SAP Community - SAP HANA Cloud

Innovating Towards a Sustainable Future: Insights from Rolf Adam

2024-06-20T14:49:27.847000+02:00

Staying Grounded in SAP Excellence

Reflecting on the industry's deep-rooted reliance on SAP, Rolf Adam underscores the steadfast commitment of major oil and gas companies to SAP solutions. With a legacy of nearly four decades, SAP remains the backbone for optimizing operations and navigating the industry's intricate processes efficiently. As the sector transitions towards renewable energy sources like Sustainable Aviation Fuel (SAF), SAP's "Clean Core" strategy and future-forward solutions, such as S/4HANA, offer a pathway for driving sustainable innovation and operational resilience.

Partnering for Success: A Collaborative Approach

Implico's alignment with SAP's product roadmap and commitment to technological innovation exemplify the successful partnership dynamics crucial for supporting industry needs. By leveraging SAP's market-leading solutions like SAP SDM and RFNO, Implico sets industry benchmarks in downstream business operations, equipping customers with cutting-edge tools and insights for enhanced performance.

Embracing Cloud Technologies: A Strategic Imperative

Amidst the industry's evolving landscape, the discussion around cloud adoption and licenses surfaces as a critical consideration. As industry dynamics necessitate the adoption of cloud-based solutions, Implico's strategic approach to cloud integration underscores a commitment to agile and scalable business models, paving the way for streamlined processes and enhanced customer experiences.

Bridging Innovation with Business Value: The Role of SAP BTP

SAP's Business Technology Platform (BTP) emerges as a catalyst for future-proofing solutions and unlocking new realms of innovation. Positioned as a playground for customization and add-ons, BTP empowers companies like Implico to extend their offerings and deliver tailor-made solutions that align with industry demands. By embracing AI technologies and leveraging the scalability of BTP, Implico charts a course towards continuous innovation and customer-centric excellence.

Charting the Future Course: A Confluence of Expertise and Vision

As the industry strides towards a future marked by sustainability, digitalization, and transformative growth, the insights shared by Rolf Adam offer a compelling narrative on SAP's pivotal role in shaping the oil, gas, and energy landscape. By staying rooted in industry expertise, fostering collaborative partnerships, and embracing cutting-edge technologies like BTP, companies like Implico exemplify a commitment to driving industry-wide progress and innovation.

Conclusion

The interview with Rolf Adam serves as a testament to the industry's resilience, adaptability, and unwavering commitment to excellence amidst transformative industry shifts. As SAP solutions continue to evolve, empowering businesses to embrace sustainability, digitalization, and efficiency, the oil, gas, and energy sector stands poised at the cusp of a new era of innovation, growth, and sustainable progress.

Find the full interview here: https://www.implico.com/news_posts/e3-magazin/

SAP S/4HANA SDM: This tool offers a cohesive approach to the trade and transport of liquids and half-solids, integrating seamlessly with the SAP system to streamline processes from demand forecasting to invoicing, thereby facilitating smoother operations in hydrocarbon product trading.

SAP S/4HANA RFNO: A dedicated solution for managing service station networks, it caters to various service station types, offering functionalities like wet goods management and loyalty card management, aiming to enhance the fuel retail experience.

Implico’s Quality Assurance Program for SAP solutions: Implico wants to ensure the implementation of SAP SDM/RFNO solutions is professional and error-free – not only when doing so ourselves but also when external companies are contracted to oversee implementation. This is why we also offer two partner services that are designed to supplement the implementation projects based on a methodological approach and with highly skilled consultants, both of which help to achieve the defined targets: the Partner Governance Service and the Partner Advisor Service.

New Machine Learning features in SAP HANA Cloud database release 2024 Q2

2024-06-24T09:23:43.449000+02:00

With the 2024 Q2 database release, several new features have been released in the SAP HANA Cloud Predictive Analysis Library (PAL), an enhancement summary is available in the What’s new document for SAP HANA Cloud database 2024.14 (QRC 2/2024).

The feature highlights for the current release are described in more detail below

Classification and Regression enhancements

A new multi-task multilayer perceptron (MLP) function is introduced, enabling multi-label classification or multi-target regression scenarios. Using a multi-task learning neural network, a single ML model allows to predict multiple, related target columns at once, as the model captures both common features across tasks as well as task-specific information with the same prediction model.

It leverages the commonalities between related tasks to improve the performance,
generalization, and training efficiency, and moreover enables efficient use of data,
better feature extraction, knowledge transfer, regularization, and end-to-end learning.
Furthermore, the function supports early stopping using validation data to avoid overfitting.
Users of the new multi-task MLP also benefit from shorter MLP training times
and models with improved accuracy.

The new function provides unique prediction model capabilities for example scenarios like

automated multi-field value proposals or pre-filling of forms
(e.g. Sales Order Automation)
or predicting multiple price-/sales-targets (average, minimum, etc.)
in a single model.

Multi-target predictions using the multi-task MLP function

For a more detailed introduction to the new algorithm see the following blog post Advancing to Multi-task Multilayer Perceptron: a new Neural Network design in HANA Machine Learning .

In further improve regression models, a new outlier detection for regression function is added,

providing the ability to detect point outliers in data used when training linear or tree-based regression models using MLR and HGBT regressors.
Outliers in training data get identified based on residual analysis and outlier-score evaluation, then can be excluded for model training and allowing to build improved regression model predictions.

Regression outlier detection

Text Processing

A new ML model-based text classification function is introduced.
It leverages

Random Decision Tree (RDT) models underneath,
aids much faster classification results, especially in case of classifying high numbers of new text documents
as well as providing a significantly improved text classification accuracy.

A detailed introduction to this new function is provided in the following blog post Inference Acceleration - Random Decision Tree Models for Text Classification.

Python ML client (hana-ml) enhancements

The full list of new methods and enhancements with hana_ml 2.21 is summarized in the changelog for hana-ml 2.21.240618 as part of the documentation. The key enhancements in this release include

Dataframe methods

create_dataframe_from_pandas now fully supports creating and upserting to SAP HANA Cloud tables with columns of type REAL_VECTOR, thus vector embeddings prepared in pandas dataframe can easily be imported in SAP HANA Cloud

AutoML configuration and methods enhancements

Finetuning option of the best pipeline in the AutoML scenario
Pipeline explainability enhanced with SHAPGlobal surrogate, a light-weight model for faster explanation of AutoML and pipeline model prediction results
Visual editor support for the AutoML scenario configuration:

Text processing

New text classification with model function, supporting RDT-based text classification
Massive Text Mining, implicit parallel analysis of multiple row vectors

Financial analysis methods

New Hull White simulation function, a trending financial math function used in modeling interest rates, vital in modeling of various financial instruments and risk
Benford Analysis function, trending algorithm used to detect anomalies in numerical datasets like e.g. financial transactions

You can find a Python Jupyter notebook example illustrating the highlighted feature enhancements here 24QRC02_2.21.ipynb.

SAP CodeJam HANA ML In 's-Hertogenbosch 2024-06 Recap

2024-06-24T18:17:00.131000+02:00

In mid-June, we held the Getting Started with Machine Learning using SAP HANA and Python in 's-Hertogenbosch, the Netherlands.

It was kindly hosted by iTrainee and organized by Mientje and @Jan-WillemK from the Vereniging Nederlandstalige SAP-Gebruikers (VNSG).

It was a mix of some theory...

...and hands-on.

Right after the SAP CodeJam @tbroek , @jpenninkhof , @rakeshjain and truly yours recorded another episode of the HANA Café NL podcast: https://youtu.be/Qm9zEeX6J88?si=XV5FoCvwkDUg6xnX

I would like to extend special thanks to @OnnoSchoenmaker and @Jan-WillemK for a tour of the city and the introduction to the importance of cheese in Dutch cuisine 🧀

Now, the next stop is Berlin on June 28th hosted by @rofinke : https://community.sap.com/t5/sap-codejam/getting-started-with-machine-learning-using-sap-hana-berlin-de/ev-p/13703853

Would you like to host such an SAP CodeJam? Here are the details: https://community.sap.com/t5/technology-blogs-by-sap/quot-getting-started-with-machine-learning-using-sap-hana-quot-as-a-new-sap/ba-p/13574098

What’s New in SAP HANA Cloud – June 2024

2024-06-24T18:38:47.809000+02:00

The first half of the year 2024 is almost over, which means we are happy to announce that the second release of SAP HANA Cloud in 2024 is live now!

Before diving deeper into the broad variety of innovations included, let me share that if you want to hear about the innovations from our product experts first-hand, including demos, make sure to watch our What’s New in SAP HANA Cloud webcast, soon to be uploaded on our YouTube channel, or take a look at my top features for the recent release in less than 5 minutes.

Innovations in SAP HANA Cloud, SAP HANA Database

Administration and Service Management

Support for larger instances

With the second quarterly release of SAP HANA Cloud in 2024, we are adding support for large SAP HANA database instances with up to 12 TB of memory. This doubles the amount of memory you can allocate to your database instances in SAP HANA Cloud.

Many customers have large on-prem SAP HANA database instances and are looking to migrate them to the cloud. With the support for larger instances, SAP HANA Cloud can accommodate these large data volumes. Additionally, you gain the flexibility to scale up your existing cloud database instances as your memory requirements increase over time.

The large instances can be provisioned on demand through the performance class High Memory (SAP HANA Cloud offers four performance classes with different core-memory ratios for sizing database instances for different workloads.) High Memory is suitable for memory-intensive workloads like the ones we are targeting with these large instances.

You can provision a large SAP HANA database instance on the main cloud service providers: Amazon Web Services, Microsoft Azure, and Google Cloud Platform. The actual configuration (i.e., the actual amount of memory and processing power) varies depending on the hyperscaler. Initially, large instances are available in selected regions. The available configurations are as follows:

Initial regional availability

AWS

9 TB
442 vCPU

12 TB

442 vCPU

EU10

US10

Azure

7,44 TB

412 vCPU

11,15 TB

412 vCPU

EU20

US20

GCP

8,63 TB

412 vCPU

11,52

412 vCPUs

EU30

US30

Upsizing existing instances to large instances can be done by restarting the instance or without a restart for near-zero downtime. You will be required to apply changes without a restart if the storage size increases beyond a hyperscaler specific boundary, that is currently 16TB on AWS and 64TB on Azure and GCP.

Managing database instances in a Cloud Foundry space, from the multi-environment tools

SAP HANA Cloud now enables the management of both Cloud Foundry and "subaccount" database instances using the multi-environment tools. This enhancement includes the ability for developers or administrators to create and delete SAP HANA Cloud instances in a Cloud Foundry space, as well as to facilitate application development from SAP Business Application Studio with runtime objects deployed through the broker of SAP HANA Infrastructure Services to an SAP HANA Cloud instance accessed through a Cloud Foundry space.

The management of database instances in a Cloud Foundry space using multi-environment tools simplifies and enhances the user experience by providing a single set of administration tools. This allows users to manage instances of SAP HANA Cloud running in both the “subaccount runtime environment” and Cloud Foundry, offering flexibility in choosing the runtime environment that best fits the user's needs. This support for Cloud Foundry, Kyma, and the “subaccount runtime environment” enhances the versatility and scalability of SAP HANA Cloud, catering to diverse deployment requirements

Management of multiple variant SQL plans based on different parameter values

The latest QRC introduces the capability to cache different SQL execution plans based on parameter values that cause variant SQL execution plans for HEX. This innovation enables the storage and caching of multiple variant SQL execution plans, optimizing SQL execution performance for various parameter values.

This functionality helps to avoid performance loss resulting from skewed data, ensuring that SQL execution remains efficient and effective across different parameter values, ultimately enhancing the overall performance and stability of SQL query execution in SAP HANA.

Integration of catalog object browsing in the administration tool

SAP HANA Cloud now integrates catalog object browsing into our central database administration tool, introducing a new "database objects" application that displays tables, views, indexes, functions, procedures, and schemas. Additionally, users can access their favorite and recently accessed catalog objects within the tool, streamlining the browsing and management of database objects.

This integration enhances productivity by consolidating catalog object functionality into a single administration tool, simplifying the user interface for browsing database objects. By providing a unified environment, this innovation improves the user experience and efficiency in navigating and interacting with the database catalog, ultimately enhancing the user's ability to effectively manage database objects within SAP HANA Cloud.

Consumption monitor for visualizing unit consumption

Additionally, with the second delivery of SAP HANA Cloud in 2024, we introduce a consumption monitor for visualizing the compute unit consumption of instances. This feature also is a new card in the administration and monitoring tool and displays historical, estimate and actual values for consumption, offering daily and detailed consumption information related to memory, compute, storage, and network data transfer.

The consumption monitor harmonizes and simplifies access to consumption-related data, delivering transparent and structured insight into consumption metrics. By providing clear visualization and detailed information on unit consumption, it enables users to predict consumption through simple forecasting, empowering them to make informed decisions and optimize resource utilization within SAP HANA Cloud. This feature offers users better control over resource management and expenditure, ultimately enhancing cost-effectiveness and performance optimization within the database.

SAP HANA Cloud Multi-Model Data Processing

Multilabel classification and outlier detection in machine learning

With the 2024 Q2 database release, several new features have been released in the SAP HANA Cloud Predictive Analysis Library (PAL) , prepared in detail in this blog [New Machine Learning features in SAP HANA Cloud database release 2024 Q2 - SAP Community].

