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This blog post will describe an end-to-end scenario, demonstrating how to capture and manage data and create advanced insights by utilising the powerful capabilities of SAP HANA Database & Analytics components in SAP Business Technology Platform (SAP BTP).

The motivation for creating this blog post is to show different capabilities provided within SAP HANA Database & Analytics portfolios and how to combine them to build 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:

  • Establish a centralised data layer accessing data from multiple non-SAP sources

  • Improve and monitor data quality to build a robust and agile foundation for data modelling and analytics activities

  • Simplify geographical data modelling in a typical data-warehouse scenario

  • Extend data-warehouse scenarios with prediction data from Machine Learning models

  • Analyse data and accelerate insights by interactive and analytical dashboards

Data-to-value showcase

Getting value from data is a journey, and we understand organisations are facing various challenges when building their own data strategy. We'd like to utilise this use case to illustrate how our unified data & analytics solutions support you resolve the following challenges and bring value-driven results.

Before: Challenges and Opportunities

  • Lack of integration between data platforms and analytics platforms and high manual efforts for integrating platforms to generate amount of organisational benefits

  • Less flexibility for IT people to connect and harmonise critical data of different formats from multiple sources into one single data layer

  • Limited access to data and less participation by business people, e.g., Chief Financial Officer or Business Analyst, to build their own reports and conduct analytics

  • Missing approach to empower collaboration between IT and business and serve the needs of different personas across the enterprise

After: Value-Driven Results

  • Unified solutions covering data management and analytics end-to-end and reducing manual efforts for integration: Our portfolio provides capabilities, supporting key scenarios in your end-to-end data journey, such as data integration & data quality, database as a service, data warehousing and analytics

  • Bring flexibility to IT people: IT staff can choose their favourite programming languages (python in this use case), develop data pipelines and establish a centralised data layer, integrating data of different formats (e.g., JSON and CSV in this use case) from multiple sources (e.g., REST APIs)

  • Bring self-service data modelling and analytics to business users: Business users are able to build data models and analytics reports by their own, through the easy-to-use user interfaces and space concept offered by our solutions. For instance, the new geo-enhanced data modelling feature demonstrated in this blog post converts geographical data (latitude and longitude) into HANA ST_POINT by a simple user interface, compared to the traditional approach -  writing SQL script

  • Bring collaboration across the enterprise and better decision-makings back by data: The toolsets offered in our unified data & analytics portfolio fulfill requirements from different personas, e.g., business analysts, data scientists, data engineers and administrators and thus empower enterprise-wide collaboration

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

This scenario illustrates how Data Engineer Karl utilises 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

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 using historical price data, predicting gasoline prices in future time periods. 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

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 and price changes (based on historical price 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.

Solution map and implementation

To better understand how SAP BTP leverages capabilities of various SAP HANA Database & Analytics components 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.

Solution map 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.meier, jonasmittenbuehler, lukas-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.