SAP Community - Data and Analytics

From Intelligent Applications to Trusted Data: Accelerating Insight to Action for Business Analysts

2025-11-26T17:00:00.025000+01:00

Business analysts today are expected to turn complex datasets into insight faster than ever; analyzing trends, spotting opportunities, and ultimately providing line-of-business leaders with the information necessary to make informed decisions. To meet these demands, they need tools that simplify analysis, ensure data quality, and accelerate their path from question to actionable insight.

At SAP TechEd 2025, SAP announced a suite of advancements designed to do just that—continuing to drive momentum across its data and AI portfolio. New intelligent applications, expanded access to curated data products in SAP Business Data Cloud, and deeper AI-driven assistance powered by Joule empower analysts to work smarter, make faster decisions, and deliver insights with confidence, all while maintaining governance and accuracy at every step.

Intelligent Applications: Purpose-Built Insights for Every Line of Business

Intelligent applications in SAP Business Data Cloud continue to evolve, giving analysts prebuilt, AI-driven insights across domains like finance, HR, supply chain, and more. By embedding more intelligence directly into everyday workflows, these applications let analysts start with much of the foundational work already modeled, monitored, and enriched with AI—so they can focus on understanding trends, identifying gaps, and answering the “why” behind the data, rather than the mechanics of assembling it.

The Value of Intelligent Applications at a Glance:

Consistent, trusted data sources resulting in reliable, repeatable insights
Less manual prep work and more time spent analyzing and interpreting
Shorter analysis cycles through immediate access to prebuilt dashboards
Higher trust and alignment through unified metrics and premodeled insights
Clearer explanations behind anomalies, trends, and performance drivers
AI-enhanced recommendations tailored to each line of business

Intelligent Applications by Line of Business

Key Updates and Expanded Use Cases

Following up on the intelligent application announcement made at SAP Sapphire in 2025, SAP has expanded both previews and general availability. Spend Intelligence, Supply Chain Intelligence, and Revenue Intelligence are now in restricted public preview, while Finance Intelligence, People Intelligence, and Cloud ERP Intelligence private packages are generally available. Together, these additions broaden the library of ready-to-use insights business analysts can tap into with minimal setup.

People Intelligence: New HR-focused use cases now deliver deeper insight into recruiting, learning effectiveness, and employee performance. Analysts gain more accurate attrition predictions and intuitive dashboards that make it easy to compare trends and risks across business units.
Revenue Intelligence: Expanded capabilities provide revenue and sales leaders with a real-time, holistic view of pipeline performance. These updates combine continuously learning ML forecasts, automated variance detection, and unified insights across sales, finance, and operations to support more predictable planning and decision-making.
Consumer Products Intelligence: A new intelligent application for consumer goods manufacturers helps optimize trade promotion planning and manage spending with greater precision. Features include real-time price elasticity analysis, promotion effectiveness insights, margin optimization tools, and streamlined workflows for executing financial operations.

Trusted Data Products Fueling Reliable, Reusable Insights

Data products remain one of the most impactful pillars of SAP Business Data Cloud—especially for analysts who need quick access to context-rich and ready-to-use business data. Instead of repeatedly rebuilding the same datasets for each analysis or project, analysts can tap into governed, out-of-the-box data products that are automatically kept up to date. This not only eliminates duplication but also ensures that everyone across functional teams are working from the same source of truth. By standardizing reusable models, metrics, and pipelines, analysts gain a foundation that speeds up reporting cycles and reduces back-and-forth with technical teams.

In summary, here's what data products bring to the table for business analysts:

Reusable datasets reduce manual preparation and accelerate reporting cycles
Shared metrics and models ensure consistent definitions across teams
Faster insight generation with less time spent requesting or rebuilding data
Increased trust in data quality, making findings easier to validate and share

Momentum Continues: More Data Products Across SAP Applications

SAP is expanding its catalog of ready-to-use data products across critical application areas, including SAP Cloud ERP, SAP SuccessFactors, sustainability, and customer experience. These additions will be available in SAP Business Data Cloud in H1 2026, giving business analysts access to richer, more discoverable datasets to support planning, reporting, and advanced analysis.

Overview: Data Products in SAP Business Data Cloud

AI Assistants and Augmented Workflows: Work Smarter, Act Faster

SAP’s latest advancements in AI assistants and automation deliver clear benefits to business analysts. SAP Analytics Cloud (SAC) already offers strong AI capabilities, including natural language querying and machine learning-powered insights, that provide analysts with a robust foundation for faster data exploration and discovery.

SAP is extending SAC with even more powerful, automated, and generative workflows designed to accelerate both analysis and planning:

Story Generation Agent – Automatically transforms raw data, business context, and user prompts into complete, insight-rich SAC stories or intelligent applications. It assembles visualizations, narrative structure, and recommendations using reusable components and user preferences.
AI-assisted workflows for planning and analytics – Includes comment summarization, comment translation, and natural language generation of advanced formulas. Analysts can describe complex planning logic conversationally and have SAC generate or refine the script behind it.
Joule embedded natively in SAC – Provides in-context conversational support to help analysts refine queries, adjust dashboards, troubleshoot logic, or accelerate iterative analysis directly within the product.

Joule Capabilities in SAP Analytics Cloud (2026)

Why This All Matters for Business Analysts

Across intelligent applications, curated data products, and AI-assisted workflows, the message is clear: SAP is removing the friction that slows analysts down and elevating their work to more strategic, impactful roles. Built natively into SAP Business Data Cloud, these capabilities ensure analysts can work within a single, unified environment where data, analysis, and decision support are seamlessly connected. Analysts can now rely on workflows that are more automated, consistent, and cross-functionally integrated. The outcome: faster answers to critical business questions, clearer recommendations for business leaders, and more time spent on the high-value analytical work that drives real business impact.

To learn more about SAP Business Data Cloud and SAP’s latest announcements:

Visit SAP Business Data Cloud on sap.com
Read the H2 2025 SAP Innovation Guide for key highlights
Watch the replays from SAP TechEd 2025 virtual sessions:
Get hands-on with the SAP BDC Getting Started workshop series (on-demand)

In addition, you can explore other innovations from SAP BDC by persona: for Data Stewards/Owners and Data Scientists.

🚀 Launch Alert! Become a Data Architecture Powerhouse: Your Foundational Learning Journey

2025-12-02T06:14:39.976000+01:00

To equip data professionals with the skills to bridge technical expertise and strategic business impact, SAP Data Architecture experts and the SIX learning team are thrilled to announce the launch of the Data Architect Learning Journey (Foundational). This essential first stage of the Becoming a Data Architect curriculum is designed to empower aspiring architects, data professionals, and solution experts to master the core principles, modern paradigms, and critical frameworks that define successful enterprise data architecture.

🏗️ Structure: Building Expertise from the Ground Up

This learning journey is structured methodically, ensuring you build expertise layer by layer, from foundational theory to strategic impact.

The journey begins with Unit 1: Foundational Concepts, where you will anchor your understanding in the essential principles of modeling, metadata management, and architectural alignment. You will master basic terminologies and fundamentals, including Conceptual, Logical, and Physical data models, and understand how data architecture integrates with enterprise frameworks like Zachman and TOGAF.

Next, Unit 2: Modern Paradigms & Integration takes you into the contemporary world of data patterns. You'll explore and compare models like Data Mesh, Data Fabric, Data Lake, and Data Lakehouse, learning how to select the right approach for any scenario. This unit also delves into integration essentials, comparing and contrasting approaches such as ETL and ELT.

Unit 3 focuses on the critical need for Governance, Security & Strategic Impact. As data ecosystems expand, governance becomes the bedrock of trust. You will learn to establish comprehensive data governance frameworks (including stewardship and data lineage) and integrate crucial security and privacy controls to ensure compliance and secure modern use cases like AI/ML.

The journey culminates in Unit 4: Communicating Data Value. A great architect is also a great leader. This final unit strengthens the communication and leadership skills needed to influence change, teaching you how to articulate the limitations of the current ("as-is") data landscape and present a compelling, business-focused narrative for the future ("to-be") architecture, focusing on quality, cost, and scale.

🎯Who Should Enroll? Shift from Technical Expert to Strategic Leader

This curriculum is ideal for data professionals at every stage ready to evolve from technical implementers into strategic architectural leaders. This includes Data Engineers and Modelers seeking advancement, Solution and Enterprise Architects aiming to solidify their data design expertise, BI and Analytics Professionals who need architectural context, and IT and Transformation Leaders focused on aligning data systems with business outcomes.

✅Why Start This Journey Now?

Modern organizations demand architects who master both technology and business value. By mastering these principles, you will gain skills that directly translate into competitive advantages: you'll improve data quality and governance, learn how to connect data architecture directly to enterprise goals, and gain the credibility needed to influence strategic decisions. Crucially, this course sets the definitive foundation for the planned SAP Certified Data Architect specialization* launching in 2026!

Visit SAP Learning and enroll in the Becoming an SAP Data Architect – Foundational Learning Journey Today! Becoming an SAP Data Architect (Foundational)

🚀 Ignite Your Data Architecture Journey: Empower Enterprise and Data Architects Today!

2025-12-03T05:18:15.798000+01:00

In the era of digital transformation, data is the heartbeat of innovation, strategy, and impactful decision-making. Strong Data Architecture is the foundation that powers every thriving enterprise. To bridge technical expertise with strategic business outcomes, the SAP Data Architecture experts alongside the SIX learning team proudly unveil the Data Architect Learning Journey (Foundational).

This inaugural phase of the Becoming a Data Architect program is crafted to equip aspiring architects, data professionals, and solution experts with essential frameworks, modern methodologies, and core principles that shape successful enterprise data architectures.

🏗️ Layered Learning: From Fundamentals to Enterprise Impact

This journey guides you through a carefully designed progression to build your expertise methodically:

Unit 1: Foundational Concepts anchors you in the fundamentals—mastering modeling techniques, metadata management, and the integration of data architectures with enterprise frameworks such as Zachman and TOGAF. Grasp the essentials of Conceptual, Logical, and Physical models that form the backbone of data design.
Unit 2: Modern Paradigms & Integration immerses you in today’s evolving landscape, exploring advanced patterns like Data Mesh, Data Fabric, Data Lake, and Data Lakehouse. Understand when and how to apply each, plus gain insights into key integration frameworks, including the nuances between ETL and ELT strategies.
Unit 3: Governance, Security & Strategic Value addresses the growing need to establish trust and compliance within expansive data ecosystems. Delve into comprehensive data governance frameworks—data stewardship, lineage, and privacy safeguards that secure modern architectures and enable AI/ML innovations.
Unit 4: Communicating Data Value sharpens your leadership voice, guiding you to communicate complex architectural changes clearly and compellingly. Learn to narrate the journey from the current state to future architecture, emphasizing quality, cost-efficiency, and scalability to align with business priorities.

🎯Who Should Embark?

Designed for data professionals ready to evolve beyond technical roles into influential architectural strategists, this foundation suits:

Data Engineers and Modelers aspiring to architect-level roles
Solution and Enterprise Architects deepening their data design acumen
BI and Analytics experts seeking architectural context
IT and Transformation leaders aligning data strategies with business goals

✅Why Now? Gain a Strategic Edge

Today’s enterprises demand architects who not only understand technology but who can also drive meaningful business outcomes. This journey empowers you to enhance data quality, governance, and alignment with enterprise strategies—skills that translate directly into leadership credibility and competitive advantage. Plus, it sets the stage for the upcoming SAP Certified Data Architect specialization, launching in 2026.

🛣️ Begin Your Path to Architecting Tomorrow

Ready to elevate your data architectural mindset and lead transformative initiatives? No prerequisites required—start your journey today!

Enroll now at SAP Learning: Becoming an SAP Data Architect – Foundational Learning Journey!