A new multilayer perceptron neural network function for multi-task prediction scenarios, i.e. multi-label classification or multi-target regressions. With a single neural network model, multiple related target columns can be predicted at once, capturing both the common features across tasks as well as the task-specific information providing unique predictions for scenarios like automated multi-field value proposals or pre-filling of forms like in Sales Order Automation.

Other improvements include enhanced outlier detection for regression scenarios, improved performance with Random Decision Tree-based text classification and AutoML scenario configuration UI and improved pipeline explainability support with the Python Machine Learning client release 2.21

Spatial function to measure the similarity between two shapes

Implementing the new spatial function ST_HausdorffDistance in SAP HANA Cloud enables users to compare and analyze spatial relationships between two sets of geographic features, such as points, lines, or polygons. This new feature provides a powerful tool for spatial data analysis, allowing users to gain valuable insights into the similarities and differences between various geographic shapes, including curves, bends, and irregularities.

The new spatial function enhances the reliability of working with real-world data, which often contains imperfections. Users can now conduct in-depth analysis and comparison of entire shapes, enabling them to make more accurate and informed decisions based on their spatial data. It also empowers users to gain deeper insights and a more comprehensive understanding of their geographic data, ultimately leading to more effective and impactful decision-making.

Database Migration

Extension of SAP Business Application Studio to support design-time object migrations

The extension of SAP Business Application Studio (BAS) to support design-time object migrations provides users with a seamless and automated solution for migrating SAP HANA extended application services to SAP Business Technology Platform (SAP BTP) and SAP Cloud Application Programming Model (CAP). This feature streamlines the migration process by supporting the conversion of design-time artifacts from SAP HANA to SAP HANA Cloud, making it easier for users to ensure compliance with the SAP CAP.

The extension of SAP Business Application Studio offers significant benefits to users by automating the conversion of design-time artifacts, reducing the time and effort required for migration, and ensuring compliance with the SAP CAP. This innovation provides a flexible and convenient alternative for migrating SAP HANA extended application services to SAP HANA Cloud without the need for an on-premise license, empowering users to efficiently and effectively manage their migration efforts.

Insights into migration duration

Next up, we are now providing insights into the average migration duration for comparable scenarios. This offers users valuable information to better understand and plan their database migration efforts. The feature enables users to gain visibility into the approximate amount of time needed to migrate a similar database, improving transparency and enhancing the predictability of the migration process. It enhances user experience by offering greater predictability and control over the migration process.

Innovations in SAP HANA Cloud, data lake

Support for the cloud connector for the data lake component

With the latest update, we now offer support for the SAP Cloud Connector for the SAP HANA Cloud, data lake. This new capability enables users to create remote server connections from the SAP HANA Cloud data lake relational engine to on-premise databases of SAP HANA using the Cloud Connector. This integration allows for seamless and secure data exchange between on-premise and cloud environments, facilitating efficient data access and analysis across distributed systems.

The addition of the cloud connector support for the SAP HANA Cloud, data lake enables users to import data directly into the data lake relational engine or execute federated queries against the on-premise data source. This streamlines the data integration process, empowering users to leverage a unified platform for data management and analysis. By bridging on-premise and cloud databases, this innovation enhances the accessibility, flexibility, and usability of data within the SAP HANA Cloud environment, ultimately improving productivity and decision-making

Further Innovations & Enhancements

Database Application Development

Ability to self-tune SQL performance with the SQL plan advisor

In this quarter's update, SAP HANA Cloud introduces the ability to self-tune SQL performance with the SQL plan advisor. This feature includes reporting on fluctuating execution times for SQL statements and an automated comparison of execution performance tests to activate self-tuning. Additionally, it enables the system to automatically apply the tuned SQL plans, thereby optimizing the performance of SQL queries within the SAP HANA Cloud environment.

The self-tuning capability brings significant benefits to users by allowing them to intuitively identify problematic performance of deviating SQL statements. By leveraging automatic comparison of different plan results and adjustment of future plans, SAP HANA Cloud minimizes the need for manual intervention, saving time and effort in optimizing SQL performance. This innovation ensures that users can consistently achieve optimal query performance, enhancing overall system efficiency and user experience.

Adding the capability to create fabric virtual tables in SAP HANA deployment infrastructure

Additionally, the capability to create fabric virtual tables within an SAP HANA deployment infrastructure (HDI) container has been introduced. This enhancement enables users to seamlessly integrate fabric virtual tables into their SAP HANA deployment infrastructure, providing a simplified and efficient method for accessing and utilizing data without the need for schemas. Moreover, it empowers users to leverage performance optimizations on remote tables, enhancing data processing and analysis capabilities.

This innovation simplifies the process of utilizing fabric virtual tables without the requirement of using schemas, streamlining the data access and manipulation process. Furthermore, it enables users to harness performance optimizations on remote tables, leading to improved efficiency and speed in data operations. This innovation aims to enhance the overall user experience and increase productivity within the SAP HANA Cloud.

Graphical editor for hdbgrants and hdbrevokes files in SAP Business Application Studio

The latest update in the SAP Business Application Studio (BAS) introduces a graphical editor for hdbgrants and hdbrevokes files, providing an intuitive and user-friendly interface for managing privileges. This graphical editor offers ease of use in adding necessary privileges using an hdbgrants file by selecting from a list of available options, gathering granting services from the mta.yaml file of the project, cloning privileges from the object owner to the application user, bidirectional modification support with instantaneous changes, as well as error handling and reporting capabilities.

The graphical editor for hdbgrants and hdbrevokes files in SAP BAS delivers substantial value to users by providing a UI-based option to add or modify privileges for accessing SAP HANA Deployment Infrastructure services. It significantly reduces the development time by simplifying and streamlining the process of privilege management. Additionally, it facilitates intuitive adoption by developers, enhancing productivity and empowering users to effectively manage privileges within SAP HANA Cloud.

Graphical View Modeling

As our loyal readers might expect, we have also provided numerous enhancement and new functionalities to our solution in regard to its graphical view modeling capabilities. You can find all details in the comprehensive blog post by Jan Zwickel.

Are you looking for more technical details on the latest release of SAP HANA Cloud? Then, take a look at our What’s New Viewer in our Technical Documentation, which describes the entire scope of the release.

Finally, make sure to follow the SAP HANA Cloud tag to stay updated on all the latest developments. You can also check out the whatsnewinsaphanacloud tag for new blog posts. If you want to watch the What’s New webinar in Q2 2024, you can find it and all future webinars soon in this YouTube playlist. Keep an eye out for our upcoming webcast on new innovations, which will also be available in the same playlist soon.

If you have any questions about SAP HANA Cloud or want to discuss any of the innovations mentioned, feel free to post them in our SAP HANA Cloud Community Q&A or in the comments section.

SAP CodeJam HANA ML in Berlin 2024-06 Recap

2024-07-01T20:41:58.273000+02:00

On the last Friday of June, we held the Getting Started with Machine Learning using SAP HANA and Python in Berlin, Germany.

It was kindly hosted by Zalando and organized by @rofinke

It was a mix of some theory...

...and hands-on.

It was great to have the product manager responsible for the SAP HANA embedded Machine Learning @ChristophMorgen with us.

I would like to extend special thanks to @oliver for organizing and all participants joining SAP Stammtisch Berlin for the day before...

...that I used as well to walk and to see the city...

...including the former Berlin Wall.

Would you like to host such an SAP CodeJam? Here are the details: https://community.sap.com/t5/technology-blogs-by-sap/quot-getting-started-with-machine-learning-usin...

A Closer Look at the New Metering Metrics added to Metrics Service from SAP HANA Cloud 2024 QRC2

2024-07-05T06:00:43.619000+02:00

In the relentless quest to boost transparency and allow our users to have a better understanding of their SAP HANA Cloud charges, we are delighted to introduce the new metering metrics to our Metrics Service as part of 2024 QRC2. The primary aim of this update is to give a clear and exact account of the usage of resources.

The addition of metering metrics gives you the ability to pinpoint the charges presently being applied to your system. The metering metrics provides a comprehensive insight into resource usage, which is measured in Capacity Units (CU) for various facets such as memory, storage, compute power, backup and network operations.

An array of system components is under the purview of these metrics, which include default nodes, replica nodes, snapshot nodes, and individual features like Cloud Connector. An exclusive feature of these metrics is that they all return 'delta' type measurements. This implies that the values, which are captured at hourly intervals, measure the changes since the metric value was last taped, giving you an up-to-date report about your consumption.

Metering metrics list

Furthermore, we've ensured the extension of these metrics to both SAP HANA Cloud, SAP HANA database and SAP HANA Cloud, data lake. You can find all the information and details in the official help document here.

How to use metrics service tutorial

To make it convenient for you to consume these metrics via the REST API, we recommend the SAP tutorial "Explore SAP HANA Cloud Alerts and Metrics". You would specifically want to look at the section "Access database metering metrics". Find it here.

Consumption monitor in HANA Cloud Central

Alongside metering metrics availability, we are also thrilled to release the consumption monitor application in HANA Cloud Central. This application lets you monitor consumption in a user-friendly interface, offering you a clear view of your system's consumption. Learn more about using the HANA Cloud Central here.

Conclusion

Armed with these new functionalities, we hope that you'll have a better understanding and grip over your system consumption. We genuinely believe that our constant enhancement will not only lead to more transparency but also help you in making informed strategic decisions. Stay tuned for more updates as we continue to evolve for your needs.

SAP HANA Cloud release June 2024 Round-up

2024-07-08T10:25:16.225000+02:00

Dear SAP HANA Cloud Enthusiasts,

As we finalize the second quarter of 2024, we are thrilled to present a comprehensive collection of the latest videos, blog posts, and resources from the June 2024 release of SAP HANA Cloud.

	What’s New Teaser Dive into SAP HANA Cloud's latest innovations with Lead Product Manager Thomas Hammer! His "What’s New" teaser offers an exciting and concise snapshot of his favorite features from the newest release. Watch it now on YouTube.
What’s New blogpost Intrigued by the teaser? Explore our "What’s New in SAP HANA Cloud in June 2024" blog post for an in-depth look and find valuable links for further insights. Read it here!
	What's New Webinar Prefer watching over reading? Our 'What’s New' webinar is just for you! Join our Product experts for an in-depth exploration of the latest features. Watch anytime by clicking here!

Additional information

In addition to our regular release updates, make sure to check the following valuable content provided by our team:

Learn more about New Machine Learning features in SAP HANA Cloud database release 2024 Q2 by @ChristophMorgen
Learn more about Calculation View Features of 2024 QRC2 by @jan_zwickel
A Closer Look at the New Metering Metrics added to Metrics Service from SAP HANA Cloud 2024 QRC2 by @Hai
Join our Early Adopter Care program:
- SAP HANA Cloud for large instances
- MDS Cubes in SAP HANA Cloud
Join our Customer Engagement Initiative:
- SAP HANA Database Migration to SAP HANA Cloud (XSC,XSA,CAP)

Don’t miss out on all the content and remember to follow us in the SAP HANA Community.

Remember to check our content following the # whatsnewinsaphanacloud tag: here

Don’t forget to subscribe and follow SAP HANA Cloud on YouTube to always stay up-to-date regarding the most recent innovations in SAP HANA Cloud.

All the best,

Andrea on behalf of the SAP HANA Cloud team

#SAP HANA Cloud, SAP HANA database #SAP HANA Cloud

#whatsnewinsaphanacloud

SAP Data and Analytics: Join our Performance focused Live Sessions Series

2024-07-12T08:47:47.532000+02:00

Performance optimization is a central task for every developer, data modeler, data engineer or data analyst since in addition to quality, speed is a key requirement for modern reporting. With this in mind, we have created a sophisticated series of expert-guided Live Sessions to address this important challenge.

Boost your skills in SAP Analytics Cloud performance and explore key features and optimizations that enhance data analytics efficiency and effectiveness in SAP Analytics Cloud, in our specialized expert-guided Live Session events, “SAP Analytics Cloud Performance overview”. Register here.

Understand the performance implications of story design and scripting in SAP Analytics Cloud, in our expert-guided Live Session events "Understanding the relationship between story design and performance in SAP Analytics Cloud". Register here.

Learn how backend data models in SAP BW/4HANA and SAP Datasphere impact SAP Analytics Cloud performance, in our expert-guided Live Session event "SAP Modeling and SAP Analytics Cloud Story performance options". Register here.

Discover best practices in data modeling in SAP HANA to ensure your models run at the highest possible speed and are built for sustainability, in our expert-guided Live Session events “Best practices when developing calculation views in SAP HANA”. Register here.

Maximize the performance of accessing data in SAP BW/4HANA. Master the techniques for increasing efficiency and speeding up the retrieval of critical data in your organization, in our expert-guided Live Session events “Performance aspects of Advanced DataStore Objects and CompositeProviders in SAP BW/4HANA.” Register here.

Explore advanced SAP Analytics Cloud features like custom filtering or display logic and review the scripting power of SAP Analytics Cloud having best practices for a clean code approach in mind, in our expert-guided Live Session events "SAP Analytics Cloud – intermediate reporting". Register here.

The Live Session events mentioned above are linked to the Learning Hub and can only be attended with a valid subscription.

