Overview
The ancient philosopher Heraclitus of Ephesus (530-470 BC) is one of the most important thinkers in history. His philosophy is a good starting point for anyone concerned with change in life. Heraclitus said that life is like a river. The peaks and troughs, pits and swirls, are all are part of the ride. Do as Heraclitus would – go with the flow. Enjoy the ride, as wild as it may be.
SAP's recent announcements reveals a pivotal shift in the company, including the new strategy for SAP Business Technology Platform (aka SAP BTP), the business platform which is the foundation of the Intelligent Enterprise. RISE with SAP is also a bold change, a new business model for the market, the best concierge service that companies can get for their digital transformation.
Within this reinvented context, we invite you for an exciting mission: what about learning how to implement a complete “SAP BTP Showcase”, grounded on a real business case, using real data, thus elaborating an end-to-end demonstration flowing multiple SAP BTP products related to SAP HANA Database & Analytics Solutions in the Cloud?
Running all steps exposed in this blog, you should be able to:
- Load data from multiple sources in SAP HANA Cloud and SAP Data Warehouse Cloud
- Explore the data management journey on technical and business users' perspectives
- Explore cross data access among SAP products
- Implement automation with Continuous Integration & Deployment (CI/CD) pipelines
- Leverage Machine Learning capabilities as a data scientist
- Develop an SAP HANA Cloud Native Application
- Create nice dashboards and explore SAP's Augmented Analytics capabilities
Example of dashboard related to this blog
We have organized this content as a “Netflix” series! You can watch episodes at your own pace, you can choose which episodes to prioritize. The series streams on architected technical topics which makes sense to follow sequentially, but you can also pick-up specific episodes to elucidate individual technical subjects. So… grab your popcorn, keep your motivation, we are pioneering this exciting journey!
If you still don't have an SAP account for starting your developments, don't worry... SAP is providing you with a completely free SAP trial account, so you can join our network and get access to all SAP BTP solutions (SAP HANA Cloud. SAP Data Warehouse Cloud, SAP Analytics Cloud) required for you to implement all projects & artifacts presented in this blog, except steps proposed in Blog 2: Access the SAP HANA Cloud database underneath SAP Data Warehouse Cloud, which require opening a ticket at SAP, what's not yet available for trial accounts.
We invite you to join us on saphanajourney.com, an interesting landing page plenty of information about learning, resources and details about SAP BTP portfolio.
The Business Case
Digital Transformation and the continuous rise of cloud computing led to an enormous growth in the amount and variety of data. The following scenario shows how our latest SAP HANA Database & Analytics Solutions in the Cloud, as part of SAP BTP, can help to tackle modern challenges. To demonstrate a common scenario that data leaders face, let’s have a look at a company that aims to optimize its production plans.
Generally, companies use multiple systems in order to store and process operational data, to archive historical data or to derive planning values. Business users are then maintaining Excel spreadsheets to persist, harmonize and transform data before visualizing insights with an analytics tool.
Traditional Analytics Landscape
All these steps – and even more - can be simplified in a safe and reliable environment by leveraging SAP HANA Database and Analytics Solutions in the Cloud.
In our scenario, different datasets are distributed across multiple SAP and Non-SAP Source Systems. Actual sales and production data are stored in SAP ERP while additional data values are derived on an SAP HANA Cloud native business application on top of the SAP HANA database, which provides powerful transactional and analytical capabilities. Historical sales data is stored on an Amazon S3 file system. Machine Learning capabilities are leveraged to predict future sales values, using SAP HANA Predictive Analysis Library.
SAP Data Warehouse Cloud’s data modeling capabilities are utilized to combine different datasets from SAP and Non-SAP Source Systems which were previously outlined. Without further assistance from IT, SAP Data Warehouse Cloud’s intuitive user interface and its business-friendly terminology empower business users to access, persist and transform data from remote sources in real-time.
Finally, the ability to seamlessly connect SAP Analytics Cloud to SAP Data Warehouse Cloud allows business users to create dashboards without replicating data. In our scenario, SAP Analytics Cloud serves as the analytics platform for enterprise-wide self-service and actionable, real time business insights to visualize actual sales and production values as well as forecasts. By comparing the company’s actual & planned production with the predicted sales values, it is possible to identify potential deviations and necessary adjustments. By reducing unnecessary production as well as underproduction, the company’s crucial production plans can be optimized which directly influences the company’s financials.
