This blog post will describe an end-to-end scenario demonstrating the integration among multiple SAP BTP products: SAP Data Intelligence, SAP HANA Cloud, SAP Data Warehouse Cloud and SAP Analytics Cloud related to SAP HANA Database & Analytics Solutions in the Cloud.

The motivation for creating this blog post is to show, how to leverage capabilities of different HANA Database & Analytics products and implement an end-to-end “data to value” showcase in practice, using real-world data. We are working continuously to extend our scenarios and blog posts, so the content is organised in a series of sub-blog posts.

Following the upcoming sessions, you should be able to:

  • Integrate data from multiple sources into SAP HANA Cloud (HDI Container) using SAP Data Intelligence
  • Improve and monitor data quality to support business and technical users in modelling and analysing data
  • Explore geographical data modelling using SAP Data Warehouse Cloud
  • Extend data-warehouse scenarios with enabling HANA Machine Learning capabilities in SAP Data Warehouse Cloud
  • Establish interactive and analytical dashboards in SAP Analytics Cloud

Data-to-value showcase


We hope this “SAP BTP Data & Analytics Showcase” helps bring the below values:

  • Create and implement your own end-to-end data-to-value story
  • Reduce the implementation time through seamless integration among multiple SAP HANA Database & Analytics Solutions in the cloud
  • Save costs for your organisation, e.g., fuel costs for logistic companies

Example of dashboard related to this blog

End-to-end demo video related to this blog

Use case and persona

The use case designed in this article is to show how customers could benefit from the seamless integration among different SAP HANA Database & Analytics Solutions in the cloud, combining multiple data sources of various types (e.g., via REST APIs), ensuring high-quality data and generating business insights faster.

Data Model

For this purpose, we choose a data model from one open website called “Tankerkönig“, where we could get the gasoline stations data in Germany and corresponding historical gasoline prices data (namely CSV files), and real-time gasoline prices data via REST APIs. We use the stations and prices data within this website for blog posting and demonstration purpose only.


To demonstrate user needs and identify features of SAP HANA Database & Analytics Solutions, the following four types of personas are assumed in this end-to-end scenario.

Persona definition related to this blog


Based on the described data model and persona definition, three scenarios are defined and implemented.

Scenario 1: Non-SAP data integration and preparation using SAP Data Intelligence

This scenario illustrates how Data Engineer Karl utilizes SAP Data Intelligence to load non-SAP data rapidly into SAP HANA Cloud (HDI Container) and manage data quality. The integration between SAP HANA Cloud and SAP Data Intelligence enables this prototype, which would be the agile preparation for further productive implementations in SAP Data Warehouse Cloud. The following tools in SAP Data Intelligence is put to use:

  • DI internal data lake to store non-SAP data namely CSV files
  • Data ingestion pipelines to load CSV files and connect REST APIs
  • Data quality improvement and monitoring via defined rules

SAP Data Intelligence product architecture provided by SAP HANA Database & Analytics

Scenario 2: Geographical data modelling and machine learning model creation using SAP Data Warehouse Cloud

In this scenario, BI Modeler Daniel would establish BI models using SAP Data Warehouse Cloud, based on the data acquired from Tankerkönig website and stored in SAP HANA Cloud. These BI models are used to demonstrate how real-time gasoline prices change with various geographical regions in Germany later in SAP Analytics Cloud.

Additionally, Data Scientist Susan could consume HANA-embedded Machine Learning algorithms via python in Jupyter Notebook, where connection to SAP Data Warehouse Cloud is established, and create machine learning models. This scenario is fully supported by the new integration feature between SAP HANA Cloud and SAP Data Warehouse Cloud – SAP HANA Cloud script server enablement for machine learning.

Scenario 3: Geographical data visualisation and analysis using SAP Analytics Cloud

This scenario shows how BI modeler Daniel would create an interactive and analytical dashboard in SAP Analytics Cloud, which consumes data models from SAP Data Warehouse Cloud and generate business insights from real-time prices data for business users faster. The seamless integration between SAP Data Warehouse Cloud and SAP Analytics Cloud makes the implementation possible.

*For all the three scenarios described, Administrator Peter needs to configure the connections among different SAP HANA Database & Analytics products.

Technical architecture and implementation

To better understand how SAP BTP leverages capabilities of various SAP HANA Database & Analytics Solutions in the cloud and offers a hybrid data platform for an end-to-end data fabric to drive business outcomes, let’s have a look at the below technical architecture. Furthermore, the corresponding implementations are also described in a series of separate blog posts, along the numbers marked in the architecture diagram.

Technical Architecture proposed by SAP and Implementation linked to blog posts

We have prepared the following blog posts which would explain more implementation details for multiple specific use cases or scenarios using SAP HANA Database & Analytics Solutions in the cloud.


We hope this blog post could give you a comprehensive overview about the integration among multiple SAP BTP products related to SAP HANA Database & Analytics Solutions in the Cloud (SAP Data Intelligence/SAP HANA Cloud/SAP Data Warehouse Cloud/SAP Analytics Cloud). Based on this context, you will be able to build your own end-to-end “data to value” story. Thank you for your time, and please stay tuned and curious about our upcoming blog posts!

At the very end, I would like to say thank you to my colleagues Axel MeierJonas MittenbuehlerLukas Schoemig and Abassin Sidiq to help make this end-to-end demo story happen together!

We highly appreciate for all your feedbacks and comments! In case you have any questions, please do not hesitate to ask in the Q&A area as well.


Randa Khaled

Randa Khaled

Author Since: November 19, 2020

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