Welcome to the Data and Analytics Learning Group

2025-12-04T14:13:56.115000+01:00

We are very excited to launch this brand-new learning group for all learners of SAP solutions in the Data and Analytics space.

Here you can connect with SAP experts and fellow learners across all data and analytics topics.

The Data and Analytics learning group is where you can be first to learn about our plans for the introduction of new courses and the updates to existing courses. You will be first to learn about new live sessions and certification updates.

The focus of this community is learning data and analytics topics. It is not meant to replace the existing SAP Community group that focuses on product-based discussions and trouble-shooting. Those communities still exist and should always be used whenever you have a question or want to discuss a topic around a detailed product feature, or need help with trouble-shooting a product-based issue. There, you are more likely to reach a huge number of developers and consultants who will be able to support you.

This learning community is a place to ask questions and join in discussions around SAP learning content such as courses, learning journeys, certification and live sessions. This is a safe space to ask even the most basic questions as you develop your knowledge. Questions are invited that relate to the exercises and systems used in our learning journeys and courses. However, this is not the place to reach out for support when you are trouble-shooting product issues that you encounter on your own systems or your customer's systems.

Always remember, everyone in this group is a learner, just like you. So don't hold back with your questions.

We are here to help you get started and to support you if you run into trouble with topics in the learning content. We are also here to help you prepare for certification. Look out for blog posts from the experts that will help with on your learning journey.

The solutions that we cover in this community include:

SAP Business Data Cloud
SAP Datasphere
SAP Analytics Cloud
SAP Databricks
SAP BW and SAP BW/4HANA
SAP BusinessObjects
SAP S/4HANA Embedded Analytics
SAP HANA Modeling and Data Provisioning
Developing data models using ABAP CDS

The community is moderated by SAP experts who are instructors and learning content developers. This means that you are connecting with the team who developed the course materials and certifications and who present the live sessions.

Make sure you click Join so that you can ask questions, jump into discussions, create blog posts, and add your responses to posts. If you don’t join you will only be able to browse the posts in this community.

Also, don’t forget to subscribe to the learning community and individual posts or tags, so you don’t miss any updates. One final thing: remember to set your notification preferences so you receive emails when a post that you subscribed to has been updated.

SAP Community provides a separate place for discussions and questions, but we thought we’d try and simply things for you by combining them. After all, a question often leads to a discussion and vice-versa. So please use the Discussions button to start a discussion and to ask questions.

Why not introduce yourself to members of this community and let us know a little about your SAP journey? What brought you here? What are you hoping to get from this community? Simply post a reply here and let us know.

Make sure you spread the word about this community. Post a link in your social media so that we can build a vibrant and effective community of data and analytics learners.

As with all SAP communities, we want this to be a safe space for our members. Please familiarize yourself with the SAP Community Rules of Engagement

Welcome aboard. Let’s get connected!

Mark Green
Moderator: SAP Community - Data and Analytics Learning Group

Embedded Analytics for SAP Sales & Service Cloud v2 - List of Available Tables Reporting & Dashboard

2025-12-05T15:53:26.048000+01:00

SAP Sales & Service Cloud Version 2 (V2) introduces a modernized approach to analytics by embedding SAP Analytics Cloud (SAC) directly into the platform. This integration provides powerful reporting and dashboard capabilities without relying on external tools, enabling real-time insights and advanced visualization options.

Note :

Embedded Analytics provided in SAP Sales & Service Cloud v2 is based on the embedded edition of SAP Analytics Cloud (eSAC). It is not the full version of SAP Analytics Cloud and therefore offers a limited set of functionalities compared to the standalone SAC solution.

What is Embedded Analytics in V2?

Unlike previous versions, V2 does not have native analytics. Instead, it leverages SAP Analytics Cloud, embedded edition (eSAC) as the single analytics engine across SAP solutions. This ensures:

Unified reporting experience across Sales and Service processes.
Real-time data analysis using standard V2 data sources.
Advanced visualization options like charts, tables, KPIs, and interactive dashboards.

Key Features

Design Stories Workcenter for administrators to create dashboards.
Analysis Workcenter for end-users to view and interact with stories.
Role-based access for personalized reporting.
Calculated Columns & Filters for custom metrics.
Single Sign-On (SSO) for secure access to eSAC

Available Data Sources & Tables

Embedded SAC uses the same standard data sources as native Business Analytics in v2. These include:

Opportunity Data (Forecast vs. Actuals)
Account & Contact Information
Activity & Interaction Logs
Service Tickets / Cases (Status, Escalation, Source Channels)
Sales Orders & Quotes
Lead Management Data
Product & Pricing Tables

Each data source can be extended with:

Filters (e.g., by region, status, time frame)
Calculated Columns (e.g., revenue variance, SLA compliance)
Aggregations for KPIs.

Reporting & Dashboard Capabilities

With embedded SAC, you can:

Build Stories combining charts, tables, and text.
Create Numeric Charts for KPIs like Open Tickets or Revenue.
Use Color Coding & Conditional Formatting for better insights.
Enable Drill-down Analysis for detailed exploration.
Share dashboards securely across teams

Benefits for Sales & Service Teams

Sales Managers can track pipeline health, forecast accuracy, and order conversion.
Service Managers can monitor ticket resolution times, escalation trends, and customer satisfaction.
Executives get consolidated dashboards for strategic decision-making.

Available Tables for Reporting & Dashbaord

Available Table Names	Techncial Name
Individual Customer	individualcustomerservice_individualCustomer_view
Volume Pipeline(Extension)	volumepipelineservice_pipeline_ext_view
Sales Quote Header	salesquoteservice_salesQuote_view
Topic Analyzer Analytical Model	mlnlptopicanalyzerservice_caseTopicAnalysisResult_view
Unique User Logins	productmetricsservice_uniqueUserLogins_view
Territory	salesterritoryservice_salesTerritory_view
Competitor Product	competitorproductservice_competitorProduct_view
Account Team Members	accountservice_accountTeamMember_view
Sales Quote Item	salesquoteservice_salesQuoteItem_view
Interaction Insight Analysis	bizinteractioninsightservice_interactionInsightAnalysis_view
Templates Usage Information	templateusageservice_usageInfo_view
Product	productservice_product_view
Opportunity(Extension)	opportunityservice_opportunity_ext_view
Channel information	emailchannelservice_channelInfo_view
Account Sales Data	accountservice_accountSalesArrangement_view
Opportunity Item	opportunityservice_opportunityItem_view
Account Hierarchy	accounthierarchyservice_accountHierarchy_view
Plant	plantservice_plant_view
Subscription Contract Header	subscriptioncontractservice_subscriptionContract_view
Competitor(Extension)	competitorservice_competitor_ext_view
Case	caseservice_case_view
Aggregated API Statistics	productmetricsservice_aggregatedAPIStats_view
Service Category	servicecategoryservice_serviceCategory_view
Contact	contactpersonservice_contactPerson_view
Warranty Analytical Model	warrantyservice_warranty_view
Sales Quote Item(Extension)	salesquoteservice_salesQuoteItem_ext_view
Analytical Model of In-Person Interactions	personinteractionservice_personInteraction_view
Sales Distribution Chain	productservice_productSalesDistributionChain_view
Case Flow Log Analytical Model	caseflowservice_caseFlowLog_view
Opportunity Competitor Product	opportunityservice_opportunityCompetitorProduct_view
Survey Design Element	surveydesignservice_surveyDesignElement_view
Email interaction information	emailservice_emailInteractionInfo_view
Letter Interaction	letterinteractionservice_letterInteraction_view
Opportunity Territory	opportunityservice_opportunityTerritory_view
Survey	surveyservice_surveyResponse_view
Sales Contract Item	salescontractservice_salesContractItem_view
Opportunity Revenue Schedule	opportunityservice_opportunityRevenueSchedule_view
Functional Location	functionallocationservice_functionalLocation_view
Case(Extension)	caseservice_case_ext_view
opportunityItem_ext(Extension)	opportunityservice_opportunityItem_ext_view
Entity Count Metric	productmetricsservice_entityCountMetric_view
Unit of Measure Conversions	productservice_productUnitOfMeasureConversion_view
Registered Product	registeredproductservice_registeredProduct_view
Service Catalog	servicecatalogservice_serviceCatalog_view
Lead	leadservice_lead_view
Survey Design Matrix Column	surveydesignservice_surveyDesignMatrixColumn_view
Product Group	productgroupservice_productGroup_view
Service Order Item	serviceorderservice_serviceOrderItem_view
Opportunity Revenue Plan	opportunityservice_opportunityRevenuePlan_view
Survey Design	surveydesignservice_surveyDesign_view
accountSalesArrangement_ext(Extension)	accountservice_accountSalesArrangement_ext_view
Employee(Extension)	employeeservice_employee_ext_view
Service Level	servicelevelservice_serviceLevel_view
Subscription Contract Items	subscriptioncontractservice_subscriptionContractItem_view
Chat	chatservice_chatInteraction_view
Account	accountservice_account_view
Visit	visitservice_visit_view
Lead Territory	leadservice_leadSalesTerritory_view
lbl_interaction_insight_analysis_result	bizinteractioninsightservice_interactionInsightResult_view
Service Order	serviceorderservice_serviceOrder_view
Forecast Opportunities(Extension)	forecastservice_opportunity_ext_view
Visit(Extension)	visitservice_visit_ext_view
Account(Extension)	accountservice_account_ext_view
Time Log	timelogservice_timeLog_view
Opportunity Header	opportunityservice_opportunity_view
Opportunity Related Entity	opportunityservice_opportunityRelatedObject_view
Task	taskservice_task_view
Organizational Unit	organizationalunitservice_organizationalUnit_view
User	userservice_user_view
Case Related Object	caseservice_relatedObject_view
Sales Order Header	salesorderservice_salesOrder_view
Survey Design Matrix Row	surveydesignservice_surveyDesignMatrixRow_view
Call Lists	calllistservice_calllist_view
Installed Base	installedbaseservice_installedBase_view
Employee	employeeservice_employee_view
Topic Keyword Analyzer Analytical Model	mlnlptopicanalyzerservice_caseTopicKeywordAnalysisResult_view
Competitor	competitorservice_competitor_view
Sales Order Item	salesorderservice_salesOrderItem_view
Lead Product	leadservice_leadProduct_view
Appointments	appointmentservice_appointment_view
Lead Related Object	leadservice_leadRelatedObject_view
Employee Assignments for Organizational Unit	organizationalunitservice_organizationalUnitEmployeeAssignment_view
Phone Calls	phoneservice_phoneCall_view
Survey Design Element Response Option	surveydesignservice_surveyDesignResponseOption_view
Pipeline Flow(Extension)	pipelineflowservice_pipelineFlow_ext_view
Product(Extension)	productservice_product_ext_view
Sales Contract Header	salescontractservice_salesContract_view
Pipeline(Extension)	pipelineservice_pipeline_ext_view

SAP Datasphere News in November

2025-12-12T12:34:40.361000+01:00

SAP Datasphere News in November

Another month with two great new releases ✨

Learn more in my short top features video in November2025 on YouTube 📺 and a recap from SAP TechEd this year in the blog 📝 below, where you also find more details about the foundational learning journey – Becoming an SAP Data Architect.

In addition, explore the latest updates in our community news blogs and more.

My top features in November

Define Row-Level Authorizations Based on IdP User Attributes

This small enhancement is very powerful and my favorite feature of November. You can now use values in custom attributes provided by your identity provider as identifiers in your data access controls and define conditions that apply to users with these values. Why is that powerful? Because using these attributes allows you to apply a single condition to all users whose accounts contain the specified attribute value. In other words, you can group users with attributes to assign permissions to a group instead of individual users.

➡️ More information.

