For years, our data architecture was optimised for a deterministic world focused on one purpose: feeding applications  
On one side you had operational databases, on the other a data warehouse, with compute in between to move and model data.
The main success metric was price–performance: maximise query throughput while minimising cost. That model worked well, but the world has changed.  AI and agentic workloads do not fit neatly into this static, siloed stack, and traditional pipelines cannot keep up with the speed and context modern business applications require.

We then entered the Lakehouse era, where compute and storage were decoupled and a unified layer emerged across data. This closed some gaps, but it still fell short of what AI-driven applications and agents need today: deep business context, not just accessible data. This is why the notion of a “knowledge core” has become so important.
It provides the shared semantic and business context that powers not only dashboards, but also agents, co-pilots, and operational applications that must understand your processes and decisions. SAP Business Data Cloud is designed exactly for this new reality.
It unifies SAP and non-SAP data, preserves business semantics, and exposes a governed knowledge core that can feed dashboards, intelligent applications, and AI agents from the same trusted foundation. 

SAP Business Data Cloud — the components that make modern, AI-driven business possible

Today's successful data strategies do more than store facts — they connect data to meaning and action. SAP Business Data Cloud delivers that connection as a single, managed service by combining four complementary layers that together form a business data fabric: lake storage, intelligent compute, a knowledge core, and AI-powered agents and applications.

Lake storage: SAP and non-SAP data (including SAP Business Warehouse sources) are brought together into a secure foundation so teams can access the full enterprise dataset without brittle point-to-point integrations. This unified storage lets organisations keep authoritative copies where needed and enable zero-copy access patterns where performance and scale demand.

Intelligent compute: Workloads run where they make most sense — SAP HANA Cloud, SAP Databricks, and SAP Snowflake provide flexible execution across workflows based on your use case.

Knowledge core: Delivered through SAP Datasphere and SAP Analytics Cloud, the knowledge core adds semantics, curated data products, and knowledge graphs so data carries business context (definitions, lineage, relations). That context is what turns raw datasets into trusted business artefacts that analysts, planners, and ML models can rely on.

AI agents & intelligent applications: Joule and Intelligent Applications surface the knowledge core to people and processes — answering open questions, generating analytics, orchestrating workflows, and executing decisions that are grounded in governed data. Agents draw on prebuilt skills and the knowledge graph to automate work while preserving traceability and control.

Why the integration matters

Individually these components are powerful, but the real value comes from how they work together as a fabric: governed data flows from lake storage into semantic models, compute layers enrich and transform it, the knowledge core captures meaning and relationships, and AI agents turn that context into action. The result is an enterprise-grade foundation for trusted, explainable AI and faster, safer decision-making at scale.

How it feels in practice – Example

A planner asks Joule for the expected impact of a seasonal promotion. The agent retrieves harmonised sales and customer data from the lake, uses a Databricks model for demand forecasting, references product and promotion semantics from the knowledge graph, and returns an explainable recommendation inside an SAP Analytics Cloud story — all while preserving lineage and access controls. That end-to-end flow is what SAP Business Data Cloud is built to enable.

Did you know that SAP Datasphere which is part of the knowledge core goes far beyond traditional data warehousing compared with SAP BW and SAP BW/4HANA?

It retains the core capabilities customers know from SAP BW and BW/4HANA — including data integration, data management and governance, analytic modeling, and integrated analytics and planning — so the foundation is not lost, but carried forward.

At the same time, SAP Datasphere expands the scope in three important ways. First, it grows into an application platform through integrated add-ons. Second, it extends into a lakehouse architecture with object storage and a separation of compute and storage, which brings greater elasticity and scalability. Third, it introduces capabilities beyond classic data warehousing, such as self-service spaces, semantic onboarding, a data marketplace, a knowledge graph for GenAI scenarios, and an open ecosystem for technology partner integrations.

The key message is simple: everything customers value in SAP BW today remains relevant, but SAP Datasphere opens the door to a much broader, more flexible, and more future-ready data platform.

What is now SAP's Offering to Safeguard investments with a guided modernization strategy?

SAP gives every SAP BW customer a clear, low‑risk path into the future with a three‑track strategy: Optimize, Modernize, Innovate.

• Optimize keeps your current SAP BW investment safe while you prepare your move. SAP helps you reduce and tier data to cut storage costs, shift BW into a private cloud if you want, and secures your operations with extended SAP BW NetWeaver maintenance as a safety net for customers who need more time.

• Modernize brings your analytics into SAP Datasphere and SAP Business Data Cloud without starting from scratch. With tools like the Query Template Generator, Data Product Generator, and BW Migration Assistant, you can systematically move queries, semantic data models, and ETL processes into a modern, cloud‑native setup.

