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Product and Topic Expert
Product and Topic Expert
updated date: 25.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 ERP and CRM, 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 AWS (Amazon Web Services).

Link for GCP: here.

Link for Azure: here.

For RISE with SAP customers, in multi-cloud environments, their own AWS subscription supports additional enterprise applications, such as Data Lake, Data Warehouse, Blockchain, Artificial Intelligence/Machine Learning, Internet of Things, and many others. These AWS capabilities complement RISE with SAP and SAP BTP to provide a holistic landscape that supports end-to-end business processes for enterprise customers.

When modernizing their SAP landscape into the cloud through RISE with SAP, and also having their own AWS subscription, customers start realizing the business benefits once they are able to combine their SAP business data federated within the SAP landscape and their other enterprise data in AWS Data Lake. This data federation can bring them new insights and possibilities for a new business model. This is why data management solutions will be the key to unlocking more success for RISE with SAP customers, and also having data management services on their own AWS subscription to complement their SAP landscape.

1. Value Proposition

1.1. Data Federation vs. Data Export

Data federation means keeping SAP business data within SAP data management solutions (data warehousing, BI, etc.), and when it comes to scenarios that involve intersection with multi-cloud environment data; Data export means exporting SAP business directly from SAP ERP and CRM to 3rd part data warehousing solutions and BI tools. Doing data federation has its significant values from the below perspectives.

1.1.1. Technical Advantage & Business Value

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 ERP & CRM will lose context, once left the SAP landscape. Meaning that when data is exported out of SAP Landscape, you will have to rebuild the context in the target data lake using various toolings like Join, Aggregation, Calculation, and others to mimic SAP’s Application Logic. This proves to be extremely difficult and expensive to manage given the complexity.

To give a specific example, data warehousing approaches, as shown in Fig. 1, flow 1+2+3 is recommended, in comparison to flow b. Because, flow b might look more direct, but will lead to heavy loads of redundant data engineering, which includes first rebuilding the context, then further massive transformation, aggregation, and summarization. Then, in the end, flow b will not only jeopardize the quality of SAP business data but also incurs massive redundant engineering and maintenance efforts.

1.1.2. Network Economics and Performance

We take a typical workflow around data warehouses as an example. The data federation approach (line 1+2+3), has a huge advantage over the data export approach (line b).

If RISE customers use BW or BW/4HANA as their main data warehouse, then the heaviest data replication takes place in flow 1, as it happens within the same virtual private cloud same availability zone, the network cost is ZERO. Then After the data is extracted, transformed, and loaded into BW or BW/4HANA, only a small amount of data needs to be replicated into SAP Datasphere in flow 2, and an even smaller amount of data to be federated into customers' own AWS for data federation purposes through flow 3.

The fact that flow b will contain full raw data export that requires not only heavy network traffic, but also massive data engineering (further massive transformation, aggregation, and summarization on top of rebuilding the context). It will be much more expensive to implement and maintain compared to flow 2+3. Though flow 2 and flow 3 incur a small amount of manageable network cost, still, compared to direct full export through flow b, which creates heavy traffic, doing data federation would save customers a lot of network traffic and have better network performance, and minimal data redundant data engineering.

Fig.1 data warehousing network flow

1.2. Value of RISE and BTP on Homogenous Infrastructure

By having BTP in their 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. As a PaaS solution, with Cloud Foundry as its dominant runtime environment, BTP runs on AWS as one of its predominant IaaS environments. Here are the advantages of BTP running on AWS.

RISE with SAP Private Cloud Edition can be hyperscaler-agnostic, meaning that SAP provides RISE customers network connectivity options from RISE landscape to their BTP subscriptions through SAP Cloud Connector, regardless of which hyperscaler their RISE landscape is on. Nevertheless, having RISE landscape and BTP hosted on homogenous infrastructure (which in this article, means that RISE on AWS and BTP on AWS), can give RISE customers additional benefits. These benefits include lower network latency, and lower total cost of ownership (TCO).

For RISE with SAP customers (especially if their RISE is on AWS), when their BTP is deployed on AWS, It is recommended that they utilize their own AWS subscriptions when they want AWS cloud-native services such as AWS IoT Services, AWS Data Lake, and others (a review is available in section 2), to complement SAP landscape as a whole.

1.3. SAP BTP as the Bridge for RISE Customers in Multi-cloud Environments

SAP BTP, as a unified, business-centric, and open platform, can service as the bridge for RISE customers to expand their SAP RISE landscape to the multi-cloud environment. This capability is backed by services on BTP including:

  • Integration Suite: API integration for both SAP and non-SAP scenarios

  • Extension Suite: extended customizing capabilities for SAP scenarios

  • Private Link Service: as a side-by-side connectivity option for SAP Cloud Connector, can give RISE customers additional security. Also provides seamless connection when consuming hyperscaler native PaaS services through it.