Cancel the Start or Stop Process of an Elastic Compute Node

When an elastic compute node is starting or stopping, you can now cancel the process. The data is rolled back and restored to the state that existed before the start or stop process was initially triggered.

➡️ More information.

Replication Flows: Increase Maximum Parallel Job

You can increase the maximum number of parallel jobs up to 16 to run replication flows. Before, that maximum was 10, so this increase is helpful when you need more parallelization for certain flows.

More information:
➡️ Configure the Size of Your SAP Datasphere Tenant and
➡️ Sizing, Capacity Planning, and Load Balancing for Replication Flows

New Incremental Aggregation Types for Transformation Flows in a File Space

The aggregation types MIN, MAX, COUNT*, and AVG have been added to the already existing types LAST, SUM, and COUNT.

➡️ More information.

Transformation Flows: Cancel a Running Merge Task

From the Local Tables (File) monitor, you can now cancel a merge task that is running with a new Cancel button.

➡️ More information.

Disable Delta Capture for Local Tables (File)

When creating a local table (file) from the table editor or as a target of your transformation flow, you can disable the delta capture settings.

More information:
➡️ Creating a Local Table (File) and
➡️ Creating a Transformation Flow in a File Space.

SAP TechEd 2025

I had the opportunity to be onsite in Berlin at SAP TechEd 2025 as well as in Sydney for SAP TechEd on tour. It was great to see so many people in person at both events. After many years of virtual TechEd’s this was excellent. Not only meeting many friends but also having those inspiring discussions with new people from various customers, partners, and also colleagues from SAP.

Session DA203 together with Dominik Kurz was recorded as part of the virtual SAP TechEd and can be watched here:
🔗 https://www.sap.com/events/teched/virtual/flow/sap/tev25/catalog-virtual/page/catalog/session/1754495722564001nPF0

Happy to share that my deep dive session “The impact of SAP Business Data Cloud on SAP Datasphere users” made it to the top 5 most attended sessions overall in Berlin. In general, all sessions around SAP Business Data Cloud drew high participation and our colleagues at the SAP BDC booth were also busy until the last minute on Thursday afternoon.

A highlight was certainly the partnership announcement with Snowflake. The announcement covers two flavors brownfield and greenfield scenarios.
(1) Brownfield: enabling Snowflake systems to be connected via BDC connect, targeted for GA in H1 2026
(2) Greenfield: offering SAP Snowflake on the SAP pricelist as a solution extension, targeted for Q1 2026

A week after TechEd we also announced a partnership with Microsoft at Microsoft Ignite, enabling Microsoft Fabric to be connected via BDC connect, targeted for GA in H3 2026.

That covers now Databricks, Snowflake, Google and Microsoft as technology partners for SAP Business Data Cloud Connect. Not much missing here from my point of view.

Becoming an SAP Data Architect - Foundational Learning Journey

This is a great learning opportunity for all you data architects out there‼️
Kudos to the learning team together with our architectural experts!

The foundational Learning Journey of becoming an SAP Data Architect is the first step in a comprehensive program designed to empower data professionals and architects of the future. This new learning journey provides a structured path to develop the knowledge, frameworks, and strategic mindset needed to design, govern, and communicate enterprise-scale data architectures.

🧭 What Is the Data Architecture Learning Journey?

The learning journey is built to create strong Data Architecture foundations moving from foundational principles to strategic business impact. Learners will gain both architectural knowledge and understanding that is essential for any modern data architect and covers the following four units:

🔹Foundational Concepts Learn the core principles and frameworks of data architecture from conceptual, logical, and physical data modelling to metadata management and frameworks.

🔹Modern Paradigms & Integration Explore today’s architectural models like Data Mesh, Data Fabric, and Lakehouse and design integration strategies (ETL, ELT, and streaming) for scalable, real-time analytics.

🔹Governance, Security & Strategic Impact Build governance frameworks, establish data lineage, safeguard privacy, and align data strategy with business objectives for measurable value.

🔹Communicating Data Value Strengthen communication and storytelling skills to articulate architectural value, design transformation roadmaps, and drive organizational adoption of data-driven change.

❓ Why This Matters

In a world where data is at the core of every business decision, the role of the Data Architect has become critical.

This learning journey helps professionals at every level develop the skills, language, and strategic vision to lead in data architecture and governance.

👉 By completing this journey, learners will be able understand how to:

Design efficient, compliant, and scalable data systems.
Integrate modern architectural paradigms.
Translate architecture into business value.
Build narratives and roadmaps that drive transformation.

➡️ How to Get Started

Visit the learning.sap.com and search for “Data Architect” OR use the Link
🔗 https://learning.sap.com/courses/becoming-an-sap-data-architect
Enroll to start your journey, Progress through the learning units at your own pace.
Earn a Digital Badge on completion and share it on your socials!

And we have a lots more in store for 2026!

🔗 More information.

More blogs about SAP BDC and Datasphere to check out 👇

Find more information and related blog posts on the topic page for SAP Datasphere. You will find further product information on our Community with various subpages about Business Content, the SAP BW Bridge as well as content for Best Practices & Troubleshooting. Also check out the new support content for SAP Datasphere on SAP Help for troubleshooting and analysis guides, how-to guides, technical details, and more.

Find out how to unleash the power of your business data with SAP’s free learning content on SAP Datasphere. It’s designed to help you enrich your data projects, simplify the data landscape, and make the most out of your investment. Check out even more role-based learning resources and opportunities to get certified in one place on SAP Learning site.

SAP BTP: Data Fabric

2025-12-12T12:50:49.920000+01:00

Irrespective of where your source system resides, SAP provides the capabilities to ingest, harmonize and unlock real-time and predictive insights on your target system with confidence. This is supported by resilient, enterprise-grade frameworks, tools, and methodologies that safeguard data quality, integrity, and consistency throughout the entire data lifecycle.

SAP. (2023). Figure:1

Source Systems for Data Provisioning in SAP

URL: https://learning.sap.com/courses/implementing-data-provisioning-in-sap-bw-4hana/identifying-sap-bw-4hana-source-systems-and-their-use-cases

The endless possibilities of raw, intrinsic data ingestion from e.g. Enterprise warehouses, data lakes, rational & in-memory databases, SaaS applications even social media into SAP HANA (On-premises and/ or Cloud) opens a world of opportunity for data engineers to acquire rich, accurate, and complete master and transactional data from all possible sources imaginable. Responsibility for managing the data acquisition processes depends on the provisioning type being used. Common approaches include:

Provisioning Type	Data Flow Responsibility	Metadata Management	Typical Use Cases
Application-Controlled	Source application pushes data to target using APIs or connectors.	Managed in application layer (schema, mappings, transformations).	Custom logic, application-driven workflows, SaaS integrations.
Change Data Capture (CDC)	CDC engine streams changes (insert/update/delete) from source to target in near real-time.	Managed by CDC tool (change logs, timestamps, transaction markers).	Real-time replication, incremental updates, event-driven architectures.
Database-Controlled	Target database pulls data from source using adapters or SDI.	Centralized in database layer (schemas, lineage, governance).	Governance-heavy environments, centralized data lakes, enterprise ETL.

Figure:2

Provisioning Types: Data Flow & Metadata Management

The utilization of high-performance ETL/ELT tools ensure the consistence, quality and reliability of the data is upheld and thus often used to unify the data to best meet the objectives of the stakeholder’s request either being executives, decision makers, marketing or product. A well-thought-out data provisioning strategy should always take precedence as this ensure a smooth flowing data process. Depending on the usage of the data, a data engineer may explorer the below different data provisioning approaches:

Data federation – Data is not loaded nor persisted into the target system (SAP HANA) but rather read directly from the source system only when the data is needed for use.
Data replication – Data from the source system is loaded, persisted into the target system and kept in sync using various mechanisms
- Real-time (Synchronous) replication
- Delta load (asynchronous)replication
Data transformation – The raw data is enhanced with additional features e.g. calculation columns, renamed fields or new fields derived from other exiting fields

SAP provides but not limited to the below high-performance native data integration technologies:

Tool	Primary Function	Position in Landscape Architecture
SLT (SAP Landscape Transformation Replication)	Real-time replication (CDC)	Integration layer between SAP sources and HANA DB
SDA (Smart Data Access)	Virtual access to remote sources (federation)	Integration layer enabling query pushdown to the HANA DB
SDI (Smart Data Integration)	ETL/ELT pipelines, adapters, dataflows	Integration layer moving and transforming data before loading into the HANA DB

Figure 3:

SAP Native ETL/ELT tools

Furthermore, for Data quality, cleansing and enrichment before persisting into the HANA DB, SAP provides the below:

Smart Data quality (SDQ) for SAP HANA on-premises
Data Quality Management Microservices (DQMm) as a subscription-based microservice for SAP HANA Cloud

SAP Hana provides a variation of consolidated tools both on premise and in the cloud, all providing for various functionality for different use cases such as:

Supporting data provisioning
Developing Database artefacts
Monitoring the performance of the develop artefacts
Monitoring the Database and its system performance
Administering and managing the Database

One of the key strengths of the SAP ecosystem is the ability to seamlessly combine cloud functionalities together with on-premises and still import specific/ needed applications as micro services into common IDE’s such as Microsoft Visual Studio code is commendable because it:

Provides business with a comprehensive 360-degree view of
- Database health overview.
- Data life cycle from end to end.
- Application life cycle from end to end.

Empowers the end users, data engineers, developers, data scientists, data analyst and support engineers with greater power and flexibility over the data being used.

Below are native tools used by SAP for various purposes:

SAP Business Application studio – Cloud development tool.
Web IDE – On-premises development tool
SAP Hana cockpit - a web-based tool for monitoring, administering, and managing SAP HANA databases, offering a centralized, browser-accessible interface with tile-based dashboards for performance, resource usage (CPU, memory, disk), alerts, and security. It allows tasks like starting/stopping services, managing users, viewing alerts, analyzing performance, and running SQL queries through its integrated Database Explorer.

SAP SaaS applications are imported into common IDE, s such as Microsoft Visual Studio code via extensions, libraries etc. for a specific purpose e.g.:

Supportability tools for SAP HANA - For both online and offline performance analysis and troubleshooting. With a focus on the needs of support engineers, the performance analysis tools provide enhanced analysis options for trace files and runtime dump files, as well as allowing in-depth analysis on extensive datasets.
The SQL analyzer tool for SAP HANA - A performance analysis tool that helps developers visualize and understand SQL execution plans. Designed for query tuning and optimization, it offers detailed insights into how queries run within the SAP HANA database.
SAP HANA Database Explorer - Provides functionality for accessing SAP HANA databases, browsing the database catalog, and executing SQL from a SQL console.

Figure 4:

SAP Development and Administration Tools Overview

The consolidated toolset further enhances productivity and convenience for end users, data engineers, developers, data scientists, data analyst and support engineers by enabling them to access all essential capabilities from a single unified platform. The choice and flexibility to work with the desired programming languages ranging from but not limited to: SQL, SQL Script, Python, Node.js, JavaScript and react native etc. Enable a cross-functional team with diverse skill set to collaborate effectively while striving towards the same common goal.

Figure 5:

Core Programming Languages and Frameworks for SAP HANA Cloud and On-Premises

After successfully completing the Explore phase, with a solid fit -to-standard analysis of proposed use cases, Extrinsic data pipelines are then implemented for reliable and consistent data flow. These pipelines support a wide range of business needs such as decision making, go-to-market activities or ML/AI use cases requested by various stakeholders. The process is typically executed and maintained by data engineers, developers, data scientists, data analyst and/or support engineers.