Product Learning CoE Analytics Team

Check the Availability of Your Multi-Environment HANA Cloud Instances with SAP Automation Pilot

2024-07-26T11:32:29.742000+02:00

In this blog post, I share hands-on insights on how to minimize downtime for your SAP HANA Cloud database by leveraging SAP Automation Pilot. By simply going through this step-by-step guide, you will learn how easy it is to set up SAP Automation Pilot to to perform automated availability checks effortlessly, so that you minimize disruptions, keep your business running smoothly and maximize performance!

Introduction

Undoubtedly, ensuring that your HANA Cloud instances are always up and running can be crucial for the success of your business operations. In Biser Simeonov's last blog post co-authored by me, “Automated Availability Check for SAP HANA Cloud (on CF level) with SAP Automation Pilot”, we showed you how to automate the monitoring of your HANA Cloud instances enabled in a Cloud Foundry space by using SAP Automation Pilot and SAP Alert Notification Service for SAP BTP – two powerful tools from the SAP Business Technology Platform (SAP BTP) DevOps portfolio. You learned how to utilize these tools to check the availability of your instances, and even start the ones that are not accessible. So far so good!

However, with the 2022 QRC3 release in October 2022, SAP HANA Cloud went "multi-environment". As a result, now you can have HANA Cloud instances in subaccounts that don’t have the Cloud Foundry environment enabled. For more information, see:

In the following guide, I will show you how to perform the same HANA Cloud Availability Check for HANA Cloud instances enabled in “Other Environments” (multi-environment setup).

How does the HANA Cloud Availability Check work?

The process is visualized in the graphic below. I advise you to check out the previous blog post for a more detailed explanation.

Step-by-Step Guide

Prerequisites

SAP HANA Cloud database provisioned on subaccount level (multi-environment);
SAP Automation Pilot (See Initial Setup);
SAP Alert Notification service for SAP BTP enabled in Cloud Foundry (See Initial Setup);
Integration between SAP Automation Pilot and SAP Alert Notification service for SAP BTP (See Integrate with SAP Alert Notification Service for SAP BTP);
SAP Service Manager instance with plan “subaccount-admin” provisioned on space level or on subaccount level;

After you’ve made sure that you meet all the prerequisites, you can proceed with the steps below.

1. Verify that you have provisioned SAP Automation Pilot. You should be able to see it in the “Subscriptions” section in your subaccount:

NOTE: SAP Automation Pilot can interact with resources in any BTP landscapes. All you need is to have at least one instance of SAP Automation Pilot.

2. Verify that you have Alert Notification and Service Manager instances, and that your HANA Cloud instance is enabled in “Other Environments”.

Your Service Manager instance can be provisioned either in your subaccount, or in your space. The only difference this will make is whether you will have to create a “Service Binding” or a “Service Key” later.

3. You need to create a Service Key for your Alert Notification service instance. This key will be used on a later stage by the SAP Automation Pilot command. To create a Service Key, choose the “Create Service Key” option:

Enter a name for your service key and choose “Create”:

4. Next, you need to create a “Service Binding” for your HANA Cloud instance, which will be used by the SAP Automation Pilot command. To do that, choose the “Create Service Binding” option:

Enter a name for your service binding and choose “Create”:

5. Depending on the scope of your Service Manager instance, you need to create:

a “Service Key” if your instance is on space level.
a “Service Binding” if your instance is on subaccount level.

6. The SAP Automation Pilot team has prepared a catalog with example commands, which can be imported directly into your SAP Automation Pilot instance.

Copy the contents of the catalog:

7. Now you need to import the catalog with example commands in your SAP Automation Pilot instance. From the left-side menu, choose “My Catalogs” and then “Import”:

Paste the catalog and choose “Import”:

8. You can see the imported catalog “Automation Pilot Examples” in “My Catalogs”. From this catalog, choose “Commands”:

9. From the catalog, open the “CheckHanaCloudMultiEnvAvailabilitySample” command by clicking on it. Explore the Input Keys, the Output Keys, and the Executors of the command:

10. Scroll down to “Configuration” and choose the “notifyANS” executor. You need to define the event that SAP Automation Pilot will ingest into Alert Notification service. Choose “Edit” to edit the properties of your event:

You can use these example values for the required event properties of your event:

body: The availability check for HANA Cloud with Service Instance ID $(.execution.input.hanaServiceInstance) has failed. Now the Automation Pilot proceeds with an automated START for this HANA Cloud instance.
category: ALERT
eventType: FailedHCAvailabilityCheck
resourceName: hana
resourceType: database
serviceKey: $(.execution.input.AnsServiceKey) -> use a dynamic expression to get the AnsServiceKey from the inputs
severity: ERROR
subject: Failed HANA Cloud Availability Check $(nowMillis | toDate("yyyy-MM-dd"))

11. After you have done that, trigger the command:

12. You need to provide values for the following input keys:

AnsServiceKey - Service Key for your Alert Notification service instance.
hanaClientTokenURL - The URL needed to be called in order to generate a token to access your HANA Cloud instance. The URL is built as follows: in the HANA Cloud service binding file, in the "uaa" section, look for the specified "url" + add to it /oauth/token?grant_type=client_credentials.
hanaCloudBindingKey - Service Binding for your Hana Cloud instance
hanaCloudResourceName - The exact name of your HANA Cloud instance, e.g. "hanaDemo", "hanaProd", etc.
hanaCluster - Needed to access the HANA Cloud API gateway, where 'cluster' typically identifies either a production, pre-production or trial environment.
hanaDatacenter - Needed to access the HANA Cloud API gateway, where 'datacenter' is the data center where your HANA Cloud is deployed, i.e. eu10, us10, etc.
hanaServiceInstance - The Instance ID of your HANA Cloud instance - it can be found from the provisioned service in BTP Cockpit, look for "Instance ID".
ServiceManagerKey - Service Key or Service Binding for your Service Manager instance used to perform management tasks against multiple instances in your BTP subaccount.

You can also look at the prepared outputs (no need to provide values for them but it is important to understand what data is returned as the command's output):

checkPing - The entire response for the metric "HDBAccessible" returned by HANA Cloud Metrics Service REST API.
isHanaCloudAvailable - Check on whether the HANA Cloud instance is available, where: "true" = HANA Cloud is available; "false" = HANA Cloud is not available.
resultHanaStart - Check on whether the HANA Cloud instance has been started successfully, where: "true" = HANA Cloud has been started; "false" = HANA Cloud was not started'.

13. Provide values for all input keys and then trigger the command:

There are two possible options for the outcome of the execution:

Option 1: Your HANA Cloud instance is accessible and the command for starting HANA Cloud has not been triggered. You should be able to see the following execution details:

If you click on “Output Values”, you can see the output values for checkPing, isHanaCloudAvailable, and resultHanaStart:

You can verify in SAP Hana Cloud Central that your HANA Cloud instance is up and running:

Option 2: Your HANA Cloud instance is not accessible and the command for starting HANA Cloud has been triggered:

You can verify in SAP Hana Cloud Central that your HANA Cloud instance is starting:

If you click on “Output Values”, you can see the output values for checkPing, isHanaCloudAvailable, and resultHanaStart:

You have successfully performed the HANA Cloud Availability Check via SAP Automation Pilot. Now you can make a subscription in Alert Notification service, so that you can receive a notification when SAP Automation Pilot detects that your multi-environment HANA Cloud instance is not accessible. If you want to do that, continue with the following steps:

14. Navigate to your Alert Notification service instance and open it. Choose the “Subscriptions” tab form the left-side menu and then “Create” to create a new subscription:

15. Enter a name for your subscription and click “Create” to move on to the next step:

16. Next, create a Condition. Give your condition a name. Then define your condition by choosing the event property “eventType”, operand “Is Equal To”, and, in our case, expected value “FailedHCAvailabilityCheck”:

The expected value for “eventType” is the one you provided in step 10:

17. To finish your subscription in Alert Notification service, select an Action by choosing “Create Action”:

Select an Action Type of your preference, for example “Email” and choose “Next”:

Give your Action a name, and then provide the email address where you want to receive the email notification:

NOTE: After you have created some of the actions, they are labeled as Not Confirmed. To enable them, you have to confirm them as described in Confirming Actions.

Choose “Save” to finish making your subscription. You will see a confirmation that your subscription was created:

You can find more information about Subscriptions, Conditions, and Actions in Configuration Management Using the SAP BTP Cockpit.

18. Now you can test your subscription. If you stop your HANA Cloud Instance and trigger the CheckHanaCloudMultiEnvAvailabilitySample command again, you will receive an email from Alert Notification service with information that the HANA Cloud Availability Check has failed. You can see an example email below:

NOTE: If you also want to receive notifications in a Microsoft Teams channel of your choice, check out the previous blog post for a detailed step-by-step explanation on how to make this configuration.

Wrap-up

In this blog, you've learned to utilize SAP Automation Pilot and SAP Alert Notification service for SAP BTP to effectively monitor the availability of your multi-environment HANA Cloud instances. By using these innovative tools in your daily operations, you can save time, prevent potential disruptions, and ensure your operations continue to run smoothly. If you are interested to learn more about these products and how they can transform your business, please refer to the following sources:

SAP Automation Pilot:

SAP Alert Notification service for SAP BTP:

You can also check out these very useful tutorials prepared by our colleague Dan van Leeuwen:

Powerful way to create a CDS view based report on BOMs in SAP S4 HANA Cloud (Public Edition)

2024-07-30T19:55:38.208000+02:00

Since, most of our reporting requirements in SAP S/4HANA Public Cloud are met through CDS Views, I’m herewith sharing my experience on how end user life can be made easy, through usage of right CDS views, and converting the same into an easily consumable app!

Here we are creating a BOM report using custom CDS view and analytical query which will display the details of all existing Bill of materials for a range of materials within plant.

Check this out –

Step 1. Create custom CDS views to fetch data in custom analytical query.

Step 2. Create a custom analytical query.

Step 3. Create application (Fiori tile) for query and assign it to Business catalog.

Selection Screen:

Final Output of Report:

Steps 1. Create custom CDS views.

Within the SAP Fiori environment, open the app 'Custom CDS Views' which can be found in the category Extensibility.

Click on Create.

Fill Label (Name of CDS) and select scenario as ‘Standard CDS View’ and click on create.

*We are using the scenario as ‘standard CDS view’ because we will use this CDS in another custom CDS view using joint conditions.

Select Primary data source as ‘I_BillOfMaterialItemDEX_2’

Click on Add to an associate data source.

Click on Associate data source.

Select associate data source as ‘I_Product’

Click on Joint Condition

Click on Add

Select Product

Select Value type = Field

Value = I_BillOfMaterialItemDEX_2.BillOfMaterialComponent

Cardinality = Zero or One

Go to Element Tab: Click on Add – Elements

Add below mentioned elements.

Click on Check.

Once Check is successfully done, Click on Publish

Custom CDS Published Successfully.

Now we will create another CDS of scenario ‘Analytical cube’ which we will use in Analytical query.

Go to App: Custom CDS Views.

Click on Create

Fill Label and select scenario as ‘Analytical Cube.’

Select Primary data source as ‘I_MaterialBOMLink’

Click on Add an Associate data source.

Select CDS view which we have created earlier.

Click on Joint Condition

Add Joint condition- BillOfMaterial and BillOfMaterialVariant

Select value type as Field and value as BillOfMaterial & BillOfMaterialVariant

Cardinality= Zero and One

Go To Elementsà Click on Add Elements

Add below mentioned elements.

Go to Element Properties à Select ‘No Operation’ for ‘BillOfMaterialItemQuantity’

Click on Check

Click on Publish

Step 2. Create a custom analytical query.

Go to App: Custom Analytical Queries

Click on Create

Mention Query Name and Select Data source as CDS created in Step 1.

In General Tab: Give Label (Name of Query Report)

Go to Field Selection view and select required fields for report

Use Up Down Option in Display Tab to set sequence for fields in report.

Use Override label option to change the column name.

Go to Filter Option

Click on Field à Select User Unit Values

Selection type = Interval, select the multiple selection checkbox.

Click on Save Draft Option

Mention Plant and click on Go.

Report Displayed.

Come back to query screen and click on Publish.

Step 3. Create application (Fiori tile) for query and assign it to Business catalog.

Go to App: View browser – Browse Views

Search your custom analytical query, select, and click on create application.

Give Report title and subtitle and click on ok.

Application ID will generate for the query.

Click on Application ID and Go to ‘view in catalog’ option

Click on Add to add this application to business catalog.

Select Business catalog.

*You can assign this application to multiple business catalog.

Select the catalog and click on Publish.

App Published successfully for selected business catalog.

Click on Business catalog.

Go to More and select used in Business roles.

Now assign one of the business roles to your user ID.

Go to SAP Home screen.

Search App: BOM Report

Fill the selection parameter and click on Go.

Thanks & Regards,

Manish Sati

Delta configurationfor Standard API in S/4 HANA cloud

2024-07-30T22:39:30.727000+02:00

In order to Create Delta for standard API:

Go to product master standard API in Business Accelerator Hub then click on tryout option.

Now, we have to configure our system environment here as shown below

And after successfully configuring Now you can tryout any of these operations in Standard API

Now in order to get delta, we have to take a delta pointer based on the fields in standard API. In this case I’m taking Lastchangedate field as delta pointer

And Filter out records based on delta pointer(Lastchangedate)as shown below

Here the logic is: LastChangeDate ge datetime'2024-01-14T00:00:00’ And it says lastchangedate is greater than or equals to 2024-01-14 and also there are other logics as well: LastChangeDate ge datetime'2024-06-13T00:00:00' and LastChangeDate lt datetime'2024-06-14T00:00:00' and it only gives records between 14th june to 13th june .

