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 single 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
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.
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.
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. 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 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.
- Blog 1: How to load data into SAP HANA Cloud using SAP Data Intelligence
- Blog 2: How to improve and monitor data quality using SAP Data Intelligence (blog post is coming soon!)
- Blog 3: How to prepare geospatial data in SAP Data Warehouse Cloud
- Blog 4: How to create machine learning models and run predictive analysis in SAP Data Warehouse Cloud (blog post is coming soon!)
- Blog 5: How to visualise geospatial data from SAP Data Warehouse Cloud in SAP Analytics 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!
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.