The harmonized data is then governed, analyzed, and visualized through multiple front-end applications. These applications are powered by various integration frameworks e.g. REST APIs, OData v4, JDBC/ODBC, each serving different user needs and functional objectives:

Figure: 6

SAP-Compatible Applications and Their Integration Frameworks

Mutatis mutandis, Data is the inception of all there is, as it has been in the beginning, is now and ever shall be. Thank you all for taking a glimpse into the above Content. Please don’t for get to like, comment and share.

Abbreviations:

API: Application Programming Interface

BTP: Business Technology Platform

CDC: Change Data Capture

DB: Database

DQMm: Data Quality Management Microservices

ELT: Extract, Load, Transform

ETL: Extract, Transform, Load

IDE: Integrated Development Environment

ML/AI: Machine Learning/Artificial Intelligence

ODBC: Open Database Connectivity

OData: Open Data Protocol

REST: Representational State Transfer

SaaS: Software as a Service

SDI: Smart Data Integration

SDA: Smart Data Access

SDQ: Smart Data Quality

SLT: SAP Landscape Transformation Replication

SQL: Structured Query Language

Reference:

SAP. (2023). Figure:1 Source Systems for Data Provisioning in SAP, URL: https://learning.sap.com/courses/implementing-data-provisioning-in-sap-bw-4hana/identifying-sap-bw-4hana-source-systems-and-their-use-cases

Data masking, data scrambling and data anonymization in Business Data Cloud with SAP Datasphere

2025-12-18T09:57:28.944000+01:00

Introduction

This blog post was inspired from: How Data Anonymization in SAP HANA secure Generative AI applications through SAP Datasphere

Here you will find a description of generalities about:

masking, scrambling and anonymization features
how to automate the creation of views with data masking, data scrambling and data anonymization and the possibility to activate/deactivate some features in the target views.

SAP Datasphere contains a lot of features. Some are accessible directly via its user interface and others are accessible via Open SQL Schema (see SAP Datasphere help site). Open SQL Schema gives you access to the underlying SAP HANA Cloud database, and extends the capacities offered via Datasphere's UI.

In this blog, we use a Datasphere space called DEMO with the database user DEMO#USER_ANONYMIZE.

We will use a Datasphere local table SALARY as an example dataset where we would like to:

mask the CREDIT_CARD column and rename the column to CREDIT_CARD_MASKED,
scramble the NAME column and rename the column to NAME_SCRAMBLED,
anonymize the SALARY column and rename the column to SALARY_ANONYMIZED

Note: SALARY column data type is Double.

Save the content below to import a sample dataset into the SALARY local table.

"ID","NAME","GENDER","CITY","SALARY","CREDIT_CARD"
"1","Paul McCartney","M","Levallois","1002.32","1111-1111-1111-1111"
"2","Martin Solveig","M","Munich","3218.21","2222-2222-2222-2222"
"3","Nils Lofgren","M","Nice","1398.32","3333-3333-3333-3333"
"4","Annika","F","Munich","2053.23","4444-4444-4444-4444"

1 - Generalities

1.1 - Data masking

Data masking is a feature implemented into SAP HANA Cloud (see Data Masking).
In this blog, we will not use privileges to manage masking, but simply use calculated SQL expressions when defining views where some columns should be masked.

To mask the content of the NAME column, we can just use the following SQL Statement into an SQL view object in Datasphere :

SELECT "ID",
   "NAME",
   CITY,
   GENDER,
   SALARY,
   LEFT(CREDIT_CARD, 4) || '-XXXX-XXXX-' || RIGHT(CREDIT_CARD, 4) AS CREDIT_CARD_MASKED
FROM SALARY

The content displayed by the view is:

1.2 - Data scrambling

To scramble characters in a string, a function needs to be created in the Open SQL Schema DEMO#USER_ANONYMIZE (the one automatically created by Datasphere when creating the database user). To connect to this schema, SAP HANA Database Explorer can be started from the space management window:

1.2.1 - Create function

Once in data Database explorer, the function SCRAMBLE_STRING can be created:

Copy/Paste the following code:

CREATE FUNCTION SCRAMBLE_STRING (IN input NVARCHAR(500))
RETURNS scrambled NVARCHAR(500)
LANGUAGE SQLSCRIPT
AS
BEGIN
    DECLARE len INT = LENGTH(:input);
    DECLARE temp NVARCHAR(500) = :input;
    DECLARE result NVARCHAR(500) = '';
    DECLARE pos INT;
 
    WHILE LENGTH(:temp) > 0 DO
        pos := CEIL(RAND() * LENGTH(:temp));  -- Pick random position
        result := result || SUBSTRING(:temp, :pos, 1); -- Append character
        temp := REPLACE(:temp, SUBSTRING(:temp, :pos, 1), ''); -- Remove picked char
    END WHILE;
 
    scrambled := result;
  END

1.2.2 - Expose table data

Since Datasphere local tables cannot be directly exposed, in order to be able to read the content of the SALARY table when using an Open SQL Schema, a view V_SALARY needs to be created in Datasphere using the Data Builder:

SELECT "ID",
	"NAME",
	"GENDER",
	"CITY",
	"SALARY",
	"CREDIT_CARD"
FROM "SALARY"

1.2.3 - Create a view

A view V_SALARY_SCRAMBLED can now be created using Database Explorer to scramble the column NAME from the V_SALARY view (the call to INITCAP function was just added to format names):

CREATE VIEW "V_SALARY_NAME_SCRAMBLED" AS
(SELECT 
	"ID" AS "ID",
	INITCAP(SCRAMBLE_STRING("NAME")) AS NAME_SCRAMBLED,
	GENDER AS GENDER,
	CITY AS CITY,
	SALARY,
	CREDIT_CARD
FROM DEMO.V_SALARY);

1.2.4 - Import the view into Datasphere

You can now import the view to create a Datasphere local table: start the process to create a dummy Datasphere view, select the Sources tab in the source browser, then click on the schema name and Drag and Drop of the View name to the text area. This triggers the table import, click on Import an Deploy, then close the New SQL View object (no need to save it, click on Discard):

Now, the view "DEMO#USER_ANONYMIZE".V_SALARY_SCRAMBLED can be opened and used in Datasphere:

1.3 - Data anonymization

The SAP HANA Data Anonymization Guide explains how to anonymize data. In this blog, we will not focus on the technique used to anonymize data, but rather on sample syntax used to anonymize data. The following SQL code creates a view with the anonymized SALARY column (the view with anonymization will be effective after the REFRESH command):

CREATE VIEW "V_SALARY_SALARY_ANONYMIZED" AS
(SELECT 
	"ID",
	"NAME",
	GENDER,
	CITY,
	SALARY AS SALARY_ANONYMIZED,
	CREDIT_CARD
FROM DEMO.V_SALARY)
WITH ANONYMIZATION(
	ALGORITHM 'DIFFERENTIAL_PRIVACY'
	PARAMETERS '{"data_change_strategy": "qualified"}'
	COLUMN "ID" PARAMETERS '{"is_sequence": true}'
	COLUMN "SALARY_ANONYMIZED" PARAMETERS '{"is_sensitive":true, "epsilon":0.1, "sensitivity":2000}'
);
REFRESH VIEW "V_SALARY_SALARY_ANONYMIZED" ANONYMIZATION;

The V_SALARY_SALARY_ANONYMIZED view can now be imported into Datasphere (see 1.2.4 - Import the view into Datasphere)

2 - Standardization

We've seen above that Open SQL Schema is powerful to create features not implemented in the Datasphere UI but accessible using SQL.

The standardization will allow the SAP Datasphere users to easily activate/deactivate masking, scrambling and anonymization for columns.
A Datasphere local table (VIEWS_CREATOR_PARAMETERS) will contain the views definitions and a custom SQL procedure (EXEC_VIEWS_CREATOR) will read its content to create the views based on the stored definitions. Then a Task Chain will be created to call the procedure from Datasphere.

2.1 - Parameters table

The table "VIEWS_CREATOR_PARAMETERS" will have the following columns:

"TARGET_VIEW": name of the view that will be created in the Open SQL Schema
"CONFIGURATION": characteristic of the target view. Contains the field name of the unmasked, scrambled anonymized field
"SEQ": optional sequence number used to order the list of the view fields
"VALUE": value related to the "CONFIGURATION". When "CONFIGURATION" is a field name, it contains an expression (or NULL when the field doesn't need to be changed in the target view)
"CREATE": 'YES' when the target field needs to be created in the target table
"RENAME_TO": target field name when "CONFIGURATION" field name needs to be changed
"ACTIVATE": 'YES' when the target field "VALUE" needs to be used (when it doesn't start with the keyword 'PARAMETERS'). Otherwise the field name stored in "CONFIGURATION" is used.

CONFIGURATION contains one of the following keywords:

'#EXECUTE#': define the behavior of the procedure that will create the target view. When "VALUE" contains the value:
- 'YES': it creates in the "TARGET_VIEW" view the fields with the 'CREATE' value set to 'YES'
- 'ALL': it ignores the value of the "ACTIVATE" field ; all the fields with the 'CREATE' value set to 'YES' will be created with the expression stored in "CONFIGURATION"
- 'RESET': if ignore the value of the "ACTIVATE" field ; all the fields with the 'CREATE' value set to 'YES' will be created with their original definition stored in "VALUE"
'#SOURCE_NAME#': when used, then "VALUE" contains the name of the table/view used to create the target view
'#ANONYMIZATION_TYPE#': when used, then VALUE contains the expression used for anonymization (see SAP HANA Data Anonymization Guide)

TARGET_VIEW	CONFIGURATION	SEQ	VALUE	CREATE	RENAME_TO	ACTIVATE
V_SALARY_DERIVED	#EXECUTE#		ALL
V_SALARY_DERIVED	#ANONYMIZATION_TYPE#		WITH ANONYMIZATION (ALGORITHM 'DIFFERENTIAL_PRIVACY' PARAMETERS '{"data_change_strategy": "qualified"}'
V_SALARY_DERIVED	#SOURCE_NAME#		V_SALARY
V_SALARY_DERIVED	ID	1	PARAMETERS '{"is_sequence": true}'	YES		YES
V_SALARY_DERIVED	NAME	2	INITCAP(SCRAMBLE_STRING(NAME))	YES	NAME_SCRAMBLED	YES
V_SALARY_DERIVED	GENDER	3	MAP(GENDER,'M','Mr','F','Ms','')	YES
V_SALARY_DERIVED	CITY	4	CITY			YES
V_SALARY_DERIVED	SALARY	5	PARAMETERS '{"is_sensitive":true, "epsilon":0.1, "sensitivity":2000}'	YES	SALARY_ANONYMIZED	YES
V_SALARY_DERIVED	CREDIT_CARD	6	LEFT(CREDIT_CARD,4) \|\| '-XXXX-XXXX-' \|\| RIGHT(CREDIT_CARD,4)	YES	CREDIT_CARD_MASKED	YES

In the previous screenshot, the view V_SALARY_DERIVED will have the following fields and properties:

"ID" will remain unchanged
"NAME" will be scrambled and renamed to "NAME_SCRAMBLED"
"GENDER" will appear not changed in the target view
"CITY" will not appear in the target view
"SALARY" will be anonymized and renamed to "SALARY_ANONYMIZED"
"CREDIT_CARD" will be masked and renamed to "CREDIT_CARD_MASKED"

In order to execute the expressions to create the target columns, a line with '#EXECUTE#' was created.
Since the view contains anonymized data, a line with '#ANONYZATION_TYPE#' was s created.

In order to be able to use this table content into an Open SQL Schema, an SQL view in SAP Datasphere needs to be created : V_VIEWS_CREATOR_PARAMETERS. This view will contain all the fields from the table.