  • Event Mesh: is the backbone for event-driven development. Connect applications, services, and systems across different landscapes.

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 AWS data management services on customer's own AWS 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

  • serverless data integration service that makes it easier to discover, prepare, move, and integrate data from multiple sources for analytics, machine learning (ML), and application development

Amazon Appflow ETL

  • fully-managed integration service that helps securely transfer data between SaaS applications such as Salesforce, Google Analytics, Facebook Ads, and ServiceNow, and AWS services such as Amazon S3 and Amazon Redshift in just a few clicks

Amazon Kinesis ETL

  • serverless streaming data service that simplifies the capture, processing, and storage of data streams at any scale

Amazon Athena ETL

  • serverless, interactive analytics service built on open-source frameworks, supporting open-table and file formats

  • can be integrated with SAP DataSphere and SAP Analytics Cloud through Federated Query

AWS Managed Database Services Database

  • offers 15+ purpose-built engines to support diverse data models such as relational, key-value, document, in-memory, graph, time series, wide column, and ledger databases

  • can be integrated through Athena by leveraging the Federated Query mechanism to SAP DataSphere and SAP Analytics Cloud

Amazon Redshift Data Warehouse

  • fully-managed, cloud-native petabyte-scale data warehouse service

  • receives Data from S3 Data Lake through Redshift Spectrum

  • can be integrated through Athena by leveraging the Federated Query mechanism to SAP Data Sphere and SAP Analytics Cloud

Amazon Simple Storage Service (S3) Data Lake

  • object storage service that offers industry-leading scalability, data availability, security, and performance

  • can be filled with SAP Data through the use SAP Data Intelligence or AppFlow or Glue.

  • can also be integrated through Athena by leveraging the Federated Query mechanism to SAP DataSphere and SAP Analytics Cloud.

  • when SAP BTP is on AWS, customers can directly consume S3 through SAP BTP

AWS Lake Formation Data Lake

  • provides a relational database management system (RDBMS) permissions model to grant or revoke access to Data catalog resources such as databases, tables, and columns with underlying data in Amazon S3

Amazon Sagemaker ML / AI

  • managed service in the AWS public cloud. It provides the tools to build, train and deploy machine learning (ML) models for predictive analytics applications.

  • can be integrated with SAP DataSphere  through its Federated ML capability

Amazon Bedrock (Preview) ML / AI

  • fully managed service that makes Foundational Models (FMs) from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that's best suited for your use case

  • currently in preview

Databricks on AWS ML / AI

3. Enhanced capability in Multi-Cloud for RISE

By having their own AWS subscriptions in multi-cloud environments, RISE customers can be empowered with enhanced capabilities. Hereby, we list some phenomenal advantages below.

3.1. IoT (Internet of Things) data

AWS provides a comprehensive suite of IoT services that can help customers to build and scale their IoT applications with ease. AWS offers a wide range of IoT services that enable customers to connect and manage their devices, collect and analyze data, and build applications that interact with their devices. Here are some of the key AWS IoT services:

  • AWS IoT Core: provides secure and reliable communication between devices and the cloud, as well as device management and data processing capabilities.

  • AWS IoT Analytics: allows customers to collect, process, and analyze large amounts of IoT data in real-time. provides tools for data visualization, machine learning, and data storage

  • AWS IoT Device Management: helps customers to manage their IoT devices at scale, including onboarding, provisioning, and monitoring.

  • AWS IoT Greengrass: enables customers to run AWS services on their IoT devices, allowing them to process data locally and reduce latency.

  • AWS IoT Events: provides a way to detect and respond to events in real-time, such as device failures or anomalies in sensor data.

  • AWS IoT SiteWise: allows customers to collect, organize, and analyze data from industrial equipment at scale, helping them to optimize their operations and reduce downtime.

3.2. Federated Query and Federated Machine Learning

With the capability to do Federated Query and Federated Machine Learning between SAP BTP and customers' own AWS subscription, it provides the ability for customers to establish a complete Enterprise Data Lake which enables RISE customers to have a holistic view of their Business end-to-end. This will unlock new business insight and even open a new opportunity to develop new capabilities and offerings.

4. Some Reference Architecture and Use Cases

This will be a continuous updating section

Use Case/Reference Architecture Link
SAP BTP and AWS Joint Reference Architecture SAP Blog
Federated Query SAP Blog
Federated Machine Learning SAP Blog


  • The blog content does not necessarily represent the official opinion of SAP or Amazon Web Services. The opinions appearing in this blog are backed by SAP or AWS 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

Acknowledgment to contributors/reviewers/advisors:

Ferry Mulyadi, co-author, Principle Partner Solution Architect, Amazon Web Services

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

Dave Smith, Lead AWS Architect, SAP ECS

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

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

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

Luc DUCOIN, Cloud Architect & Advisor, RISE Cloud Advisory

Richard Traut, Cloud Architect & Advisor, RISE Cloud Advisory