
Understanding these terms will help you:
- Navigate the platform more effectively
- Communicate clearly with technical and non-technical colleagues
- Make the most of your investment in SAP Business Data Cloud