2.2 - SQL Procedures

Three SQL procedures are created:

EXEC_VIEWS_CREATOR_CREATE_STATMT: generates a create view statement
EXEC_VIEWS_CREATOR: with a string parameter used to filter the view V_VIEWS_CREATOR_PARAMETERS to create only one target view by calling the procedure EXEC_VIEWS_CREATOR_CREATE_STATMT and embedding the code with instructions to drop the view if it already exists and generates the REFRESH statement if anonymization is used.
EXEC_ALL_VIEWS_CREATOR: calls EXEC_VIEWS_CREATOR for the distinct values of TARGET_VEWS.

2.2.1 - Procedure EXEC_VIEWS_CREATOR_CREATE_STATMT

This procedure returns the CREATE VIEW statement for one target view defined in the view "VIEWS_CREATOR_PARAMETERS" .

In the code below:

replace DEMO#USER_ANONYMIZE with the name of your Open SQL Schema
replace DEMO with your Datasphere space name.

CREATE PROCEDURE EXEC_VIEWS_CREATOR_CREATE_STATMT (
	IN TARGET_NAME_ VARCHAR(1000),
	IN CURRENT_SPACE_ VARCHAR(1000),
	IN CURRENT_SCHEMA_ VARCHAR(1000),
	IN EXECUTE_ VARCHAR(1000),
	OUT FLAG_ANONYMIZATION_ INTEGER,
	OUT SQL_DDL VARCHAR (2500)
)
AS
BEGIN
    DECLARE PART1 VARCHAR(2500);
    DECLARE PART2 VARCHAR(2500);
    DECLARE PART3 VARCHAR(2500);
    DECLARE PART4 VARCHAR(2500);
    DECLARE PART5 VARCHAR(2500);

	/*
	CREATE VIEW PART 1: begining part to create the target view
	*/
    SELECT TOP 1 'CREATE VIEW '||:CURRENT_SCHEMA_||'.'|| :TARGET_NAME_ AS x
    into PART1 
    FROM DEMO.V_VIEWS_CREATOR_PARAMETERS 
    WHERE "TARGET_VIEW"=:TARGET_NAME_;

	/*
	CREATE VIEW PART 2: list columns for the target view
	*/
	WITH VIEW_PARAMETERS as
		(SELECT
			"TARGET_VIEW" AS "TARGET_VIEW",
			"CONFIGURATION" AS "CONFIGURATION",
			"SEQ" AS "SEQ",
			CASE :EXECUTE_ 
				WHEN 'YES' 
					THEN CASE WHEN IFNULL("ACTIVATE", 'NO')<>'YES'
						THEN "CONFIGURATION"
						ELSE "VALUE"
					END
				WHEN 'ALL' THEN "VALUE"
				WHEN 'RESET' THEN "CONFIGURATION" 
			END AS "VALUE",
			"CREATE" AS "CREATE",
			"RENAME_TO" AS "RENAME_TO"
		FROM DEMO.V_VIEWS_CREATOR_PARAMETERS
		WHERE "CONFIGURATION" NOT IN ('#ANONYMIZATION_TYPE#', '#EXECUTE#', '#SOURCE_NAME#')
			AND "TARGET_VIEW"=:TARGET_NAME_
		)
    SELECT ' ( ' ||
	    string_agg( IFNULL(RENAME_TO,"CONFIGURATION" )
	    	    , ', ' ORDER BY "SEQ" ) ||
	    ' ) as select ' ||
	    string_agg( 
	        IFNULL(
	            ( CASE WHEN "VALUE" LIKE 'PARAMETERS%' 
					THEN NULL 
					ELSE "VALUE" 
	            END ) 
	            , "CONFIGURATION"
	        ) ||
			' as ' ||
			IFNULL( RENAME_TO, "CONFIGURATION" )
			, ', ' ORDER BY "SEQ"
		) 
    AS x 
    INTO PART2
    FROM VIEW_PARAMETERS
    WHERE "CREATE" = 'YES';
    
	/*
	CREATE VIEW PART 3: select expression for the target view
	*/
	SELECT TOP 1 ' from '|| :CURRENT_SPACE_ || '.' || "VALUE" 
	as x 
	into PART3 
	FROM DEMO.V_VIEWS_CREATOR_PARAMETERS 
	WHERE "CONFIGURATION" = '#SOURCE_NAME#' AND "TARGET_VIEW"=:TARGET_NAME_;

	WITH VIEW_PARAMETERS as
		(SELECT
			"TARGET_VIEW" AS "TARGET_VIEW",
			CASE :EXECUTE_ 
				WHEN 'YES' 
					THEN CASE WHEN IFNULL("ACTIVATE", 'NO')<>'YES'
						THEN "CONFIGURATION"
						ELSE "VALUE"
					END
				WHEN 'ALL' THEN "VALUE"
				WHEN 'RESET' THEN "CONFIGURATION" 
			END AS "VALUE"
		FROM DEMO.V_VIEWS_CREATOR_PARAMETERS
		WHERE "CONFIGURATION" NOT IN ('#ANONYMIZATION_TYPE#', '#EXECUTE#', '#SOURCE_NAME#')
			AND "TARGET_VIEW"=:TARGET_NAME_ AND "CREATE"='YES'
			AND EXISTS (SELECT 1 
						FROM DEMO.V_VIEWS_CREATOR_PARAMETERS 
						WHERE UPPER("CONFIGURATION") = '#ANONYMIZATION_TYPE#' AND "TARGET_VIEW"=:TARGET_NAME_ AND :EXECUTE_ in ('YES','ALL')
						)
		)
	SELECT count(*) as x
	INTO FLAG_ANONYMIZATION_
	FROM VIEW_PARAMETERS
	WHERE "VALUE" LIKE 'PARAMETERS%' AND "TARGET_VIEW"=:TARGET_NAME_;
	
	IF :FLAG_ANONYMIZATION_ <> 0 THEN
		/*
		CREATE VIEW PART 4: add predicate for anonymization
		*/
	    SELECT "VALUE" AS x 
	    INTO PART4 
	    FROM DEMO.V_VIEWS_CREATOR_PARAMETERS 
	    WHERE "CONFIGURATION" = '#ANONYMIZATION_TYPE#' AND "TARGET_VIEW"=:TARGET_NAME_;
	
		/*
		CREATE VIEW PART 5: list parameters for anonymization
		*/
		WITH VIEW_PARAMETERS as
		(SELECT
			"TARGET_VIEW" AS "TARGET_VIEW",
			"CONFIGURATION" AS "CONFIGURATION",
			"SEQ" AS "SEQ",
			CASE :EXECUTE_ 
				WHEN 'YES' 
					THEN CASE WHEN IFNULL("ACTIVATE", 'NO')<>'YES'
						THEN "CONFIGURATION"
						ELSE "VALUE"
					END
				WHEN 'ALL' THEN "VALUE"
				WHEN 'RESET' THEN "CONFIGURATION" 
			END AS "VALUE",
			"RENAME_TO" as "RENAME_TO"
		FROM DEMO.V_VIEWS_CREATOR_PARAMETERS
		WHERE "CONFIGURATION" NOT IN ('#ANONYMIZATION_TYPE#', '#EXECUTE#', '#SOURCE_NAME#')
			AND "TARGET_VIEW"=:TARGET_NAME_ AND "CREATE"='YES'
			AND EXISTS (SELECT 1 
						FROM DEMO.V_VIEWS_CREATOR_PARAMETERS 
						WHERE UPPER("CONFIGURATION") = '#ANONYMIZATION_TYPE#' AND "TARGET_VIEW"=:TARGET_NAME_ AND :EXECUTE_ in ('YES','ALL')
						)
		)
		SELECT string_agg('Column ' || IFNULL(RENAME_TO,"CONFIGURATION") || ' ' || "VALUE", ' ' ORDER BY "SEQ") ||' )' AS x
	    INTO PART5
	    FROM VIEW_PARAMETERS
	    WHERE UPPER(LEFT("VALUE",10))='PARAMETERS' AND "TARGET_VIEW"=:TARGET_NAME_;
		
	END IF;	   
	
    select string_agg(x, ' ' order by rn) as x 
    into SQL_DDL from (
	    SELECT :PART1 as x ,1 as rn from DUMMY UNION ALL
	    SELECT :PART2 as x ,2 as rn from DUMMY UNION ALL
	    SELECT :PART3 as x ,3 as rn from DUMMY UNION ALL
	    SELECT :PART4 as x ,4 as rn from DUMMY UNION ALL    
	    SELECT :PART5 as x ,5 as rn from DUMMY
    ) a ;
END

2.2.2 - Procedure EXEC_VIEWS_CREATOR

This procedure creates the SQL instructions to create one target view.

It contains:

a DROP command that will be executed if the TARGET_VIEW is already existing
a CREATE VIEW command to create the TARGET_VIEW
A REFRESH command if TARGET_VIEW contains any field that needs to be anonymized

In the code below:

replace DEMO#USER_ANONYMIZE with the name of your Open SQL Schema
replace DEMO with your Datasphere space name.

CREATE PROCEDURE EXEC_VIEWS_CREATOR (IN TARGET_NAME_ VARCHAR (1000)) 
AS
BEGIN
    DECLARE SQL_DROP VARCHAR(2500);
    DECLARE SQL_DDL VARCHAR(2500);
    DECLARE EXECUTE_ VARCHAR(2500);
    DECLARE REFRESH_ VARCHAR(2500);
    DECLARE FLAG_DROP_ INTEGER;
    DECLARE FLAG_ANONYMIZATION_ INTEGER;
    DECLARE CURRENT_SCHEMA_ VARCHAR(2500);
    DECLARE CURRENT_SPACE_ VARCHAR(2500);

SELECT UPPER("VALUE") as x into EXECUTE_ FROM DEMO.V_VIEWS_CREATOR_PARAMETERS WHERE UPPER("CONFIGURATION") = '#EXECUTE#' AND "TARGET_VIEW"=:TARGET_NAME_;

IF :EXECUTE_ IN ('YES', 'RESET', 'ALL') THEN
	SELECT 'DEMO#USER_ANONYMIZE' into CURRENT_SCHEMA_ FROM DUMMY;
	SELECT SUBSTR_BEFORE(:CURRENT_SCHEMA_, '#') into CURRENT_SPACE_ from DUMMY;
    SELECT count (*) INTO FLAG_DROP_ FROM views WHERE schema_name=:CURRENT_SCHEMA_ and view_name=:TARGET_NAME_;
   
    /*
    DROP SQL expression to premare the replacement for the target view.
    */
    SELECT TOP 1 'DROP VIEW '||:CURRENT_SCHEMA_||'.'||:TARGET_NAME_ ||' ; ' as x 
    into SQL_DROP
    from DEMO.V_VIEWS_CREATOR_PARAMETERS WHERE "TARGET_VIEW"=:TARGET_NAME_;

    /*
    CREATE SQL expression to create the target view.
    */
	CALL "DEMO#USER_ANONYMIZE"."EXEC_VIEWS_CREATOR_CREATE_STATMT"(
		TARGET_NAME_ => :TARGET_NAME_ /*<NVARCHAR(1000)>*/,
		CURRENT_SPACE_ => :CURRENT_SPACE_ /*<NVARCHAR(1000)>*/,
		CURRENT_SCHEMA_ => :CURRENT_SCHEMA_ /*<NVARCHAR(1000)>*/,
		EXECUTE_ => :EXECUTE_/*<NVARCHAR(1000)>*/,
		FLAG_ANONYMIZATION_ => FLAG_ANONYMIZATION_,
		SQL_DDL => SQL_DDL
	);
	
	IF :FLAG_ANONYMIZATION_ <> 0 THEN    
	    /*
	    REFRESH SQL expression for anonymization feature.
	    */
	    SELECT 'REFRESH VIEW '|| :CURRENT_SCHEMA_ ||'.'|| :TARGET_NAME_ || ' ANONYMIZATION;'
	    INTO REFRESH_ 
	    FROM DUMMY ;
	END IF;

    IF FLAG_DROP_ <> 0 THEN 
        EXECUTE IMMEDIATE SQL_DROP;
    END IF;

    EXECUTE IMMEDIATE SQL_DDL;
    IF :FLAG_ANONYMIZATION_ <> 0 THEN
    	EXECUTE IMMEDIATE REFRESH_;
    END IF;

END IF;
END

2.2.3 - Procedure EXEC_ALL_VIEWS_CREATOR

This procedure calls the procedure EXEC_VIEWS_CREATOR for all the distinct values for TARGET_VIEW.