So After initializing this logic run as shown below

we get records that are changed on or after 14th January 2024 in OData URL itself and it gives count of how many records are present as shown below

Now we can check data by taking URL till highlighted part and run OData in PowerBI

Now in Power BI, Select OData feed and add the required URL

And we can see required delta records here

SAP Application Migration Assistant

2024-08-01T13:32:47.968000+02:00

Problem:

NEO or HANA 2 XSC database content as tables, views, calculation views, etc. needed to be migrate to SAP Business Application Studio or BUILD Code for further development.

Requirements:

A SAP Cloud Connector for your HANA 2 environment
A BTP account with SAP Business Application Studio (BAS) or SAP BUILD Code entitled

Solution:

Convert DB content from XSC/sys_repo into HDI/CAP content using SAP Application Migration Assistant.

Preparation:

In BTP: Configure your DevSpace as mentioned below
In BTP: Configure the use of SAP Cloud Connector

It should look similar to this screenshot:

Find more help on this in the cloud connector help and in this blog:

Connect SAP HANA On-Premise to SAP HANA Cloud and Database with Cloud Connector

Add the following extensions to your development space of BAS:

SAP HANA Application Migrations Assistant will help you migrate content

SAP HANA XS Advanced Tools will allow to work with BAS on your XSA environment

SAP HANA XS Advanced Tools allows you to connect and deploy to XSA environment for HANA on premise
SAP HANA Application Migration Assistant will migrate content from XSC/sys_repo to an HDI project.

Calling the SAP HANA Application Migration Assistant by F1 :"SAP HANA Application..."

Sample of conversion from XSC to CAP:

Original design time object in HANA Studio:

Here is the result of a conversion to CAP CDS:

Once you are in BAS you can also see the HANA Cloud generated code here as well:

There are many functions and features build in to this tool. Please try out and read the help manual.

Summary:

At this moment the tool converts XSC design time objects of the database to an HDI / CAP level. Not all objects can be migrated since there is no equivalent in HANA Cloud /CAP. Anyhow this helps to get a huge amount done supported by the generator/converter. With the change of xsjs to Node.js we decided to use the CAP model. This helps developers to also get the ODATA access being generated as well. And with BAS and/or BuildCode it should be be possible for many use cases to get into the BTP world with all modern features as the latest Fiori and Joule support.

This will also help you to get to the environment where you also can develop for "Clean Core". So you end with this migration at the same dev environment.

Also many thanks to the great support from the CAP team.

Outlook:

Several things are in the pipeline:

XSA -> SAP HANA Cloud and CAP
Data migration
1. This is a bit more complicated: We have to rename the objects to be CAP compliant. So the source and the traget name differ. We provide a list of the name changes and we will use this to generate SQL commands for data copy purpose.

Links:

SAP HANA Application Migration Assistant (This tool)

The old migration tool that is replaced by this tool

Cloud Connector installation

SAP HANA Cloud Migration Guide

Sample on GitHUB

Connect SAP HANA On-Premise to SAP HANA Cloud and Database with Cloud Connector

more help is on its way (will be added later)

Connect SAP Sales Cloud V2 to SAP Datasphere

2024-08-07T22:29:07.449000+02:00

How to connect SAP Sales Cloud V2 to SAP Datasphere.

SAP Sales Cloud V2 provides Rest APIs only. You can find them here in the Business Accelerator Hub: https://api.sap.com/package/SAPSalesServiceCloudV2/rest

You can use these Rest APIs and generate output with your own environment within Business Accelerator Hub. You have to user Business User Credentials and the user name has to have at least six characters.

For the following example, I did that with the Opportunities. The output is a nested JSON. Why is that an issue?

You have one opportunity with 4 Business Partners (each take a different role). Another Opportunity only has 2. This issue goes on with involved parties and items and many more attributes. Back with Sales Cloud V1 (C4C/Cloud for Customers) you were able to extract the collections. Now you will have issues with extracting the information needed. Therefore, you have to recreate those collections, you used to have.

You will have several options to do that, for example with SAP Integration Suite. You can select the JDBC scenario. I still recommend doing that, but if you don’t have the option to use SAP Integration Suite, you can launch a python script.

In my example I did run it locally, but if I would do it for a customer, I would just launch it on Cloud Foundry (within SAP BTP). I will provide a blog on how to do that very soon.

This code calls the Rest API, flattens it, extracts the collections needed for this case and writes the tables back into the HANA under SAP Datasphere. I will use those tables to build views upon them. The tables will update once the code is being executed again. Adjust the code to call for Leads, Accounts etc. This is a workaround until SAP provides the APIs, we used to have in Sales Cloud V1 or if the customer does not have SAP Integration Suite. There are still a lot of other options to work around.

import requests 

from requests.auth import HTTPBasicAuth 

import pandas as pd 

from pandas import json_normalize 

from hana_ml.dataframe import create_dataframe_from_pandas 

from hana_ml.dataframe import ConnectionContext 

  

def fetch_data_from_api(url, username, password): 

    try: 

        # Define headers for the request, including additional config headers 

        headers = { 

            'config_authType': 'Basic', 

            'config_packageName': 'SAPSalesServiceCloudV2', 

            'config_actualUrl': 'https://myxxxx.de1.test.crm.cloud.sap', 

            'config_urlPattern': 'https://{hostname}', 

            'config_apiName': 'SalesSvcCloudV2_xxxxxx', 

            'Accept': 'application/json' 

        } 

         

        # Send a GET request to the API endpoint with Basic Auth and headers 

        response = requests.get(url, headers=headers, auth=HTTPBasicAuth(username, password)) 

         

        # Raise an HTTPError if the HTTP request returned an unsuccessful status code 

        response.raise_for_status() 

         

        # Parse the JSON response 

        data = response.json() 

         

        return data 

    except requests.exceptions.HTTPError as http_err: 

        print(f"HTTP error occurred: {http_err}") 

    except Exception as err: 

        print(f"Other error occurred: {err}") 

  

def extract_main_opportunity_data(data): 

    # Extract the main opportunity data and convert it to a DataFrame 

    opportunities = data.get('value', []) 

    df_opportunities = json_normalize(opportunities) 

    return df_opportunities 

  

def extract_involved_parties(data): 

    # Extract involved parties from each opportunity 

    opportunities = data.get('value', []) 

    involved_parties_list = [] 

  

    for opportunity in opportunities: 

        involved_parties = opportunity.get('involvedParties', []) 

        for party in involved_parties: 

            party['opportunity_id'] = opportunity.get('id')  # Link party to opportunity 

            involved_parties_list.append(party) 

     

    df_involved_parties = pd.DataFrame(involved_parties_list) 

    return df_involved_parties 

  

def extract_items(data): 

    # Placeholder for extracting items if they exist in the JSON structure 

    # Similar to extracting involved parties 

    items_list = [] 

  

    for opportunity in data.get('value', []): 

        items = opportunity.get('items', [])  # Adjust the key as per actual data 

        for item in items: 

            item['opportunity_id'] = opportunity.get('id')  # Link item to opportunity 

            items_list.append(item) 

     

    df_items = pd.DataFrame(items_list) 

    return df_items 

  

def clean_dataframe(df): 

    # Convert all columns to strings to avoid type issues 

    df = df.astype(str) 

    # Replace problematic characters or values if necessary 

    df = df.replace({r'\r\n': ' ', r'\r': ' ', r'\n': ' '}, regex=True) 

    return df 

  

def write_dataframe_to_hana(dataframe, table_name, conn_context): 

    # Clean the dataframe to ensure compatibility 

    dataframe = clean_dataframe(dataframe) 

    hana_df = create_dataframe_from_pandas(conn_context, dataframe, table_name, force=True) 

    return hana_df 

  

# URL of the REST API endpoint 

api_url = 'https://myXXXXXXXX.de1.test.crm.cloud.sap/sap/c4c/api/v1/opportunity-service/opportunities' 

  

# Your credentials for Basic Auth 

username = 'xxxxxxxx' 

password = 'xxxxxxxx' 

  

# Fetch data from the API 

data = fetch_data_from_api(api_url, username, password) 

  

# Extract and convert JSON data to DataFrames 

if data is not None: 

    df_opportunities = extract_main_opportunity_data(data) 

    df_involved_parties = extract_involved_parties(data) 

    df_items = extract_items(data)  # If items exist 

     

    # SAP HANA Cloud connection details 

    host = xxxxxxxxxxxx.hana.prod-eu10.hanacloud.ondemand.com' 

    port = '443' 

    user = 'XXXXXXX’ 

    password = 'xxxxxxxxxxxxx' 

     

    # Establish connection 

    conn_context = ConnectionContext(address=host, port=port, user=user, password=password) 

     

    # Write dataframes to SAP HANA Cloud 

    write_dataframe_to_hana(df_opportunities, 'OPPORTUNITIES', conn_context) 

    write_dataframe_to_hana(df_involved_parties, 'INVOLVED_PARTIES', conn_context) 

    write_dataframe_to_hana(df_items, 'ITEMS', conn_context) 

     

    # Close the connection 

    conn_context.close() 

else: 

    print("No data retrieved from the API.")

SAP Integration Strategy - End-to-end business processes deep dives - NEW eBook available

2024-08-11T23:05:07.722000+02:00

Dear community members,
Dear colleagues,

I'm happy to announce our 2nd edition of our SAP Integration Strategy eBook, this time more focusing on how our customers benefit from an integrated SAP software portfolio by enabling them to design, model, implement, and ultimately run their business processes end-to-end.

You may remember our first Youtube calls and presentations – that we also already put together with this first eBook – and in which we informed you about our overall integration strategy at SAP, where we walked you through our so-called suite qualities, to address the various aspects of integration requirements as such.

Simply take these 2 examples – e.g. the need to unify and harmonize our user interfaces of the various software solutions that SAP offers, and the other is about requirements such as consistent security and identity management, that functions across all applications, just think of Single Sign-On.

Our suite qualities are now implemented in our SAP software solutions, and SAP is therefore proven to be able to support our customers’ end-to-end business processes.

Thus it was just obvious for us to share these achievements with you, as we ran the so called E2E business process deep dives series to provide a detailed picture what customers and partners can expect from SAP to drive their digital transformation and establish business processes, at the same time fitting to their particular needs:

Lead to Cash
Hire to Retire
Source to Pay
Design to Operate

With the new eBook you now have an excellent collection of all of our SAP Community calls and the related presentations that show you how SAP can now support your business requirements with a holistically integrated software portfolio.

For upcoming events, webinars and any kind of new information you may bookmark this SAP Community Topic Page called “SAP Integration Strategy” to remain up-to-date with regards to our upcoming sessions and webinars that we area about to setup for you.

Happy reading and enjoy watching the recordings as well as studying the presentations!

Backup of SAP HANA Database on GCP using backup & DR services

2024-08-12T07:33:01.981000+02:00

Backup of SAP HANA Database on GCP using backup & DR services

This article contains all the SAP and GCP related technical Snapshots and settings which can be used for configuring HANA Database backup on SUSE Linux Enterprise & RHEL. Operations described in the document are performed when SAP HANA Database is deployed on GCP. SAP has provided several notes which support SAP products on Google.

SAP HANA BACKUP ON GCP

SAP HANA, as a critical component of many business applications, demands robust business continuity and disaster recovery (BCDR) strategies. Any downtime can result in significant financial losses and reputational damage. A well-planned BCDR strategy ensures business continuity, minimizes data loss, and speeds up recovery time. Google Cloud provides a comprehensive platform to safeguard your SAP HANA environment against unforeseen disruptions. This blog goes into the technical aspects of implementing SAP HANA BCDR on Google Cloud.

Implementing a robust SAP HANA BCDR strategy on Google Cloud is crucial for ensuring business continuity. By carefully evaluating your business needs, leveraging Google Cloud's capabilities, and following best practices, you can protect your SAP HANA environment and minimize the impact of potential disruptions.

Prerequisites:

All the configured services— see SAP Note 1697613 and SAP Note 1649519— such as nameserver and indexserver must be running. You can check this in the Overview of SAP HANA studio > Operational State > Confirm all services are started.

Create FS for logabackup on disk as BU-DR will backup login on local Disk & take snapshot of Disk on storage.

Database log_mode must be normal under the HANA Studio configuration tab.

All SAP HANA servers (Compute Engine) that have HANA data to be protected by Backup and DR Service must have been onboarded to Backup and DR Service
All SAP HANA servers (Compute Engine) that have HANA data to be protected by Backup and DR Service must have the Backup and DR agent installed.

HANA log backup recommendation:

Make sure to set the HANA Archive log backup path as the basepath_logbackup and basepath_catalogbackup in global.ini
Don't use HANA Archive log mount to store files other than HANA archive log backup
By default archive purge will run with every database backup. This achieves the best recovery RTO

Create the database backup user from HANA Studio:

Choose a database username based on the company's standard. Make sure to create this user account under SYSTEMDB.

HANA 2.0 (SPS05 and later): Click the System privilege tab and assign privileges by selecting the following:

Backup admin
Catalog read
Database backup operator +. Database recovery operator
Database admin
Database start
Database stop

Deploy the newly created system user by clicking the green arrow.