Overall Technical Architecture
So, let’s start showcasing an overall view about the technical architecture proposed by SAP for this business challenge:
Overall Technical Architecture proposed by SAP
This showcase is based on a set of featured SAP Solutions, like SAP HANA Cloud, SAP Data Warehouse Cloud, SAP Analytics Cloud, and other SAP BTP services like ObjectStore, Destinations, SAP Business Application Studio, SAP Continuous Integration and Delivery, etc.
The architecture supports an end-to-end technical demonstration, integrating multiple SAP solutions, also leveraging Continuous Integration & Delivery (CI/CD) concepts for SAP HANA Cloud.
For source data, we are using a real dataset representing Germany’s countrywide energy consumption for the past 5 years. We have downloaded csv files from the open website https://www.smard.de/, and used them as sources to our demo.
All developments were made available on public reusable GitHub repositories for you to leverage during your practice.
Solution Map
Now, let's zoom on the specific components and organize an easy-to-follow "Solution map", in 7 steps, highlighted in "pink", as represented below:
Solution Map: 7 linked blogs
We have prepared multiple specific use-cases, and we will scrutinize their technical intricacies in the following additional blogs & videos, which are organized as follows:
Blog 1: Load data into SAP Data Warehouse Cloud: how to easily consume data from systems of records (e.g. SAP ERP), cloud and on-premise databases (e.g. SAP HANA, SQLServer, Oracle, Athena, Redshift, BigQuery, etc.), oData Services, csv/text files available in your own local computer, or any File/Object store (e.g. Amazon S3). We will leverage SAP Data Warehouse Cloud’s Replication and Data Flow capabilities, as well as demonstrate how to access remote sources using data virtualization.
Blog 2: Access the SAP HANA Cloud database underneath SAP Data Warehouse Cloud: how to create an SAP HANA Deployment Infrastructure (aka HDI) container on SAP HANA Cloud, and persist actual sales data originated from an external system in the same SAP Data Warehouse Cloud’s existing persistence area. We will show how to provide bi-directional access between SAP Data Warehouse Cloud and SAP HANA Cloud’s managed datasets. You will also see how to expose SAP HANA Cloud & SAP Data Warehouse Cloud’s artifacts, like a table or a Graphical View, as oData services. You should also take a look on this additional blog, which provides hands-on instructions for exposing SAP Data Warehouse Cloud artifacts as oData services and a complete Git repository to kick-start implementation.
Blog 3: SAP Continuous Integration & Delivery (CI/CD) for SAP HANA Cloud: how to develop and trigger a pipeline using either Jenkins or SAP Continuous Integration and Delivery for automating the deploy of the above SAP HANA Cloud application on multi-target (DEV/PRD) landscapes.
Blog 4: Run future sales prediction using SAP HANA Cloud Machine Learning algorithms: how to create an SAP HANA Cloud HDI container, load training and testing historical data, and run Predictive Analytics Library (PAL) procedures for just-in-time predicting future sales (energy consumption) values.
Blog 5: Develop a SAP HANA Cloud native application: how to create a SAP Cloud Application Programming Model project, which will manage additional data values, working on the back-end application (HDI providing oData services) as well as the front-end SAP Fiori/SAPUI5 application + an AppGyver application that is capable of leveraging enterprise capabilities, deployed on dedicated services in SAP BTP.
Blog 6: Provide governed business semantics with SAP Data Warehouse Cloud: how to consume all of the multiple data sources referenced in the blog, enabling business users with self-service data modeling, harmonization, transformation and persistence.
Blog 7: Consume SAP Data Warehouse Cloud’s assets using SAP Analytics Cloud: how to provide self-service business insights to the business community. We will also demonstrate how to use SAP Analytics Cloud’s Smart Insights and Smart Discovery augmented analytics smart features.
This blog is also available at saphanajourney.
Conclusion
When following each of the blogs presented in the earlier section, you will be able to implement a complete end-to-end project, integrating multiple SAP BTP products, leveraging some sophisticated technical features not yet widely known in SAP ecosystem.
Let’s exercise on the contents of this blog and expand knowledge in SAP Business Technology Platform.
All of your feedback is appreciated. Enjoy!
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