In the code below:

replace DEMO#USER_ANONYMIZE with the name of your Open SQL Schema
replace DEMO with your Datasphere space name.

CREATE PROCEDURE EXEC_ALL_VIEWS_CREATOR()
LANGUAGE SQLSCRIPT
AS
BEGIN
	DECLARE CURSOR c_cursor FOR SELECT DISTINCT TARGET_VIEW FROM DEMO.V_VIEWS_CREATOR_PARAMETERS;
	FOR rec as c_cursor
	DO
	    CALL "DEMO#USER_ANONYMIZE"."EXEC_VIEWS_CREATOR" (:rec.TARGET_VIEW);
  END FOR;
END

2.3 - Task Chain

In order to be autonomous to mask, scramble and anonymize data in Datasphere (without accessing to the Open SQL Schema) a task chain can be created to call the SQL procedure EXEC_ALL_VIEWS_CREATOR.

2.3.1 - Grant permissions

Datasphere space needs to view and run the SQL Procedure that was created in the Open SQL Schema, some grants need to be done in SQL Developer by the user DEMO#USER_ANONYMIZE to the Datasphere space user.

In the code below:

replace DEMO#USER_ANONYMIZE with the name of your Open SQL Schema
replace DEMO with your Datasphere space name.

CALL "DWC_GLOBAL"."GRANT_PRIVILEGE_TO_SPACE" (
	OPERATION => 'GRANT', 
	PRIVILEGE => 'EXECUTE', 
	SCHEMA_NAME => 'DEMO#USER_ANONYMIZE', 
	OBJECT_NAME => '', 
	SPACE_ID => 'DEMO');

2.3.2 - Create a task chain

2.3.3 - Test

For testing purposes, we can use the parameters table described in 2.1 - Parameters table .

After running the task chain, the view V_SALARY_DERIVED was created and can now be imported into the Datasphere space (see 1.2.4 - Import the view into Datasphere ).

Finally your space should contain this list of objects:

If you open V_SALARY_DERIVED:

Conclusion

If you face some missing features in Datasphere, consider Open SQL Schema, it may offer you the opportunity to implement what you miss using the underlying SAP HANA Cloud database.

If you liked this blog, say it by clicking on the Like button.

Rewiring of SAP Datasphere to SAP Business Data Cloud

2025-12-19T09:58:19.950000+01:00

The rewiring of existing SAP Datasphere tenants to SAP Business Data Cloud has become available in 2025. Since then, I have been in many discussions with customers, partners, but also SAP colleagues at SAP TechEd in Berlin, Sydney and other events or meetings.

I observed that people unintentionally misinterpret the term “rewiring”. It sounds technical, hence there are associations with data, object or even full tenant migrations. This is not the case!

Let me use this blog to clarify what rewiring is, and what it is not. Moreover, give you some guidance on the steps which you need to take.

When do I need to rewire my existing SAP Datasphere tenant?

You have made a decision to sign up for SAP Business Data Cloud (BDC) and to make use of your existing tenants for SAP Datasphere and / or SAP Analytics Cloud as part of your new landscape with SAP BDC.

If you have not made any decision on SAP BDC yet, you can certainly use your existing tenant(s) until the end of your current contract without SAP BDC. However, you will miss out on new capabilities offered by SAP BDC with SAP managed data products and the enhanced open partner ecosystem options.

What is tenant rewiring?

The term “rewiring” sounds quite technical. Therefore, there are immediate associations with data, object or even full tenant migrations. This is not the case!

The term rewiring mainly refers to a change of your commercial model from a stand-alone tenant to a tenant which is managed as part of a larger SAP BDC landscape.
Rewiring helps you change the management of your tenant lifecycle to the SAP for Me portal.
Prerequisites are that you commercially move the tenant on your same customer ID and your current data center being available also for Business Data Cloud. Please check on these two points!
If these conditions are met a lot will stay the same. Meaning you stay on your existing hyperscaler and data center. Hence, your tenant and its URL will remain intact, just under a new commercial model, even without any downtime required.

What is tenant rewiring not?

Rewiring is therefore not a tenant migration as your existing tenant stays the same and is reused as part of an SAP BDC landscape.

Rewiring does not migrate your tenant between hyperscalers or data centers, as with the conditions mentioned before, your current data center – given that it is available for SAP BDC – will stay the same on your existing customer ID.

Rewiring does not migrate your data or any objects, as the full existing tenant is only commercially moved as described before.

Rewiring does not require any tenant downtime.

How can I initiate a tenant rewiring?

Before you can start to initiate the rewiring, you need to change the commercial model of your SAP Datasphere tenant to a Business Data Cloud contract – in short: a subscription to SAP Business Data Cloud must exist.

For tenants provisioned via BTP cockpit

After the commercial change, your SAP BTP administrator can initiate the tenant rewiring directly through the SAP BTP platform global account as a self-service. You control the timing of rewiring yourself, as you can trigger it via self-service.

Once the request is received, the process is initiated to update the tenant and move it to SAP Business Data Cloud. The tenant will be visible in SAP Business Data Cloud when the rewiring is completed.

For subscription tenants provisioned by SAP

After the commercial change, your subscription is moved from standalone to SAP Business Data Cloud. Be aware, that the SAP Business Data Cloud cockpit feature must be activated in your appropriate data center through SAP for Me portal prior to rewiring.

The rewiring itself is initiated as part of the subscription change request. That means, your tenant is automatically moved to SAP Business Data Cloud for the contract start date.

Where do I find more detailed information?

For more details I recommend these three resources:

SAP Community Blog by @Ammar_Naji:
🔗 UPDATE: SAP Datasphere and SAP Analytics Cloud Availability via SAP Business Data Cloud

SAP Help documentation for detailed steps:
🔗 Rewiring of Standalone SAP Datasphere and SAP Analytics Cloud Subscriptions to SAP Business Data Cloud

🔗 SAP Note 3650483

Let me know if there are still questions about rewiring and post them below 👇 in the comments section. I will try to answer them and loop in colleagues in case I do not know the answer myself.

Authorization setup for SAC Import Model from S4 ABAP CDS Views

2025-12-24T13:05:47.674000+01:00

Purpose of this document

The purpose of this blog is to explain how to implement data-level authorization in SAP Analytics Cloud Stories when:

Data is extracted from SAP S/4HANA CDS Views
The connection type is Import Model
Authorization must be enforced based on Company Code and Plant

This approach ensures that users can access only the data they are authorized to see, even though the data is physically stored in SAC.

Business Use Case

In many finance and controlling scenarios:

A single SAC Story is shared across multiple business users
Users belong to different Company Codes and Plant
Data is periodically imported from SAP S/4HANA into SAC

Since Import Models store data in SAC, runtime authorisation from S/4HANA (CDS DCLs) is not applied. Therefore, authorization must be designed and enforced within SAC.

Authorization Concept for Import Models

When working with SAC Import Models, authorization is handled as follows:

Layer	Responsibility
SAP S/4HANA CDS	Defines which data is extracted
SAC Model	Enforces row-level data authorization
SAC Story	Automatically respects model-level security

Key points to note:

CDS DCLs are evaluated only at data extraction time
SAC Data Access Control (DAC) governs user visibility
Story designers do not need to manage authorization logic

Example Used in This Blog

The same example is used consistently throughout the blog.

Attribute	Value
Authorization Dimensions	Company Code, Plant
Sample Company Code	0100
Sample Plant	RA01, RA02, …
SAC Connection Type	Import
Source System	SAP S/4HANA CDS View

Step-by-Step Authorization Setup

Step 1: Prepare CDS View in SAP S/4HANA

Ensure that the CDS View contains the fields required for authorization, i.e., Company Code and Plant.

Example CDS Used:

@AbapCatalog.sqlViewName: 'ZCDEMO002V'
@AbapCatalog.compiler.compareFilter: true
@AbapCatalog.preserveKey: true
@AccessControl.authorizationCheck: #NOT_REQUIRED
@EndUserText.label: 'Demo Authorization Consumption VIew'
@Metadata.ignorePropagatedAnnotations: true
@Analytics.query: true
@VDM.viewType: #CONSUMPTION
define view ZC_DEMO_AUTH_2
  as select from ZI_DEMO_AUTH_1
{
  PurchaseOrder,
  PurchaseOrderItem,
  PurchaseOrderCategory,
  CompanyCode, // Authorization field
  Plant,       // Authorization Field
  @Semantics.currencyCode: true
  DocumentCurrency,
  @DefaultAggregation: #SUM
  @Semantics.amount.currencyCode: 'DocumentCurrency'
  PurOrderNetAmount
}

Important notes:

Authorization-relevant fields must be part of the CDS output
CDS DCLs are not used for runtime authorization in SAC Import Models

Step 2: Create Import Model in SAP Analytics Cloud

Navigate to Connections in SAC.
Create an SAP S/4HANA Import Connection.
Import the CDS View as a Model.

At this stage, the data is physically stored in SAP Analytics Cloud.

Step 3: Enable Data Access Control (DAC)

Open the imported Model
Navigate to Settings
Navigate to Model Preferences
Enable Data Access Control

This step activates row-level authorization for the model.

Step 4: Mark Authorization-Relevant Dimensions

Enable data access control for the required dimensions.
Select CompanyCode, and Plant.

These dimensions are now evaluated during data access.

Step 5: Create Data Access Roles

Navigate to Security → Data Access
Create a new Data Access Role

Provide Role information, Role template, and review before creating. Template helps to create roles fast for general sections like BI Admin or Planner. In this we will choose template as ‘Blank’.

After role is created, we will move to ‘Select Model’, choose your import model.

Select dimensions required for authorizations and provide values for each.

Step 6: Assign Data Access Role to Users

Navigate to Security → Roles
Select the relevant user
Assign Business Role – SAC_ROLE_DEMO_001 to users for Row-level authorization.
The user now inherits Company Code and Plant restrictions.

Step 7: Validate Authorization in SAC Story

Create or open a Story based on the model.
Add a table or chart in story for validation.
Include Company Code and Plant as dimensions.
Authorization is automatically enforced at runtime.

Results

After completing the setup:

Data visibility is restricted at row level.
A single Story can be reused for multiple user groups.
No duplication of Stories or Models is required.
Authorization is centrally managed in SAC.
Import performance remains unchanged.

Best Practices

Enable Data Access Control immediately after model creation.
Use consistent authorization dimensions across models.
Follow clear and business-aligned naming conventions for roles.
Do not rely on CDS DCLs for Import Model security.
Always validate authorization using multiple test users.

Conclusion

When working with SAP Analytics Cloud Import Models based on SAP S/4HANA CDS Views, authorization must be implemented within SAC.

By using:

Data Access Control
Authorization-enabled dimensions
Data Access Roles

you can achieve secure, scalable, and reusable Story-level authorization based on Company Code and Plant fully aligned with standard SAP practices.

SAP Datasphere News in December

2026-01-09T10:11:58.169000+01:00

SAP Datasphere News in December

What a year it has been. Wow! 2025 was packed with great events, lots of new features and enhancements to extend existing features, and the launch of SAP Business Data Cloud.
Explore the latest updates in our community news blogs and enjoy watching my top feature highlights for 2025 on YouTube 📺 below.