Get the instance and SQL port numbers:

You need the HANA instance and SQL Port numbers for system DB.

To get the instance and SQL port numbers from HANA 2.0 studio:

Go to System.
Click the Landscape tab.
Click the Services tab.
Under the Service column, navigate to the nameserver.
Get the value of SQL port. The instance number is the second and third digits of the SQL port number.

Create the SAP HANA hdbuserstore key for system DB backup users:

Use this naming convention for the userstore key name: For SYSTEMDB, set the key name to (Database backup username). For example, for database backup username (Actbackup), set SYSTEMDB—key_name— (ACTBACKUP).

Procedure:

Connect to the HANA database system using SSH and log into (sid)adm: su - (sid)adm
Change directory: cd exe

Create entries in hdbuserstore using command hdbuserstore: ./hdbuserstore SET (key_name) (host name):(port) (DB_user_name) (DB_user_password)

In this example: hdbuserstore SET GCPBACKUPDR HOSTNAME:30013 GCPBACKUPDR ****** (DB_user_password)

Check the keystore: ./hdbuserstore list

Prepare Below command according to above steps :

hdbuserstore SET GCPBACKUPDR HOSTNAME:30013 GCPBACKUPDR ******

hdbsql -U GCPBACKUPDR

Set up the log mode and log backup in HANA Studio:

Using the HANA Studio set the HANA log mode and log backup location for Backup and DR to take backups of HANA DB transaction logs:

In SAP HANA HDB studio, make sure log backup is set correctly under DATABASE (SYSTEMDB FOR HANA 2.0) > Backup > Configuration page:

Destination type is File.
Destination is set to a local file system mount path.
Backup interval is set to required RPO.
Automatic log backup is enabled.
Database log_mode must be set to normal

Check under Database configuration: DATABASE (SYSTEMDB FOR HANA 2.0) > Configuration page. In the filter, type basepath.
Verify that basepath_logbackup is set correctly:

Set the basepath_catalogbackup to the same as basepath_logbackup.
Open the basepath_catalogbackup edit page and set the New value to the same as basepath_logbackup. Click Save. This ensures the backup of the catalog with log backup for point in time recovery.

On the HANA database server, run the re-config for the values to take effect: hdbnsutil -reconfig.

hdbnsutil -reconfig

Install agent on HANA DB Host :

gcloud storage cp gs://sap-artifacts-ib50/binaries/linux/connector-Linux-11.0.10.417.rpm /tmp/connector-Linux-11.0.10.417.rpm

sudo rpm -Uvh /tmp/connector-Linux-11.0.10.417.rpm

sudo cp /opt/act/cbt/bin/cbt_activate.sh /opt/act/cbt/bin/cbt_activate.sh.bak

sudo sed 's/bash$/&\nexit 0/' /opt/act/cbt/bin/cbt_activate.sh -i

Onboard HANA DB host to Backup and DR console and exchange secret :

Select Project & go to Backup DR

Select Compute Engine

Select service Account & Click on next.

Select Project ID / Zone & Click on Search.

Select DB hostname that you want to configure & Next.

Click on Next:

Click on Finish :

RUN SAP HANA Onboarding Wizard.

Enter Secret.

Create Secret below:

sudo /opt/act/bin/udsagent secret --reset –restart

If get above error remove –restart & give below command.

sudo /opt/act/bin/udsagent secret --reset

Copy Key from above command & put in SECRET

Host Updated Successfully.

Select HANA SID which needs to onboard & Click on OK.

Select SID & Next.

Configure Backup options for HANA SID :

Provide USER STORE KEY, Select BACKUP Capture Method, Retention & Click on Save.

Click on Next

FINISH

Onboarding Finished Successfully.

Run Ad-hoc backup:

Go to App Manager à Applications

Select SID & Click on manage backup Configuration.

Select Tamplate created for HANA backup & Profile & click on Apply Plan

Apply backup plan.

Click on Okay

Click on Run Now to run ad-hoc backup.

Go to Monitor – Job Monitor:

Backup Job Started.

Check backup logs on below OS location:

tail -f /var/act/log/UDSAgent.log

Backup Completed Successfully.

Concepts of Core Data Services (CDS)

2024-08-14T08:57:26.030000+02:00

Metaphor: Building a Custom Library

Imagine a custom library (a physical one, with bookshelves and books) that caters to specific readers with unique needs.

Imagine we’re setting up a specialized library focused on self-help and mental health topics. Our goal is to organize this library so that readers can easily find information relevant to their needs.

1. Books (Tables)

In this library, the books are like the tables in your database.

Each book contains detailed information on a specific topic, such as anxiety management, cognitive behavioral therapy, or stress relief techniques.

Similarly, a database table holds detailed records (rows) on a specific subject (columns).

Example: We have a book (table) on “Stress Management Techniques” that includes details like different methods for stress relief, case studies, and practical exercises.

2. Bookshelves (CDS Views)

A bookshelf is like a CDS View.

It doesn’t hold books directly but arranges selected books in a way that makes it easy for our readers to find specific information.

When we create a CDS view, we are essentially curating specific data from our tables and organizing it on a bookshelf.

Example: We create a bookshelf (CDS view) that displays only books about “Mindfulness Practices.” This helps readers interested in mindfulness quickly find relevant resources without sifting through the entire collection.

3. Sections (Projections)

Each bookshelf have sections that categorize the books even further.

In CDS View, Projections allow us to create a focused view by selecting only certain columns (fields) from a table. It’s like choosing to display only specific chapters or sections of a book on a particular bookshelf.
This makes it easier for our readers to find exactly what they need without getting overwhelmed by too much information.
In a CDS view, when we create a projection, we’re essentially telling the system, “I only want to see these specific columns from the table, and I don’t need the rest right now.”

Main Entity: MindfulnessPractices

entity MindfulnessPractices {
    key ID           : Integer;
    PracticeName     : String(100); // 'Deep Breathing', 'Body Scan'
    TechniqueType    : String(50);  // 'Breathing', 'Meditation', 'Walking'
    Duration         : Integer;     // Duration in minutes, e.g., 10, 20, 30
    DifficultyLevel  : String(20);  // 'Beginner', 'Intermediate', 'Advanced'
    Benefits         : String(200); // 'Reduces anxiety', 'Improves focus'
}

Projection Example: Breathing Exercises

Create a projection to focus specifically on Breathing Exercises

entity BreathingExercises as projection on MindfulnessPractices {
    PracticeName,
    TechniqueType,
    Duration,
    Benefits
} where TechniqueType = 'Breathing';

Another projection example for Body Scan Meditation

entity BodyScanMeditation as projection on MindfulnessPractices {
    PracticeName,
    Duration,
    DifficultyLevel,
    Benefits
} where TechniqueType = 'Meditation' and PracticeName = 'Body Scan';

Library Card (Key Fields)

Each book in our library have a unique library card that identifies it.

In CDS views, this is equivalent to the key fields. Key fields uniquely identify each record in our view.

Example: The ISBN or library card of a book is like a key field in a table, ensuring each book is distinct and easily retrievable.

Index (Annotations)

Annotations in CDS views are like footnotes or highlights in our library books — they provide extra information that can be useful for readers or systems using the data.

What are Annotations?

Annotations add metadata to entities, fields, or projections in CDS views. This metadata can provide additional information, control how data is displayed, or enforce certain rules.

Example: An annotation highlights which books provide practical exercises or which ones are written by experts in the field, helping readers quickly identify valuable resources.

Main Entity “MindfulnessPractices” with Annotations:

@AbapCatalog.tableCategory: #TRANSPARENT
@EndUserText.label: 'Mindfulness Practices Techniques'
entity MindfulnessPractices {
    key ID           : Integer;
    
    @EndUserText.label: 'Name of the Practice'
    PracticeName     : String(100); // E.g., 'Deep Breathing', 'Body Scan'

    @EndUserText.label: 'Type of Technique'
    TechniqueType    : String(50);  // E.g., 'Breathing', 'Meditation', 'Walking'

    @EndUserText.label: 'Duration in Minutes'
    Duration         : Integer;     // E.g., 10, 20, 30 minutes

    @EndUserText.label: 'Difficulty Level'
    DifficultyLevel  : String(20);  // E.g., 'Beginner', 'Intermediate', 'Advanced'

    @EndUserText.label: 'Benefits of the Practice'
    Benefits         : String(200); // E.g., 'Reduces anxiety', 'Improves focus'
}

Explanation of Annotations

@AbapCatalog.tableCategory: #TRANSPARENT: Specifies the type of database table (e.g., #TRANSPARENT, #VIEW, #DDL) that will be generated. In this case, a transparent table is created.
@EndUserText.label:This annotation is like putting a title or caption on a section of our bookshelf to make it clear what each field or entity represents. Adds a user-friendly label to fields or entities in CDS Views.

For example, @EndUserText.label: 'Name of the Practice' adds a label to the PracticeName field, so when someone views this data, they see a more descriptive title instead of just the technical field name.

Projections with Annotations

Create a projection for Breathing Exercises with annotations.

@EndUserText.label: 'Breathing Exercises'
entity BreathingExercises as projection on MindfulnessPractices {
    @EndUserText.label: 'Name of the Practice'
    PracticeName,
    
    @EndUserText.label: 'Type of Technique'
    TechniqueType,
    
    @EndUserText.label: 'Duration in Minutes'
    Duration,
    
    @EndUserText.label: 'Benefits of the Practice'
    Benefits
} where TechniqueType = 'Breathing';

Explanation of Annotations in Projection:

@EndUserText.label: 'Breathing Exercises': This annotation gives the entire projection a descriptive name, so users know they are looking at a list of Breathing Exercises.

Annotations are a powerful feature in CDS views, providing additional metadata to control how data is processed, displayed, or integrated with other systems.

Here are some of the most commonly used annotations in CDS views:

@ ObjectModel Annotations

These annotations are used to define object modeling behavior, such as creating associations, determining the behavior of actions, or controlling how the data is exposed in OData services.

Imagine we’re organizing a self-help workshop. We have several roles, activities, and rules in place to ensure everything runs smoothly. The @ObjectModel annotations are like the guidelines and protocols that help us manage the workshop.

@ObjectModel.association.type

This defines a dependent relationship between entities, where one cannot exist without the other.

Metaphor: Relationships between Participants

In our workshop, participants form pairs or small groups for certain activities. We need to define how these relationships work:

#TO_COMPOSITION_CHILD: This is like saying, "These two participants must work together as a team. If one leaves, the other can’t continue on their own."
#TO_COMPOSITE_PARENT: This could be, "This participant is the leader of their group. If they leave, the group disbands."

@ObjectModel.readOnly

Imagine we have some people attending the workshop just as observers — they can watch, but they can’t participate or change anything.

This marks certain data as read-only, meaning it can’t be altered, just like observers in our workshop.

@ObjectModel.usageType

It is like Purpose of the Workshop. Different workshops serve different purposes. Some are for therapy (healing and processing emotions), others for training (learning new skills), and others still for discussion (sharing ideas).
This defines how the data is used or what purpose it serves, much like determining the goal of our workshop.

@ObjectModel.foreignKey.association

Links Between Workshop Sessions. Suppose we have multiple sessions in our workshop, and some participants need to attend a particular session because it's linked to another. For instance, if they attend "Session A: Introduction to Mindfulness," they should also attend "Session B: Advanced Mindfulness."
This sets up a connection or dependency between entities, similar to linking workshop sessions.

@ObjectModel.association.type: #TO_COMPOSITION_CHILD
@ObjectModel.readOnly: true

// Indicates that the CDS view is intended for use in SAP S/4HANA Public Cloud.
@ObjectModel.usageType.serviceQuality: #S4_PUBLIC_CLOUD

//Indicates that the CDS view is intended for use in SAP S/4HANA On-Premise.
@ObjectModel.usageType.serviceQuality: #S4_HANA_ON_PREMISE

//Indicates that the CDS view is intended for analytical purposes, such as in reporting or dashboards.
@ObjectModel.usageType.application: #ANALYTICS

//Indicates that the CDS view is used in transactional contexts, where data changes frequently and operations like create, update, or delete are common.
@ObjectModel.usageType.application: #TRANSACTIONAL

// Indicates that the CDS view is used for search purposes, optimizing it for quick data retrieval.
@ObjectModel.usageType.application: #SEARCH

@ObjectModel.foreignKey.association: 'SessionLink'

@ObjectModel.usageType.semanticKey

Imagine we’re setting up a system to manage a library. Each book in the library has a unique ID number (like B12345). This number is great for computers because it’s unique, but for humans, it doesn’t really mean much. It's just a random string of characters.

Now, let's say we also want to use the ISBN (International Standard Book Number) as a key. The ISBN is a number that people recognize and understand. It’s printed on every book, and it uniquely identifies that book across the world. This ISBN is a semantic key—it’s still unique, but it also carries meaning because it’s widely recognized in the context of books.

How Does This Relate to CDS Views?

In a CDS view, @ObjectModel.usageType.semanticKey is used to mark fields that are meaningful and could be used as an alternative key to identify a record, instead of just relying on a technical ID. This is particularly useful when you want to expose your data in a way that’s more understandable to users or other systems.

