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Product and Topic Expert
Product and Topic Expert
updated date: 26.Jul.2023

Multi-cloud setup has become more and more prevalent for enterprise customers. With that being said, it’s quite common for RISE with SAP Private Cloud Edition customers, to run their business mainly on SAP business applications, while still having some other 3rd party software solutions and their own hyperscaler subscriptions (Microsoft Azure, Amazon Web Services, Google Cloud). As such, when it comes to data insights and foresight on top of data generated from multiple sources, having the data properly located in the corresponding landscape, and processed by the right tools becomes essential.

This blog post focuses on Microsoft Azure.

Link for AWS: here.

Link for GCP: here.

1. Value Proposition

1.1. Data Federation vs. Data Export

There are two types of data interoperability: either you make a copy of your data, also called “export” or “replication”, or you use the data from its original place. The latter is what data federation is about. In the following, we explain why data federation is a more recommended approach.

As stated in the previous blog, ‘Unlock the Power of Business Data for SAP RISE Customers: Mastering Data Management and Cultivating ...‘, using SAP data management solution on top of business data generated in SAP landscape has its unique value, especially in cases with regard to currency conversion, hierarchy, derivation, time dependency master data, and so on. For instance, corporate-finance-related planning, analytics, visualization, and machine learning.

With that being said, the data generated from SAP business applications is recommended to stay within SAP landscapes (flow 1+2+3). Given that exporting the data directly outside (flow b) will suffer a sacrifice of decreasing the data quality. To remediate that, it will be necessary to rebuild the context in the target data lake/data warehouse using various pipelines like join, aggregation, calculation, and others to mimic SAP’s application logic. Hence, to avoid this difficulty and complexity, it is recommended to combine the two landscapes (flow 1+2+3) and benefit from the best on both sides. In the following sections we are summarizing technical approaches and some examples showing how to do this kind of federations by combining the best from the two sides.

Fig.1 data warehousing network flow

1.2. Get the Latest Co-innovation Closely from SAP & Microsoft through BTP to Multi-cloud

By having BTP in the landscape, RISE customers can easily stay aligned with the Clean Core Strategy, when doing extensions, integrations, and innovation through BTP. Even further, BTP can be used as a bridge of RISE with SAP Private Cloud Edition landscape (managed by SAP, on Azure, AWS, GCP, and SAP DC) to connect to their own hyperscaler subscription and reinvent their data strategy.

SAP BTP is a unified, business-centric, and open platform for the entire SAP ecosystem. With BTP on Azure, RISE customers can benefit from the latest close co-innovation between SAP and Microsoft. The integration scenarios with SAP through BTP cover a broad dimension:

  • connectivity: Azure Private Link Service & SAP Private Link with BTP on Azure. An enablement tutorial can be found here.

  • messaging: service bus, event hub, and event grid. An enablement tutorial can be found here.

  • monitoring: Azure Monitor, a tutorial on using Azure Monitor to monitor SAP BTP services can be found here.

  • security: with Microsoft Entra (previous Azure AD), and Microsoft Sentinel. See section 3.1.

  • integration: S/4 or SuccessFactors with Microsoft Teams integration through the Bridge framework; BTP Integration Suite with Azure Logic App integration tutorial can be found here

  • extension: with Azure-managed database services. a tutorial on integrating BTP and Azure CosmosDB can be found here.

  • extension with ABAP environment: an enablement tutorial can be found here.

Fig.2 System Landscape

2. Data Management Solutions Review

For SAP data management solutions, we did a review in this blog, ‘Unlock the Power of Business Data for SAP RISE Customers: Mastering Data Management and Cultivating ...‘. We followed the flow of how business data is generated in SAP landscape, then how it is been stored. Based on that, for analytical purposes, how could ETL jobs been done, and what is the approach to do BI, ML, and AI.

Below, we also do a review of Azure data management services on customer’s own Azure subscription. We will explain what the service essentially is and typically been used. in addition, will summarize how these services can be integrated with SAP data management.

Service name Service Cluster Remarks
Azure Data Factory ETL

  • cloud ETL service for scale-out serverless data integration and data transformation

  • provide an enriched category of connectors for both Azure services, and 3rd-party Databases, File Storages, APIs, Services, and Applications (including SAP)

Databases on Azure Database

  • offers 10+ Azure-managed database services such as relational, key-value, document, in-memory, etc.

  • also provides Azure managed Database servers (eg. HANA)

Azure Synapse Analytics Data Warehouse

  • analytics service that brings together enterprise data warehousing and Big Data analytics

Azure Data Lake Storage Data Lake

  • built on top of Azure Blob Storage

  • a set of capabilities dedicated to big data analytics

Power BI BI

  • data visualization solution with both desktop access and web access

Microsoft Fabric Analytics Suite

  • all-in-one analytics solution, which covers data movement, analytics and machine learning

AI Applications/Services ML/AI

Azure Machine Learning ML/AI

  • train and deploy models and manage the ML lifecycle (MLOps)

Azure Databricks ML/AI

Azure OpenAI Service ML/AI

  • provides REST API access to OpenAI's powerful language models including the GPT-4, GPT-35-Turbo, and Embeddings model series

  • users can access the service through REST APIs, Python SDK, or our web-based interface in the Azure OpenAI Studio

3. Enhanced capability in Multi-Cloud for RISE

3.1. Harmonized and Enhanced Enterprise Security with Microsoft Entra and Microsoft Sentinal

As written in this blog 'Harmonized Single Sign-On for SAP RISE Customers in Multi-Cloud Environment', in multi-cloud environments, there could be multiple identity providers (IDP), with each IDP designed for its own purpose with native integration with its own cluster of service providers. RISE with SAP customers would use SAP IDPs (BTP Cloud Identity Services, with/without SAP Single Sign-On) for SAP solutions, and SAP IDM (SAP GRC) to leverage security workflow management.