SAP Datasphere 2025 highlights in a nutshell

What a great ride it has been in 2025 with SAP Datasphere!
Let me share a few of my personal highlights.

SAP Business Data Cloud

The announcement of SAP Business Data Cloud in February this year made quite some noise and puts data, analytics and AI at the center of our portfolio at SAP. We delivered SAP Business Data Cloud in May 2025 as a fully managed software as a service solution that unifies and governs all SAP data and seamlessly connects with third-party data.

It is an evolution of the business data fabric journey we started with SAP Datasphere and SAP Analytics Cloud some years ago and also offers a path forward for your investments in SAP BW.

Delivering out-of-the-box always-on data products is the next pivotal step in our data strategy following a harmonized data model to provide scalable access without duplication using zero-copy delta share technology.

SAP BDC serves as the foundation for your data and AI projects leveraging SAP managed and custom data products, including our growing open partner ecosystem with Databricks and the newly announced partnerships with Google Big Query, Snowflake and Microsoft Fabric.

My top features in 2025

Picking my top features for all of 2025 was not easy and here is my top feature list in no particular order.

Object store (general availability)

In 2025 we delivered the option to include an object store next to the well-known SAP HANA Cloud based persistency for a cost-efficient storage. You can use the existing tooling you already know to now create local tables on files, use replication flows, transformation flows which are then using Spark as compute engine - all orchestrated by task chains. You can load large quantities of data via replication flows and prepare the data using transformation flows and then share data to other spaces to be used as a source for additional flows, views, and of course analytic models. A really great enhancement!

Versioning of data builder objects

Object versioning has been introduced for views, analytic models, data access controls, and local tables. The new object versions are created automatically at the time of deployment which allows you now to list the past versions of objects, open a past version in read-only mode in a new tab, download a past version to a CSN/JSON file and restore a past version to replace the current version.

Be aware that the saving of an error-free restored version overwrites the current object. It is also important to understand that the versioning happens for the metadata of an object and not the data.

Efficiency features

Then there are tons of smaller enhancements that have been delivered in 2025 to increase efficiency and productivity overall. Let me just name a few here for the different components of Datasphere.

Analytic model

stacking and reusing of models
support for structures and data access controls for analytic models
we introduced a measure-dependency graph, the fiscal time dimension and unit conversion
and many enhancements with variables and parameters

Replication flows

delta only load type
support for views
reusing of source objects in multiple flows
many new sources and targets

Transformation flows

batch processing
parameter support
HANA tables as source in Spark transformation flows
other features bringing Spark transformation flows almost on par with HANA Cloud based ones

Task chains

deep retry of failed tasks in nested task chains
Rest API
Parameter support
notification tasks
amongst other improvements

Catalog

Support for SAP BW/4HANA, seamless planning, SAP BDC data products, Databricks via BDC connect
GenAI-assisted generation of catalog asset descriptions and classifications
Mass import of existing glossaries, terms, KPI’s, etc. from flat files

SAP Cloud application lifecycle management

SAP Datasphere is now integrated into SAP Cloud application lifecycle management (ALM) for health, job and automation monitoring.

Health monitoring enables you to check the health of one or more Datasphere tenants from the Health Monitoring app in Cloud ALM to check on

Memory and Disk usage
Out of Memory Events
Task Failures and
Admission Control Events

Job and automation monitoring enables you to monitor the tasks runs and executions in one or more SAP Datasphere tenants from the Job and Automation Monitoring app in Cloud ALM.

The integration overall eases the monitoring of an SAP Datasphere tenant in a complex customer landscape with a centralized, consistent way of tracking the tenants.

In addition, the early watch alert reports are now also available for SAP Datasphere. EWA is an automatic service analyzing the essential administrative areas of your SAP systems and covered by your maintenance agreement.

Data access controls based on IdP attributes

Data access controls based on identity provider (IdP) attributes are a small enhancement, but it is very powerful. You can now use values in custom attributes provided by your IdP as identifiers in your data access controls and define conditions that apply to users with these values. It is powerful because using these attributes allows you to apply a single condition to all users whose accounts contain that specific attribute value. In other words, you can group users with attributes to assign permissions to a group instead of individual users.

YouTube feature highlights of 2025

And more feature highlights of 2025

There are so many more great features which we delivered in 2025, so in addition to my 2025 top features, I pulled together the 2025 feature highlights in all different areas of SAP Datasphere for you. Enjoy the ride!

Data Integration

Catalog & Monitoring

Data Modelling

Miscellaneous

What exactly is rewiring for SAP Datasphere?

I have heard that question so many times over the last few months from customers, partners, but also SAP colleagues at events like SAP TechEd in Berlin or Sydney.
During these discussions I observed that people unintentionally misinterpret the term “rewiring”. It sounds technical, hence there are associations with data, object or even full tenant migrations. This is not the case!

Therefore, I wrote a blog to clarify what rewiring is, and what it is not. Moreover, give you some guidance on the steps which you need to take.
🔗 https://community.sap.com/t5/technology-blog-posts-by-sap/rewiring-of-sap-datasphere-to-sap-business-data-cloud/ba-p/14292149

More blogs about SAP BDC and Datasphere to check out 👇

Working with BW Authorizations in Datasphere on Enterprise Level

2026-01-14T22:07:10.971000+01:00

Related resources

Blog Post: Introducing Remote Authorizations from SAP BW/4HANA for SAP Datasphere
Blog Post by @Martin_Kuma: BW-Like Authorizations in Datasphere (DSP)
Documentation: Import SAP BW and SAP BW∕4HANA Analysis Authorizations
Note: 3062381

Why resolve BW authorizations in Datasphere?

In enterprise analytics SAP Datasphere often needs to federates the SAP BW analysis authorization table RSDWC_RSEC_DAC to data access controls (DAC). We build on this SAP standard (no custom BW developments are required beyond generation/extraction of the standard table) and bring the resolution step into the HANA layer for scale and performance.

Performance matters: The default runtime-only resolution evaluates long WHERE predicate strings per session user and iterates them at query time. With many users and complex rules, that becomes inperformant and unpredictable in terms of latency. By pre‑resolving per objectinto a persisted table we decouple expensive checks from BI queries, achieve consistent, auditable results, and enable scheduled refreshes.

Prerequisites

RSDWC_RSEC_DAC as a Remote Table in Datasphere. Generate/refresh it in BW with transaction RSDWC_DAC_RSEC_GEN and the BAdI RSDWC_DAC_RSEC_USER_UPDATE (and optionally schedule report RSDWC_DAC_RSEC_EXTRACT). The table can then be exposed to Datasphere.
A HANA DB user (Open SQL schema) in Datasphere. This user owns the Function, Procedure and Table and is granted to the space technical user to run in Task Chains.
Optional: an Entities/"booked values" table/view. Applying predicates to the set of actually booked combinations avoids generating all possible cross‑combinations which can be a huge performance multiplier and inflate result sizes.

Where our default approaches falls short

Runtime resolution per current user leads to high CPU/time due to long predicate strings.
SQL views in Datasphere lack loops/cursors; scaling to many users is cumbersome.
Duplicating views per user and UNION them is not maintainable and limits parallelization.

Example at a glance

In this blog’s walkthrough OBJECT01 is our authorization object that groups the key BW characteristics used for data access in the BWAUTH02 mapping. Concretely, OBJECT01 controls combinations of the following standard InfoObjects:

/BIC/0COMP_CODE (Company Code)
/BIC/0PLANT (Plant)
/BIC/0SALESORG (Sales Organization)
/BIC/0PROFIT_CTR (Profit Center)
/BIC/0CS_PLEVEL

The SAP standard table RSDWC_RSEC_DAC (federated as a remote table to Datasphere) holds per BW user and per object the dynamic WHERE‑predicate string that expresses allowed values/ranges for these characteristics.

Solution overview: Function + Procedure + Table + Task Chain + View

We implement five building blocks in an Open SQL schema and orchestrate them from Datasphere:

Table Function `TF_AUTH_RESOLVE_OBJECT01_BWAUTH02`: reads normalized WHERE predicate from RSEC view and applies it to booked entities via `APPLY_FILTER`.
Procedure `SP_AUTH_OBJECT01_REFRESH_BWAUTH02`: loops over all BW users for OBJECT01 and persists the union of results into a target table (full refresh).
Persisted table `AUTH_OBJECT01_RESULT_BWAUTH02`: mirrors function output for /BIC/0* fields.
View `GV_AUTH_OBJECT01_RESULT_BWAUTH02`: exposes the table for DACs and downstream pipelines.
Task Chain `TC_AUTH_OBJECT01_REFRESH_BWAUTH02`: schedules the refresh procedure regularly.

Source artifacts and naming

RSEC (clean) view: "DEMO_AR_001"."GV_RSDWC_RSEC_DAC_BWAUTH02" (column FILTERFIELDNM holds the normalized predicate)
Entities view (booked combinations): "DEMO_AR_001"."GV_OBJECT01_DAC_ENTITIES_BOOKED_BWAUTH02"
Target table: "DEMO_AR_001#HDB_USER01"."AUTH_OBJECT01_RESULT_BWAUTH02"
Exposing view: "DEMO_AR_001"."GV_AUTH_OBJECT01_RESULT_BWAUTH02"
Task Chain: TC_AUTH_OBJECT01_REFRESH_BWAUTH02

Target table

CREATE COLUMN TABLE "DEMO_AR_001#HDB_USER01"."AUTH_OBJECT01_RESULT_BWAUTH02" (
    "User"             NVARCHAR(256),
    "OBJECTNAME"       NVARCHAR(40),
    "/BIC/0PLANT"      NVARCHAR(20),
    "/BIC/0SALESORG"   NVARCHAR(20),
    "/BIC/0CS_PLEVEL"  NVARCHAR(20),
    "/BIC/0PROFIT_CTR" NVARCHAR(20),
    "/BIC/0COMP_CODE"  NVARCHAR(20)
);

Table Function

/* --------------------------------------------------------------------------------------------------
   Object:   FUNCTION "DEMO_AR_001#HDB_USER01"."TF_AUTH_RESOLVE_OBJECT01_BWAUTH02"
   Purpose:  For a given BW user, returns the authorized ENTITY combinations
             for OBJECT01 (fixed here; can be parameterized later).
             The WHERE predicate string is read dynamically from the RSEC view
             and applied to the Entities view via APPLY_FILTER.

   Input:    P_USER (NVARCHAR(256)) – BWUSERID for which the authorization resolution is executed.

   Output:   Result set with the following columns (aligned to the new standard InfoObjects):
             "User", "OBJECTNAME",
             "/BIC/0PLANT", "/BIC/0SALESORG", "/BIC/0CS_PLEVEL",
             "/BIC/0PROFIT_CTR", "/BIC/0COMP_CODE"

   Assumptions & behavior:
     - Exactly ONE record exists in the RSEC view for (BWUSERID = P_USER, OBJECTNAME = 'OBJECT01_BWAUTH02').
       -> SELECT ... INTO expects exactly 1 row. More than one -> "row not unique"; none -> "no data found".
     - INFOOBJ is intentionally fixed to 'OBJECT01_BWAUTH02' for this scenario.
     - APPLY_FILTER expects a syntactically valid WHERE string (without the "WHERE" keyword).