@AbapCatalog.sqlViewName: 'MINDFULNESS_VIEW'
@EndUserText.label: 'Mindfulness Practice Data'
define view MindfulnessPractice
{
    key PracticeID          : String(10);  // Technical ID, like 'P12345'

    @ObjectModel.usageType.semanticKey: true
    PracticeName            : String(100); // Semantic Key, like 'Breathing Techniques'

    @ObjectModel.usageType.semanticKey: true
    InstructorName          : String(100); // Semantic Key, like 'Kiran'

    @ObjectModel.usageType.semanticKey: true
    PracticeDurationMinutes : Integer;     // Semantic Key, like 30
}

Explanation

PracticeID: This is like the unique internal code that the system uses to keep track of each mindfulness practice session, similar to a library’s ID for a book.

By marking PracticeName, InstructorName, and PracticeDurationMinutes as semantic keys, we tell the system that these fields carry real-world meaning and can be used to identify the practice sessions in a more understandable way, just like using the title of a mindfulness book rather than just a random PracticeID code P12345.

@ObjectModel.usageType.dataCategory

Imagine we’re organizing chapters in a mindfulness practice book. Each chapter might serve a different purpose—some chapters are core practices that everyone should follow, while others are supplementary tips or additional resources.

In the context of CDS views, @ObjectModel.usageType.dataCategory is used to label data based on its role. It helps us understand whether the data is essential (like core practices), frequently changing (like notes from a recent workshop), or supportive (like reference materials).

@ObjectModel.usageType.dataCategory helps categorize different types of data:

#MASTER: Core data that’s fundamental and doesn’t change often, like the main chapters in our mindfulness book.
#TRANSACTIONAL: Data that records ongoing activities and changes frequently, like notes or exercises we do in each session.
#REFERENCE: Supplementary data that supports the core content, like an index or a list of contributors in the book.

@AbapCatalog.sqlViewName: 'MINDFULNESS_BOOK_VIEW'
@EndUserText.label: 'Mindfulness Practice Chapters'
define view MindfulnessChapters
{
    key ChapterID              : String(10);  // Technical ID, like 'C001'

    @ObjectModel.usageType.dataCategory: #MASTER
    CorePracticeName           : String(100); // Core Practices, like 'Mindful Breathing'

    @ObjectModel.usageType.dataCategory: #TRANSACTIONAL
    RecentSessionNotes         : String(500); // Session Notes, like 'Reflections from Aug 2024 workshop'

    @ObjectModel.usageType.dataCategory: #REFERENCE
    SupplementaryMaterialTitle : String(100); // Supplementary Materials, like 'Guide to Deep Breathing'
}

Catalogs (Associations)

Let’s say we have the following books (tables) in our library:

Book A: Anxiety Management Techniques
Book B: Mindfulness Practices
Book C: Cognitive Behavioral Therapy (CBT)

Now, we want to create an association between these books so that when someone is exploring one book, they can easily find related ones.

Creating an Association:

Catalog Entry (Association Definition)

We create a catalog entry that says, “If a reader is looking at ‘Anxiety Management Techniques’ (Book A), they may be interested in ‘Mindfulness Practices’ (Book B).”

In technical terms, this is an association between the Book A and Book B.

2. Cross-Reference (Using the Association)

When a reader is reading Book A, our catalog directs them to also check out Book B.

Similarly, in a CDS view, if we are querying data from one table, the association allows us to pull in relevant data from the another table.

We can extend this further by linking Book C (CBT) with both Book A and Book B, creating a network of interconnected topics.

This makes our library (or CDS view) more powerful because it provides a holistic view by showing how different topics relate to each other.

How Associations Work in a CDS View:

Syntax: When we define an association in a CDS view, we are essentially creating a “catalog entry” in our library that says, “This data (from Table A) is related to that data (from Table B).”

Usage: When we query the CDS view, we can use these associations to join tables and bring in related data.

Cardinality: Associations can also define the nature of the relationship — whether it’s a one-to-one relationship (like a single book with a single corresponding reference) or a one-to-many relationship (one book that refers to multiple others).

Borrowing Policy (Authorization)

Finally, our library will have a borrowing policy to control who can access certain books.

In CDS views, authorization rules determine who can view or modify the data.

Example: Only certified mental health professionals can access certain specialized books, just like only authorized users can access sensitive data in a CDS view.

By thinking of CDS views as a thoughtfully organized library focused on self-help and mental health, we can better understand how they help structure and present data efficiently, making it easier for users to find exactly what they need.

Leveraging Image Embedding and HANA Vector Engine for Reverse Product Search

2024-08-19T06:16:05.780000+02:00

Introduction

HANA Cloud Vector Engine significantly accelerates the development of Retrieval-Augmented Generation (RAG) applications. Traditionally, RAG solutions have focused on leveraging text data from internal documents. However, with the rise of multimodal AI, as seen with GPT-4o, there is a growing trend towards accepting various input types such as images, audio, and video. To maximize the power of Vector Engine, integrating image similarity search is essential for enhancing its capabilities.

Reference: HANA Database Vector Engine Guide

Use Cases

Imagine a potential customer visiting a friend’s house and discovering an amazing chair. However, his friend can’t remember where he bought it. This is where Reverse Product Search comes into play. The customer takes a picture of the chair and finds that your company sells the exact same model. Reverse Product Search is particularly useful when customers cannot identify a product by name or barcode. It’s also ideal for finding repair parts or similar items.

Image Similarity Search with HANA Vector Engine

Embedding Product Images

The first step is to convert images into num erical data so that a computer can measure the similarity between them. In this prototype, we used OpenAI's CLIP, a zero-shot model that links images and text for cross-modal understanding without task-specific training.

from langchain_experimental.open_clip import OpenCLIPEmbeddings

clip_embd = OpenCLIPEmbeddings(model_name="ViT-g-14", checkpoint="laion2b_s34b_b88k")

This code embeds the product images and stores them in HANA Cloud.

Product images are preloaded into image_storage using the Product Master API.

def process_images(clip_embd, hana_conn, image_storage):
    
    cursor = hana_conn.cursor()
    images = image_storage.get_images()

    csv_rows = []
    for i, img in enumerate(images):
        model = 'OpenCLIP'
        embedding =  clip_embd.embed_image([BytesIO(img["data"])])
        csv_rows.append([i,img["name"],model,str(embedding[0])])

    sql = '''
      INSERT INTO EMBEDDING_CONTENT ("id","content","model","EMBEDDING")
      VALUES (?,?,?,TO_REAL_VECTOR(?))
    '''
    
    try:
        cursor.executemany(sql, csv_rows) 
    except Exception as e:
        hana_conn.rollback()
        print("An error occurred:", e)        
    finally:
        cursor.close()

Retrieving Similar Images

Once the product images are embedded and stored in HANA Cloud, you can retrieve images similar to a photo taken by a smartphone. Specifically, this code embeds the mobile_image and compares the cosine similarity between the mobile_image and each image in HANA Cloud. Finally, it retrieves and ranks the top 5 images based on their similarity scores.

def process_mobile_image(clip_embd, hana_conn, mobile_image):
    
    cursor = hana_conn.cursor() 
    embedding =  clip_embd.embed_image([BytesIO(mobile_image)])
    
    sql = '''
      SELECT TOP 5 "id"
      FROM "EMBEDDING_CONTENT"
      ORDER BY COSINE_SIMILARITY("EMBEDDING", TO_REAL_VECTOR(?)) DESC
    '''
    
    try:
        cursor.execute(sql, (str(embedding[0]),))
        retrieved_products = cursor.fetchall()
    except Exception as e:
        hana_conn.rollback()
        print("An error occurred:", e)        
    finally: 
        cursor.close()
        return retrieved_products

Reverse Product Search with BTP SDK for iOS

Interacting with Product Master API

Product images are managed as attachments of product master data so that they can be easily accessed by application.
To download attachments, you have to use Get All Originals operation to retrieve the metadata of the attachment.
Reference: Get All Originals

GET <host>/sap/opu/odata/sap/API_CV_ATTACHMENT_SRV/GetAllOriginals?LinkedSAPObjectKey='TestEKKO'&BusinessObjectTypeName='EKKO_RFQ'

Using the metadata retrieved, you can download an attachment from the product master data.
Reference: Download Attachment Content

GET<host>/sap/opu/odata/sap/API_CV_ATTACHMENT_SRV/AttachmentContentSet(DocumentInfoRecordDocType='SL1',DocumentInfoRecordDocNumber='10000002076',DocumentInfoRecordDocPart='000',DocumentInfoRecordDocVersion='00',LogicalDocument='1C98EC1818551EDA998508E3A05E0BCE',ArchiveDocumentID='1C98EC1818551EDA998508E3A05E4BCE',LinkedSAPObjectKey='TESTEKKO',BusinessObjectTypeName='EKKO_RFQ')/$value

BTP SDK for iOS

To build an end-to-end solution, I have developed an iOS application using the BTP SDK for iOS. This application captures images, sends them to a server-side application, and displays a ranking of similar products.

Conclusion

As we have explored, integrating Image Similarity Search unveils new potential for HANA Vector Engine. Additionally, seamless integration with SAP technologies such as S/4HANA Product Master and BTP SDK for iOS enables the efficient development of sophisticated and innovative enterprise applications.

References

Enhancing S/4HANA with SAP HANA Cloud Vector Store and GenAI

First steps using the Hana Vector Engine with SAP GEN AI

Mastering SAP HANA Cloud Vector Engine for RAG-driven LLM Applications: An Insightful Guide

New pilots to empower customers to leverage AI and SAP HANA Cloud capabilities to innovate faster

2024-08-19T12:53:19.760000+02:00

Artificial Intelligence or AI is disrupting business everywhere! Organizations are looking at ways to innovate and update their business processes to make best use of AI to make savings and add improved functionality to their customers. As we come to terms with AI’s vast potential, SAP has adjusted its strategy with the goal of becoming not only the leading enterprise application provider but also the number one business AI company. SAP provides opportunities to use AI to unlock new levels of efficiency and innovation in their customer’s businesses.

To support this strategy, in SAP Enterprise Support we are providing two upcoming pilots of Expert Guided Implementation services through the SAP Enterprise Support Advisory Council program designed to empower customers with the knowledge and skills to leverage SAP HANA Cloud’s advanced capabilities and AI to improve efficiency and innovation in organizations.

Expert Guided Implementation (EGI) services are multi-day remote delivered sessions, delivered through by SAP Enterprise Support Academy and typically delivered over 3-5 working days. In short, this is a virtual classroom style session, where an SAP expert explains step-by-step the configuration of a set objective using training materials and step-by-step demonstration.

In our first pilot EGI service, we delve into Multiple Model Programming using the SAP HANA Vector Engine and explore how Generative AI, specifically Retrieval-Augmented Generation (RAG), can transform customer’s data management and AI strategies. The service is designed to provide a comprehensive understanding of SAP HANA Cloud’s advanced features and how they can be leveraged to support cutting-edge AI applications. By the end of the service, customers will be equipped with skills to implement multiple model programming, utilize the Vector Engine, and enhance your AI models with Retrieval Augmented Generation (RAG). Plus, insights into effective data tiering strategies to optimize data management will be provided.

In our second pilot EGI service, we equip customers with the skills and knowledge to harness the power of the SAP Cloud Application Programming (CAP) model in SAP HANA Cloud. The service will introduce you to SAP Build Code and Joule, SAP’s generative AI assistant, to streamline application development processes. The service provides with a comprehensive understanding of the SAP CAP model and how to effectively use SAP Build Code and Joule to accelerate your application development. By the end of the service, customers will have the skills to develop robust CAP applications, utilize SAP HANA Cloud for data management, and leverage generative AI to enhance your coding efficiency.

Please join us for this exciting journey into the world of Generative AI, Joule and SAP HANA Cloud and unlock new possibilities for your business! For further queries including how to join these pilots, reach out to the SAP Enterprise Support Advisory Council directly at mailto:esac@sap.com.

Thanks and Regards,

Robert Kelly

BTP Lead – SAP Enterprise Support

Hands-on Tutorial: Machine Learning with SAP HANA Cloud

2024-08-23T15:29:23.621000+02:00

Since SAP HANA Cloud is a multi-model database management system, it can store, process and enrich different kinds of data (ie tables, geo-spatial, graph) with its in-built engines. This blog shows how to implement a SAP Business AI project with a time-series forecast by using the embedded Predictive Analysis Library.

Trigger a forecast from your favourite Python environment without having to extract the data from SAP HANA Cloud, thanks to our Python package hana_ml. Avoid data duplication (keeping your architecture lean) and benefit from the in-memory power.

The whole code of this project can be downloaded from this repository.

Use case
Architecture / prerequisites
SAP HANA Cloud
Python / Jupyter Notebooks
Time-series forecasting
- Data upload
- Forecasting single time-series
- Forecasting multiple time-series
Going further

FYI, the new blogging platform on the SAP Community now allows for multiple authors to work on the same document. That's our first attempt, and we ourselves at least like the outcome 😀 @AndreasForster and @YannickSchaper

Use case

We have chosen to go through a time-series forecasting example, as this is an extremely common requirement for the most diverse projects. Many standard applications from SAP already have time-series forecasting built in, such as SAP Analytics Cloud or SAP Integrated Business Planning. If you have a requirement, that goes beyond a standard application, you can leverage the SAP Business Technology Platform (BTP), ie SAP HANA Cloud.