• Innovate is where you unlock real upside: SAP‑managed data products replace legacy SAP BW workflows, AI agents accelerate data product creation, and tight integration with platforms such as Databricks opens up advanced ML and GenAI scenarios on trusted SAP data.

The best part: these tracks are not a rigid sequence. You can optimize parts of your landscape, modernize priority workloads, and innovate in high‑value areas in parallel — at the pace that fits your organization’s readiness and risk profile.

Let's have a closer look into the 3 Toolings. 

Data Product Generator for SAP Business Data Cloud – Already available

The Data Product Generator is the fast lane for bringing your SAP BW data layer into SAP Datasphere. It pushes InfoProvider data straight into the SAP Datasphere object store while keeping SAP BW semantics fully intact, so you can reuse your existing logic instead of rebuilding it.

It supports all key SAP BW InfoProvider types — InfoObjects, DSOs, CompositeProviders, MultiProviders, InfoCubes, and even Queries-as-InfoProviders — preserves associations, and handles both initial loads and delta updates across large SAP BW landscapes. In internal benchmarks, this automation delivers at least an 80% faster path than manual extraction and loading.

In the architecture shown here, the Data Product Generator reads directly from the SAP BW InfoProvider, lands files and delta tables in the SAP BW Inbound Space of the SAP Datasphere object store, and surfaces the result as a ready‑to‑use Data Product with an accompanying Delta Share.

Example of an SAP BW InfoProvider that has been replicated into the object store via subscription – including its full business context and semantics.

Query Template Generator for SAP Business Data Cloud – planned for Q2/Q3 2026

The Query Template Generator is the engine that moves your analytical workloads from SAP BW into SAP Datasphere without disruption. Its purpose is to automatically migrate SAP BW query metadata and business logic into Analytic Models, so users can continue working with familiar queries while the platform modernizes underneath.

In concrete terms, it converts SAP BW Queries into Analytic Models, transfers all query metadata, and brings along the fact source together with its associated dimensions. SAP’s internal measurements show around a 50% reduction in migration effort compared to manual re‑creation.

As shown in the architecture on the right, the Query Template Generator reads a SAP BW Query with its InfoProvider and InfoObjects and translates it into an Analytic Model with a Fact View, dimensions, hierarchies, and texts — all landing in a dedicated HANA Space in SAP Datasphere. It works hand‑in‑hand with the Data Product Generator, which takes care of the underlying data layer.

BW Migration Assistant for SAP Business Data Cloud – planned for Q4/2026 – Q1/2027

The BW Migration Assistant is the tool that moves your data pipelines – not just queries or one‑off data extracts, but the full ETL layer. Its purpose is to translate SAP BW data flows into equivalent SAP Datasphere artefacts, with AI handling most of the heavy lifting on ABAP code conversion.

It focuses on SAP BW data flows: transformations, DTPs, and the chained InfoProvider‑to‑InfoProvider structures that form the backbone of typical BW architectures. The AI engine converts ABAP routines into SQL‑based Transformation Flows in SAP Datasphere, and SAP’s internal measurements indicate about a 50% reduction in migration effort compared to manual re‑implementation.

In the diagram, you see a multi‑hop SAP BW data flow on the left — multiple InfoProviders linked by Transformations and DTPs — being translated into a corresponding chain of Local Tables and Transformation Flows in the SAP Datasphere Object Store Space on the right. 

Call to Action!

Now is the right moment to move a legacy SAP BW to SAP Business Data Cloud if you want to get serious about AI, for three key reasons:

1. AI needs live, contextual data – not just historical reports.
   Modern GenAI, agents, and ML models depend on fresh, granular, well‑defined data products. Classic SAP BW is batch‑oriented, tightly coupled, and hard to plug into flexible AI workloads.

2. SAP BDC turns your SAP BW investment into an AI‑ready asset instead of technical debt.
   You can lift SAP BW models, semantics, and data flows into cloud‑native data products and analytic models, instead of rebuilding everything from scratch. That means faster time‑to‑value and far less migration risk.

3. There is a real window of opportunity right now.
   Maintenance timelines, cloud strategies, and exploding AI demand are converging. If you modernize SAP BW into SAP BDC now, you create a clean, governed foundation for AI for the next 5–10 years, instead of pouring more effort into a legacy stack that can’t keep up.

In short: legacy SAP BW slows your AI ambitions down; SAP Business Data Cloud gives you a future‑proof, open data foundation where AI can actually scale and deliver measurable business impact.