To complement the SAP landscape, by enabling Microsoft Entra (previously, Azure AD) in RISE customers' own Azure subscription, RISE customers can integrate Microsoft Entra with SAP IDPs through BTP Cloud Identity Services, so that end-users will feel seamless when working across the 2 landscapes' data management solutions. Moreover, Microsoft Entra is one of the most prevalent enterprise IDPs on the market, and provides integration with other hyperscaler IDPs (including with AWS IAM Identity Center, and with Google Cloud Identity), hence can be used in multi-cloud security 'hub and spoke' setup, as the central 'hub' IDP.

In addition, Microsoft Sentinel (SAP RISE certified), the cloud-native security information and event management (SIEM) platform, can be used to elevate and enhance enterprise security across platforms. With built-in AI to help analyze large volumes of data across platforms, Microsoft Sentinal can be used as a single console to monitor enterprise estate (both on SAP RISE, and on customer's own Azure subscription). An enablement tutorial about using pre-built playbooks for SOAR (security, orchestration, automation, and response) capabilities to react to threats quickly, could be referenced here.

3.2. Microsoft 365 integration

Microsoft 365 as one of the predominant enterprise productivity suites, provides entrenched integration with SAP solutions, from Microsoft Office and Microsoft Teams. Quite mentionable that, in data management solution area, there are integrations like, SAP Analytics Cloud, add-in for Microsoft Office; integrate SAP data through OData service into Microsoft Excel (a tutorial is available here).

3.3. Open AI

RISE customers could build enterprise-complaint ChatGPT-like services by enabling Azure OpenAI Services on their own Azure subscription. Azure OpenAI Service gives enterprise customers advanced large language model (LLM) with OpenAI GPT-4, GPT-3, Codex, and DALL-E models with the security and enterprise promise of Azure. SAP RISE customers can extend their S/4HANA in RISE landscape through BTP with Azure Open AI service. A tutorial can be found here.

In addition, RISE customers can use SAP GRC SAC (SAP Access Control) on their RISE subscription to govern and manage their security workflow, when different roles of enterprise users work around Azure Open AI services. An enablement guidance can be found here.

3.4. IoT Services

Azure offers a wide range of IoT services that enable customers to connect and manage their devices, collect and analyze data. Here is an overview of Azure IoT services.

A tutorial of Azure IoT service using Azure Cosmo DB service, then extend the BTP services can be found here.

4. Some Reference Architecture and Use Cases

This will be a continuous updating section

Use Case/Reference Architecture Link
Reduce your CO2 footprint using a smart Generative AI application on SAP BTP SAP Discovery Center
Integrating SAP Business Processes in Microsoft Teams using SAP BTP SAP Blog
Microsoft Azure Machine Learning for Supply Chain Planning SAP Blog
Connecting to IoT devices using Azure IoT Hub and visualizing the data using BTP and SAC SAP Blog
S/4HANA Extention through SAP BTP with Azure Open AI SAP Blog
empower SAP RISE enterprise users with Azure OpenAI in multi-cloud environment SAP Blog
Creating a meal generator application with SAP Build, Cloud Application Programming and OpenAI SAP Blog


  • The blog content does not necessarily represent the official opinion of SAP or Microsoft. The opinions appearing in this blog are backed by SAP or Microsoft documentation which can be revealed in the corresponding reference links.

  • SAP takes no responsibility for managing and operating customers’ own data center, nor for customers’ own hyperscaler subscription

  • SAP notes that posts about potential uses of generative AI and large language models are merely the individual poster’s ideas and opinions, and do not represent SAP’s official position or future development roadmap. SAP has no legal obligation or other commitment to pursue any course of business, or develop or release any functionality, mentioned in any post or related content on this website.

Acknowledgment to contributors/reviewers/advisors:

*knowledge is meant to be shared, and copyright matters

Ke Ma (a.k.a. Mark), co-author, Senior Cloud Architect, RISE Cloud Advisory RA group

Special THANK YOU to Microsoft colleagues from SAP on Azure PM team who co-wrote this blog:

Holger Bruchelt, Program Manager, Microsoft

Martin Pankraz, Program Manager, Microsoft

Bartosz Jarkowski, Program Manager, Microsoft

Prashanth Sayeenathan, Senior Specialist, Microsoft

Michael Truong Ngoc, Machine Learning Engineer, SAP IES AI CoE

Frank Gong, Digital Customer Engagement Manager, SAP ECS

Sven Bedorf, Co-head of Cloud Architecture & Advisory, RISE Cloud Advisory, MEE

Daniel Temming, Co-head of Cloud Architecture & Advisory, RISE Cloud Advisory, MEE

Kevin Flanagan, Head of Cloud Architecture & Advisory, RISE Cloud Advisory, EMEA North

Luc DUCOIN, Cloud Architect & Advisor, RISE Cloud Advisory

Richard Traut, Cloud Architect & Advisor, RISE Cloud Advisory