In our case now we would like to forecast how many nights foreign tourists or business travellers will spend in Switzerland in the coming months. We are interested in the total numbers, but we also want to know how many nights different nationalities will spend in the country.

Such forecasts of overnight stays can be useful for many purposes, for instance to help decide where to spend a Marketing budget. It can also help the hotel industry to have the right number of staff (and languages) working through the year.

These might just be examples we invented ourselves, based on data that is publicly available (thanks to the opendata.swiss portal!), but such time-series forecasts are used extremely often in the business world. We have used time-series forecasting in many diverse projects, from forecasting the Net Working Capital for Treasury departments to solar power production at a utilities company.

All these use cases have in common, that in such a BTP project an expert (you! 😀 ) creates a forecast that is then shared with the business users, who might not want to know all technical details of how the forecasts were created.

Architecture / prerequisites

To follow this blog's instructions, you need to have access to:

An instance of SAP HANA Cloud, either a productive or free-tier system. The free trial does not have the Predictive Analysis built in. In the next chapter we give some guidance on setting up a free-tier system using a Pay-As-You-Go account, which should allow everyone with a credit card (that we understand will not be charged) to implement the scenario without occurring any costs.
A Python environment. We explain how to set up miniconda, but if you already have an existing Python installation, you should be able to use this as well. Of course, you could also use the Business Application Studio on our Business Technology Platform.
It helps if you have a bit of Python or scripting experience. But we hope this blog can also be a good entry point for someone who is just starting out with Python.
Motivation to get to know SAP HANA's Predictive Analysis Library.

So the overall architecture for this implementation is very straight forward.

Currently the Predictive Analysis Library provides all these algorithms, and the list keeps growing.

SAP HANA Cloud

First things first, you need a SAP HANA Cloud system, in which to execute the Machine Learning. This could be a productive system that you already have access to. You can also use a free-tier instance of SAP HANA Cloud. To allow everyone to follow the steps hands-on, we are using a free-tier option with Pay-As-You-Go account here.

The free trial of SAP HANA Cloud however doesn't contain HANA Machine Learning. If you wonder what the differences are between free-tier and free trial, then this tutorial should help you out. With a paid instance of SAP HANA Cloud the setup of the Machine Learning is slightly differently, you need to activate the script server.

Remember that productive SAP Datasphere systems also have the same Machine Learning built-in, thanks to the embedded SAP HANA Cloud. We plan to release a separate blog for this.

So let's start working with SAP HANA Cloud as free-tier. If you haven't got a system yet, you can follow the below tutorials. Just be aware, that not all BTP components are available in all regions. Make sure to have your BTP subaccount in a region, in which the SAP HANA Cloud free-tier is available. See the Discovery Center for options. We are using "Europe (Netherlands)" on Microsoft Azure.

Start Using SAP HANA Cloud Free Tier in SAP BTP Cockpit
Provision an Instance of SAP HANA Cloud, SAP HANA Database
- Select the "Predictive Analysis Library (PAL) option when creating the SAP HANA Cloud instance
- You can also select "Allow all IP addresses" for now to keep it simple. You can restrict this to specific IP addresses or ranges later on

Once SAP HANA Cloud is running, take note of the instance's SQL endpoint, which is needed to logon to the system from Python.

Now create a Database User for our project. You can execute the code for example in the SAP HANA database explorer, as explained in: Tools to Manage and Access the SAP HANA Cloud, SAP HANA Database

Execute these commands as DBADMIN in the Database Explorer. Just replace YOURSECRETPASSWORD with literally your secret password (the one you had to enter when creating the instance). This creates a user called AIUSER, whose password doesn't expire. This user is also assigned the necessary rights to trigger the Predictive Analysis Library.

CREATE USER AIUSER PASSWORD YOURSECRETPASSWORD NO FORCE_FIRST_PASSWORD_CHANGE;
ALTER USER AIUSER DISABLE PASSWORD LIFETIME;
GRANT AFL__SYS_AFL_AFLPAL_EXECUTE TO AIUSER;
GRANT AFLPM_CREATOR_ERASER_EXECUTE to AIUSER;

Python / Jupyter Notebooks

Use a Python environment of your choice to be able to use Jupyter Notebooks. There are many options, ie with SAP (Business Application Studio) or non-SAP environments. The following steps show how to run Jupyter Notebooks on your local laptop by using the free environment miniconda.

Download and install miniconda. The easiest option is probably the graphical installer.

Open the "Anaconda Prompt" that was installed by minconda. On Windows just use the Operating System's "Search" box to find the prompt.

miniconda allows you to have multiple Python environments side by side. Making changes in one of these environments does not affect the others. This can be very useful to run tests in one environment without interfering with your productive code. Enter the following code into the Anaconda Prompt, execute each line individually. These statements:

create a new Python 3.12 environment (which is currently the stable version of Python) named "sandbox"
activate the new "sandbox" environment, so that the following commands are executed for this environment
install the python package jupyterlab, which, you might have guessed, installs the Jupyter Notebook environment into the blank Python environment
add the "sandbox" envrionment to the Jupyter environment (you will see later, what this means)
and finally, open the Jupyter environment.

conda create --name sandbox python=3.12
conda activate sandbox
pip install jupyterlab
python -m ipykernel install --user --name=sandbox
jupyter lab

Executing these commands will open the Jupyter Lab environment, in which individual Jupyter Notebooks can be executed. Notice the "sandbox" icon in the Notebook section. This icon appears because of the earlier "python -m ipykernel install --user --name=sandbox" command.

Click the "sandbox" icon in the Notebook section and a Jupyter Notebooks opens up, in which you can script your Python code. At the top right you can see that the Notebook is running in the "sandbox" environment. Eventually you might have a number of Python environments, hence it is good to know for sure, in which of these environments the code is executed.

In case you haven't worked with Jupyter Notebooks yet, this introduction to JupyterLab gets you started. Once you are familiar with the Jupyter interface, install the hana_ml Package, that we will use to trigger the embedded Machine Learning. Enter this code into a cell in the Notebook, then execute the code, for example with the "Run" icon on top.

!pip install hana_ml

This downloads and install the hana_ml-package with its dependencies, which can take a few seconds. Once complete, run this code in a new cell, to see the version of the freshly installed package.

import hana_ml
print(hana_ml.__version__)

Time-series forecasting

We are ready for the fun part, everything should be ready to upload some data and create some time-series forecasts. Remember, that all code can be downloaded from this repository. The notebooks that you find there are a little more elaborate than then blog.

Data upload

You find this section's code in the Notebook "010 Data upload.ipynb".

Begin by logging on to SAP HANA Cloud, through the hana_ml package in a Jupyter Notebook. Use the credentials of the AIUSER.

import hana_ml.dataframe as dataframe
conn = dataframe.ConnectionContext(address='YOURENDPOINT',
                                   port=443, 
                                   user='AIUSER', 
                                   password='YOURSECRETPASSWORD', 
                                  )
conn.connection.isconnected()

Now bring the data that we want to upload from this CSV file into a Pandas DataFrame in Python. Display a few rows of the data.

import pandas as pd
df_data= pd.read_csv('OVERNIGHTSTAYS.csv') 
df_data.MONTH = pd.to_datetime(df_data.MONTH, format='%d/%m/%Y') 
df_data.head(5)

The table contains of four columns

The month
The Swiss region
The nationality of the guests
And the number of overnight stays

Then upload the Pandas DataFrame through the hana_ml package into a table in SAP HANA Cloud. The table and its columns are created automatically on the fly.

import hana_ml.dataframe as dataframe
df_remote = dataframe.create_dataframe_from_pandas(connection_context=conn, 
                                                   pandas_df=df_data, 
                                                   table_name='OVERNIGHTSTAYS',
                                                   force=True,
                                                   replace=False)

You can also see the data types of the columns that were created. the df_remote object points to the newly created table, and the dtypes()-method returns their names and column types.

df_remote.dtypes()

Forecasting single time-series

You find this section's code in the Notebook "020 Time-series forecast.ipynb".

Usually you would first spend some time exploring data. We keep this part in the tutorial very short, but we encourage you to spend sufficient time on data exploration in your own projects.

For now retrieve a few rows of data, to make sure that there are no obvious problems (ie empty rows). The head()-method first restricts the dataset to avoid downloading unnecessary data. The collect()-method then downloads the data into Python (as Pandas DataFrame) to display the data.

df_remote = conn.table('OVERNIGHTSTAYS')
df_remote.head(5).collect()

Get a quick feel for the content of the tables. The describe()-method instructs SAP HANA Cloud to calculate for instance how many values are missing per column, or what the min / max / median values are for numerical columns.

df_remote.describe().collect()

Each of the values above was calculated in SAP HANA Cloud. Because the describe()-method was called, hana_ml created the required SELECT statement, which you can access as well. You can execute this SQL-statement also in other environments to retrieve the results.

print(df_remote.describe().select_statement)

You can also get a more graphical overview of the dataset in a canned report.

from hana_ml.visualizers.unified_report import UnifiedReport
UnifiedReport(df_remote).build().display()

We move to the data preparation for our use case. We want to create a single forecast of all overnight stays by month. Hence we need to aggregate all detailed values by month. The hana_ml-DataFrame has a method for that aggregation. Remember, that this aggregation is also virtual. Essentially, it is adding a GROUP-clause to the hana_ml-DataFrame, which is a SELECT statement under the hood. Also look at a few rows of the monthly aggregates.

df_rem_agg = df_remote.agg([('sum', 'OVERNIGHTSTAYS', 'OVERNIGHTSTAYS_SUM')], group_by='MONTH')
df_rem_agg = df_rem_agg.sort('MONTH')
df_rem_agg.head(5).collect()

That means we can train a time-series model. We used the AdditiveModelForecast algorithm, which is also known as Prophet.

from hana_ml.algorithms.pal.tsa.additive_model_forecast import AdditiveModelForecast
amf = AdditiveModelForecast()
amf.fit(data=df_rem_agg)

View the trained model in another canned report.

from hana_ml.visualizers.unified_report import UnifiedReport
UnifiedReport(amf).build().display()

We have a trained time-series model, but we have not yet created a prediction. To create a forecast, we first need to create a hana_ml DataFrame, that contains the dates/months we would like forecasts for. Hence find out the most recent date of the training history: 2024-05-01 (May 1st 2024).

str_lastdate = df_rem_agg.tail(1, ref_col='MONTH').collect().iloc[0,0]
str_lastdate = str(str_lastdate)[0:10]
print(str_lastdate)

Based on the last known above date/month, create a new hana_ml DataFrame that lists the following 12 months.

from hana_ml.algorithms.pal.random import binomial
months_to_forecast=12
df_rem_future = binomial(conn, n=1, p=1, num_random=months_to_forecast)
df_rem_future = df_rem_future.select('*', (f'''ADD_MONTHS(TO_DATE ('{str_lastdate}', 'YYYY-MM-DD'), ID+1)''', 'MONTH') )
df_rem_future = df_rem_future.select('MONTH', ('0', 'TARGET'))
df_rem_future.head(20).collect()

Now apply the trained time-series model to predict the overnight stays in those 12 months.

df_rem_predicted = amf.predict(data=df_rem_future)
df_rem_predicted.head(5).collect()

The canned report for the trained model now also shows the above forecast. You can clearly see how a repeating pattern was identified and used to predict the future dates. Most overnight stays are in the summer (hiking?) but the winter also has its peaks (skiing and snowboarding!).

from hana_ml.visualizers.unified_report import UnifiedReport
UnifiedReport(amf).build().display()

To share the forecasts with business users it's often best to combine the training data and forecasts into a single dataset. Here we combine both parts into a single hana_ml DataFrame.

df_rem_predicted = df_rem_predicted.select('MONTH',
                                          ('NULL', 'OVERNIGHTSTAYS_SUM'),
                                          ('YHAT', 'FORECAST'),
                                          ('YHAT_LOWER', 'FORECAST_LOWER'),
                                          ('YHAT_UPPER', 'FORECAST_UPPER'))
df_rem_agg = df_rem_agg.select('*', 
                               ('NULL', 'FORECAST'),
                               ('NULL', 'FORECAST_LOWER'),
                               ('NULL', 'FORECAST_UPPER'))
df_rem_all = df_rem_predicted.union(df_rem_agg)
df_rem_all.sort('MONTH').tail(5).collect()

Now save the hana_ml DataFrame to a table in SAP HANA Cloud. SAP Analytics Cloud for instance can now access the data.

df_rem_all.save('OVERNIGHTSTAYS_FORECAST_TOTAL', force=True)

You have created a time-series forecast!

Forecasting multiple time-series

You find this section's code in the Notebook "30 Multiple time-series forecast.ipynb".

In the previous example we aggregated all overnights stays into monthly values and forecasted the following 12 months. Now we want to create individual forecasts for the countries from which most visitors are coming. Hence let's find the top 10 countries with the most overnight stays.

df_remote = conn.table('OVERNIGHTSTAYS')
df_remote.agg([('sum', 'OVERNIGHTSTAYS', 'TOTAL')], group_by='COUNTRYOFRESIDENCE' ).sort('TOTAL', desc=True).head(10).collect()

We want to use that list of countries in a filter on the whole table. One way of achieving this, is to create a comma separated list of values for a where clause.

countries = df_remote.agg([('sum', 'OVERNIGHTSTAYS', 'TOTAL')], group_by='COUNTRYOFRESIDENCE') \
     .sort('TOTAL', desc=True).select('COUNTRYOFRESIDENCE').head(10).collect() \
     .iloc[:,0].tolist()
countries = str(countries)
countries = countries.replace('[', '(')
countries = countries.replace(']', ')')
df_remote = df_remote.filter(f'''"COUNTRYOFRESIDENCE" IN {countries}''')

The hana_ml DataFrame now only holds information about those 10 countries. Now we aggregate the data again, but this time by both month and country, so that we can create country-specific forecasts.

df_rem_agg = df_remote.agg([('sum', 'OVERNIGHTSTAYS', 'OVERNIGHTSTAYS_SUM')], group_by=['MONTH', 'COUNTRYOFRESIDENCE'])
df_rem_agg = df_rem_agg.sort('MONTH')
df_rem_agg.head(5).collect()

Let's see the historic values of the 10 countries.

df_data_plot = df_rem_agg.pivot_table(values='OVERNIGHTSTAYS_SUM', index='MONTH', columns='COUNTRYOFRESIDENCE', aggfunc='sum').collect()
df_data_plot.plot(x='MONTH').legend(loc='center left',bbox_to_anchor=(1.0, 0.5));

Since the plot is quite full, let's focus on the 2 most popular foreign countries, Germany and the United States.

df_data_plot[['MONTH', 'Germany', 'United States']].plot(x='MONTH').legend(loc='center left',bbox_to_anchor=(1.0, 0.5));

There are similarities in the overnight stays from the two countries (summers are most popular). However, numbers from the United States seem to be rising, while numbers from Germany seem to be dropping. Individual forecasts for each country can capture such individual patterns.

Train individual models with the same AdditiveModelForecast algorithm as before, but now we specify the country of residence as group key, which produces individual models for each country.

from hana_ml.algorithms.pal.tsa.additive_model_forecast import AdditiveModelForecast
amf = AdditiveModelForecast(massive=True)
amf.fit(data=df_rem_agg, group_key='COUNTRYOFRESIDENCE')

As before, we now create a hana_ml DataFrame for the prediction. This DataFrame must list all the countries as well as all the dates for each country. The list of dates/months, as done before.

from hana_ml.algorithms.pal.random import binomial
months_to_forecast=12
str_lastdate = df_rem_agg.tail(1, ref_col='MONTH').collect().iloc[0,0]
str_lastdate = str(str_lastdate)[0:10]
df_rem_future = binomial(conn, n=1, p=1, num_random=months_to_forecast)
df_rem_future = df_rem_future.select('*', (f'''ADD_MONTHS(TO_DATE ('{str_lastdate}', 'YYYY-MM-DD'), ID+1)''', 'MONTH') )
df_rem_future = df_rem_future.select('MONTH', ('0', 'TARGET'))

But now we create a cartesian product with the countries.

df_rem_countries = df_remote.agg([('sum', 'OVERNIGHTSTAYS', 'TOTAL'  )], group_by='COUNTRYOFRESIDENCE' ) \
                            .sort('TOTAL', desc=True).select('COUNTRYOFRESIDENCE').head(10)
df_rem_topredict = conn.sql(df_rem_future.add_id().set_index('ID').join(df_rem_countries.add_id('ID').set_index('ID'), how='cross' ) \
                   .select_statement.replace('ON T0."ID" = T1."ID"', ''))
df_rem_topredict = df_rem_topredict.drop('ID')
df_rem_topredict.head(10).collect()

Apply the individual models on its corresponding dates/months in the prediction DataFrame. Here the predict() method returns multiple objects. The forecast we need are in the first (with index 0).

df_rem_pred = amf.predict(data=df_rem_topredict, group_key='COUNTRYOFRESIDENCE')
df_rem_predicted = df_rem_pred[0]

That's all it took to mass produce individual time-series models and forecasts. To verify that the forecasts are individual, look at the data for Germany...

df_data = df_rem_predicted.filter(''' "GROUP_ID" = 'Germany' ''').collect()
df_data.plot(x='MONTH').legend(loc='center left',bbox_to_anchor=(1.0, 0.5));

... and the United States.

df_data = df_rem_predicted.filter(''' "GROUP_ID" = 'United States' ''').collect()
df_data.plot(x='MONTH').legend(loc='center left',bbox_to_anchor=(1.0, 0.5));

Combine the historic data with the predictions and save them to a single table.

df_rem_predicted = df_rem_predicted.select(
                                  'MONTH',
                                  ('GROUP_ID', 'COUNTRYOFRESIDENCE'),
                                  ('NULL', 'OVERNIGHTSTAYS_SUM'),
                                  ('YHAT', 'FORECAST'),
                                  ('YHAT_LOWER', 'FORECAST_LOWER'),
                                  ('YHAT_UPPER', 'FORECAST_UPPER')
                                 )
df_rem_agg = df_rem_agg.select('*', ('NULL', 'FORECAST'),
                                  ('NULL', 'FORECAST_LOWER'),
                                  ('NULL', 'FORECAST_UPPER')
                                  )
df_rem_all = df_rem_predicted.union(df_rem_agg)
df_rem_all.save('OVERNIGHTSTAYS_FORECAST_COUNTRIES', force=True)

You have the data ready to be shared with business users, for instance through SAP Analytics Cloud. Of course you can also see the data here in Python. Select a country and put the data into a plot.

import matplotlib.pyplot as plt
df_data = df_rem_all.filter('''"COUNTRYOFRESIDENCE" = 'Germany' ''').collect()
fig, ax = plt.subplots()
ax.fill_between(df_data['MONTH'].values, df_data['FORECAST_LOWER'].values, df_data['FORECAST_UPPER'].values, alpha=0.2)
ax.plot(df_data['MONTH'].values, df_data['OVERNIGHTSTAYS_SUM'].values, '-')
plt.xticks(rotation=45);

Now you are the expert in massing producing time-series forecasts with SAP HANA Cloud!

Going Further

This final chapter has a few ideas on how to continue from the above.

Secure logon

To avoid having to use the Database user and its password in clear text, you can also refer to logon credentials kept in the Secure User Store from the SAP HANA client. This command stores the credentials under the key "MYHANACLOUD".

C:\Program Files\SAP\hdbclient>hdbuserstore -i SET MYHANACLOUD "YOURSQLENDPOINT:443" AIUSER

You can use this key to logon to SAP HANA Cloud.

import hana_ml.dataframe as dataframe
conn = dataframe.ConnectionContext(userkey='MYHANACLOUD')
conn.connection.isconnected()

Scheduling

You can automate the creation of new forecasts by scheduling the hana_ml logic. One option is described in the blog Scheduling Python code on Cloud Foundry.

Restrict IP addresses

In the above steps we kept it simple and allowed all IP addresses to connect to SAP HANA Cloud. See the SAP HANA Cloud Administration Guide on allowing only specific IP addresses or ranges.

An easy option to determine your external IP address is a small piece of code like:

from requests import get
ip = get('https://api.ipify.org').text
print(f'My public IP address is: {ip}')

Improve forecast accuracy with external variables

Providing additional context can increase the forecast accuracy. One customer is using SAP's factory calendar table TFACS to flag working days in a daily forecast. For our monthly data of overnight stays in Switzerland, the exchange rates of the different currencies to the Swiss Franc could be useful for example. See the holiday and exog parameters of the fit()-method to pass such context to the algorithm.

Summary and next steps

We hope this hands on blog post enables you to supercharge your SAP HANA Cloud with Machine Learning. Transform your SAP data into actionable insights and harness the full potential of HANA ML!

To close off this tutorial, we have a few ideas for you to continue your journey:

📥Get hands-on: If you have scrolled through the blog, take the time to implement the scenario yourself. You can download the notebooks and learn by doing.

📚Learn more:

Go through SAP HANA ML examples and try this other hands-on tutorial, here using a classification
Explore the documentation of Machine Learning in SAP HANA Cloud and the hana_ml Python package
Join one of the regular HANA ML code sessions. You can check here or reach out directly to Witalij Rudnicki.
Should you happen to speak German, there is a whole book on "Data Science mit SAP HANA"

🔗Join the Discussion: Share your experiences in the comments and connect with us.

Start your migration to SAP HANA Cloud now!

2024-08-23T18:58:45.674000+02:00

SAP HANA Cloud, a cloud-native database solution, is the top choice for developers creating intelligent data applications that prioritize successful business processes, enhanced user experiences, and elevated analytics. It is an all-in-one solution that underpins modern applications and analytics sturdily and cost-effectively. As the cloud-based data foundation for SAP Business Technology Platform (SAP BTP), it orchestrates mission-critical applications and analytics across your enterprise data in one comprehensive solution.

SAP HANA Cloud was built to support the most innovative applications. In addition, SAP HANA Cloud is key for supporting SAP customers’ clean core strategies, as a side-by-side solution for offloading custom objects and applications.

The time is now to move to SAP HANA Cloud. So, the question is, how do you get there?

The SAP Migration Factory for SAP BTP program is a high-touch, concierge-model consulting service that puts you on the fast track toward realizing the advantages of SAP HANA Cloud. Expert guidance services are delivered remotely by seasoned teams from SAP or approved SAP partners. The program is a global initiative to assist customers with architecture optimization, modernization risk management, and end-to-end migration strategy to SAP HANA Cloud. The result is a modernized IT landscape optimized for intelligence.

Benefits and services include:

Free migration assessments that can produce business cases, detailed statements of work, and estimates of effort and cost
Modernization of data landscape with SAP HANA Cloud to empower your intelligent enterprise
Low-risk migration with defined services and expertise
Rapid realization of value through tools, assets, and proven methods expediting migration
Support provided by highly skilled and experienced experts on migration to SAP HANA Cloud

SAP and its partners offer a range of additional options for services, support, tools, and assets that can put you on the fast track to database modernization. Explore them online or via our comprehensive brochure.

To learn more about SAP's database migration program and to get started, customers can check out the resources linked to above, speak with their SAP Account Executive or reach out to migrationfactory@sap.com.

It's time to begin the journey to modernization and digital transformation today!

SAP Community - SAP HANA Cloud

Innovating Towards a Sustainable Future: Insights from Rolf Adam

New Machine Learning features in SAP HANA Cloud database release 2024 Q2

Classification and Regression enhancements

Text Processing

Python ML client (hana-ml) enhancements

SAP CodeJam HANA ML In 's-Hertogenbosch 2024-06 Recap

What’s New in SAP HANA Cloud – June 2024

Innovations in SAP HANA Cloud, SAP HANA Database

Administration and Service Management

Support for larger instances

Managing database instances in a Cloud Foundry space, from the multi-environment tools

Management of multiple variant SQL plans based on different parameter values

Integration of catalog object browsing in the administration tool

Consumption monitor for visualizing unit consumption

SAP HANA Cloud Multi-Model Data Processing

Multilabel classification and outlier detection in machine learning

Spatial function to measure the similarity between two shapes

Database Migration

Extension of SAP Business Application Studio to support design-time object migrations

Insights into migration duration

Innovations in SAP HANA Cloud, data lake

Support for the cloud connector for the data lake component

Further Innovations & Enhancements

Database Application Development

Ability to self-tune SQL performance with the SQL plan advisor

Adding the capability to create fabric virtual tables in SAP HANA deployment infrastructure

Graphical editor for hdbgrants and hdbrevokes files in SAP Business Application Studio

Graphical View Modeling

SAP CodeJam HANA ML in Berlin 2024-06 Recap

A Closer Look at the New Metering Metrics added to Metrics Service from SAP HANA Cloud 2024 QRC2

Metering metrics list

How to use metrics service tutorial

Consumption monitor in HANA Cloud Central

Conclusion

SAP HANA Cloud release June 2024 Round-up

What’s New Teaser

What’s New blogpost

What's New Webinar

Additional information

SAP Data and Analytics: Join our Performance focused Live Sessions Series

Check the Availability of Your Multi-Environment HANA Cloud Instances with SAP Automation Pilot

Introduction

How does the HANA Cloud Availability Check work?

Step-by-Step Guide

Prerequisites

Wrap-up

Powerful way to create a CDS view based report on BOMs in SAP S4 HANA Cloud (Public Edition)

Delta configurationfor Standard API in S/4 HANA cloud

SAP Application Migration Assistant

Problem:

Solution:

Preparation:

Summary:

Outlook:

Links:

Connect SAP Sales Cloud V2 to SAP Datasphere

SAP Integration Strategy - End-to-end business processes deep dives - NEW eBook available

Backup of SAP HANA Database on GCP using backup & DR services

SAP HANA BACKUP ON GCP

Create the database backup user from HANA Studio:

Concepts of Core Data Services (CDS)

Metaphor: Building a Custom Library

1. Books (Tables)

2. Bookshelves (CDS Views)

3. Sections (Projections)

Library Card (Key Fields)

Index (Annotations)

What are Annotations?

Explanation of Annotations

Projections with Annotations

Explanation of Annotations in Projection:

@ObjectModel.usageType.semanticKey

How Does This Relate to CDS Views?

Explanation

@ObjectModel.usageType.dataCategory

Catalogs (Associations)

Borrowing Policy (Authorization)

Leveraging Image Embedding and HANA Vector Engine for Reverse Product Search

New pilots to empower customers to leverage AI and SAP HANA Cloud capabilities to innovate faster