   Security/runtime:
     - SQL SECURITY INVOKER: Privilege checks are executed in the caller's context.
       -> Caller must have SELECT on the views used in the space/schema.
     - APPLY_FILTER is safer than EXEC/EXECUTE IMMEDIATE regarding SQL injection,
       however FILTERFIELDNM must be maintained reliably in the RSEC view.
---------------------------------------------------------------------------------------------------*/
CREATE FUNCTION "DEMO_AR_001#HDB_USER01"."TF_AUTH_RESOLVE_OBJECT01_BWAUTH02"(
    IN P_USER NVARCHAR(256)
)
RETURNS TABLE (
    "User"           NVARCHAR(256),
    "OBJECTNAME"     NVARCHAR(40),
    "/BIC/0PLANT"    NVARCHAR(20),
    "/BIC/0SALESORG" NVARCHAR(20),
    "/BIC/0CS_PLEVEL" NVARCHAR(20),
    "/BIC/0PROFIT_CTR" NVARCHAR(20),
    "/BIC/0COMP_CODE"  NVARCHAR(20)
)
LANGUAGE SQLSCRIPT
SQL SECURITY INVOKER
AS
BEGIN
    /* Local variables:
       - FILTER_CLAUSE: holds the dynamic WHERE predicate read from the RSEC view
       - INFOOBJ: fixed to OBJECT01 (current scenario)
    */
    DECLARE FILTER_CLAUSE NCLOB;
    DECLARE INFOOBJ NVARCHAR(40);

    /* Fixed authorization object; can be modeled as IN parameter if needed */
    INFOOBJ = 'OBJECT01';

    /* 1) Load filter string for the user.
          Expects exactly one match. If SELECT returns 0 or >1 rows, HANA raises an exception.
          This should be ensured in the RSEC view content. */
    SELECT FILTERFIELDNM
      INTO FILTER_CLAUSE
      FROM "DEMO_AR_001"."GV_RSDWC_RSEC_DAC_BWAUTH02"
     WHERE BWUSERID   = :P_USER
       AND OBJECTNAME = INFOOBJ;

    /* 2) APPLY_FILTER:
          - Applies the dynamic WHERE expression to the entities view.
          - The result is materialized in the implicit table variable lt_result. */
    lt_result = APPLY_FILTER(
        "DEMO_AR_001"."GV_OBJECT01_DAC_ENTITIES_BOOKED_BWAUTH02",
        :FILTER_CLAUSE
    );

    /* 3) Assemble result:
          - DISTINCT -> avoid duplicates.
          - P_USER and INFOOBJ are carried as constant columns. */
    RETURN
    SELECT DISTINCT
        :P_USER    AS "User",
        INFOOBJ    AS "OBJECTNAME",
        "/BIC/0PLANT",
        "/BIC/0SALESORG",
        "/BIC/0CS_PLEVEL",
        "/BIC/0PROFIT_CTR",
        "/BIC/0COMP_CODE"
    FROM :lt_result;
END;

You can check how it is working:

SELECT * FROM "DEMO_AR_001#HDB_USER01"."TF_AUTH_RESOLVE_OBJECT01_BWAUTH02"('ABC02')

Procedure

/* --------------------------------------------------------------------------------------------------
   Object:   PROCEDURE "DEMO_AR_001#HDB_USER01"."SP_AUTH_OBJECT01_REFRESH_BWAUTH02"
   Purpose:  Full-refresh build of table "AUTH_OBJECT01_RESULT_BWAUTH02":
             - Collect all BWUSERID relevant for OBJECT01 (from GV_RSDWC_RSEC_DAC_BWAUTH02)
             - For each user, call table function TF_AUTH_RESOLVE_OBJECT01_BWAUTH02
             - Persist the union of results into the target table

   Flow:
     1) Cursor over DISTINCT BWUSERID from the RSEC view (OBJECTNAME = 'OBJECT01')
     2) DELETE the target table (clear all rows for a full rebuild)
     3) FOR-loop: INSERT ... SELECT from the table function for each user

   Security:
     - SQL SECURITY INVOKER: privilege checks run in caller context
     - Caller requires:
       * SELECT on source RSEC view
       * INSERT/DELETE on target table

   Performance:
     - Loop calls the TF once per user (N calls).
       For large N, consider a set-based approach (bulk table function) later.

   Notes:
     - INSERT lists columns explicitly to guard against future schema changes
---------------------------------------------------------------------------------------------------*/
CREATE PROCEDURE "DEMO_AR_001#HDB_USER01"."SP_AUTH_OBJECT01_REFRESH_BWAUTH02" ()
LANGUAGE SQLSCRIPT
SQL SECURITY INVOKER
AS
BEGIN

  /* 1) Cursor: distinct list of relevant users for OBJECT01 */
  DECLARE CURSOR cur_users FOR
    SELECT DISTINCT BWUSERID
      FROM "DEMO_AR_001"."GV_RSDWC_RSEC_DAC_BWAUTH02"
     WHERE OBJECTNAME = 'OBJECT01'
       AND BWUSERID   IS NOT NULL;

  /* 2) Full rebuild: clear target table */
  DELETE FROM "DEMO_AR_001#HDB_USER01"."AUTH_OBJECT01_RESULT_BWAUTH02";

  /* 3) Implicit FOR-loop: HANA opens/closes the cursor automatically */
  FOR cur_row AS cur_users DO

    INSERT INTO "DEMO_AR_001#HDB_USER01"."AUTH_OBJECT01_RESULT_BWAUTH02"
      ( "User", "OBJECTNAME",
        "/BIC/0PLANT", "/BIC/0SALESORG", "/BIC/0CS_PLEVEL",
        "/BIC/0PROFIT_CTR", "/BIC/0COMP_CODE" )
    SELECT
        "User", "OBJECTNAME",
        "/BIC/0PLANT", "/BIC/0SALESORG", "/BIC/0CS_PLEVEL",
        "/BIC/0PROFIT_CTR", "/BIC/0COMP_CODE"
    FROM "DEMO_AR_001#HDB_USER01"."TF_AUTH_RESOLVE_OBJECT01_BWAUTH02"( :cur_row.BWUSERID );

  END FOR;

END;

Execute via Task Chain step or manually:

CALL "DEMO_AR_001#HDB_USER01"."SP_AUTH_OBJECT01_REFRESH_BWAUTH02"();
SELECT * FROM "DEMO_AR_001#HDB_USER01"."AUTH_OBJECT01_RESULT_BWAUTH02";

Security, view exposure, and task chain

Create Datasphere view: "DEMO_AR_001"."GV_AUTH_OBJECT01_RESULT_BWAUTH02" over the table to feed DACs and downstream processes.

Schedule a Task Chain "TC_AUTH_OBJECT01_REFRESH_BWAUTH02" that calls the procedure for regular refresh.

To add the procedure your user needs to be granted:

-- Minimal grants (adapt to your technical user / space)
GRANT EXECUTE ON PROCEDURE "DEMO_AR_001#HDB_USER01"."SP_AUTH_OBJECT01_REFRESH_BWAUTH02" TO "DEMO_AR_001";
GRANT SELECT, INSERT, DELETE ON "DEMO_AR_001#HDB_USER01"."AUTH_OBJECT01_RESULT_BWAUTH02" TO "DEMO_AR_001";
-- Optional: schema-wide
GRANT EXECUTE ON SCHEMA "DEMO_AR_001#HDB_USER01" TO "DEMO_AR_001" WITH GRANT OPTION;

If you do not know the needed Schema:

SELECT SCHEMA_NAME, SCHEMA_OWNER
FROM SYS.SCHEMAS

Performance & scalability

Pre-resolving moves string evaluation out of query-time; persisted results enable indexes and faster joins.
Keep the entities view "booked" to avoid generating all combinations (Cartesian blow-up).
For very large user counts, consider a set-based bulk table function to avoid N function calls.

Troubleshooting

Error 260 invalid column name: fix duplicated /BIC/ prefixes or mismatched InfoObject names.
No data found / row not unique: enforce exactly 1 row per (BWUSERID, OBJECTNAME) in RSEC view.
Permissions: INVOKER needs SELECT on sources and INSERT/DELETE on target; scheduler user needs EXECUTE on procedure.

What you get

A maintainable, enterprise-ready pattern to pre-resolve BW authorizations in SAP Datasphere, fully based on SAP standard RSDWC_RSEC_DAC: reproducible results, faster queries and a scheduling hook via Task Chains. From here, you can attach DACs to exposed views or plug the table into harmonization and governance pipelines.

High Impact SAP Data Objects to Clean for an S/4HANA Selective Transition from ECC

2026-01-15T13:38:05.506000+01:00

*Views Expressed here are my own and doesn't represent any entity.

Introduction

Moving from SAP ECC to S/4HANA using a Selective Data Transition is not just a technical system conversion,it is a data transformation journey. While selective carve outs allow organizations to migrate only relevant organizational units, time slices, or data sets, poor data quality can jeopardize the entire S/4HANA transformation.S/4HANA’s simplified data model, stricter checks, and real-time processing mean that legacy inconsistencies tolerated in ECC will cause errors, functional gaps, or performance issues in S/4HANA.

This blog outlines the SAP data objects that require mandatory data cleaning before a selective S/4HANA migration, along with practical examples and rationale for each.

Why Data Cleaning Is Mandatory in a Selective Transition

In a selective approach:

Only chosen company codes, plants, or years are migrated
Historical inconsistencies become more visible
Data is restructured, not simply converted

S/4HANA:

Enforces business partner (BP) as a single master
Removes many legacy tables
Uses real-time reconciliation
Requires consistent master and transactional data

Without cleaning, you risk:

Migration failures
Reconciliation mismatches
Broken business processes
Delayed go-lives

1. Business Partner (Customer & Vendor)

What Needs Cleaning

Duplicate customers/vendors
Inconsistent master data across company codes
Missing mandatory fields (address, tax, bank details)
Vendors acting as customers (and vice versa)

ECC Example

Vendor 100045 exists for AP
Customer 200078 exists for AR
Same legal entity, different tax IDs and addresses

Why This Is Mandatory

In S/4HANA:

Customers and Vendors are merged into Business Partner
Roles (Customer, Supplier) are assigned to a single BP number
Inconsistent data causes CVI (Customer Vendor Integration) errors

Rationale

Selective transitions often migrate only certain company codes, making duplicates harder to reconcile post-migration. Cleaning ensures:

Smooth BP conversion
Accurate AR/AP reporting
Compliance with S/4 data model

2. Material Master Data

What Needs Cleaning

Obsolete or unused materials
Incomplete views (MM, SD, PP)
Invalid units of measure
Duplicate material numbers

ECC Example

Material created but never used in 8 years
Sales view missing for materials with open SD history
Base UoM inconsistent with alternative UoM

Why This Is Mandatory

S/4HANA:

Performs real-time MRP
Uses simplified inventory tables (MATDOC)
Enforces stricter checks on material consistency

Rationale

Migrating unused or inconsistent materials:

Increases migration volume
Slows performance
Causes MRP and ATP errors

Selective transitions benefit most by reducing material footprint.

3. Chart of Accounts & G/L Accounts

What Needs Cleaning

Blocked or unused G/L accounts
Accounts without proper account type
Inconsistent descriptions across company codes
Legacy reconciliation accounts

ECC Example

G/L 799999 created for testing, never used
Same G/L has different names in different company codes

Why This Is Mandatory

S/4HANA:

Introduces Universal Journal (ACDOCA)
FI and CO are merged
Reconciliation accounts behave differently

Rationale

Dirty G/L data leads to:

Conversion errors
Financial reconciliation issues
Incorrect financial statements

In selective transitions, only active and relevant accounts should be migrated.

4. Open Financial Transactions (AR/AP)

What Needs Cleaning

Cleared documents not properly closed
Open items with incorrect statuses
Residual items with zero balance
Old parked documents

ECC Example

Invoice marked as open but cleared manually
Zero-balance open items still flagged as open

Why This Is Mandatory

S/4HANA:

Performs real-time reconciliation
No longer tolerates technical inconsistencies
Universal Journal requires exact balances

Rationale

Open AR/AP items are always migrated in selective scenarios. Incorrect open items cause:

Balance mismatches
Failed migrations
Post-go-live reconciliation chaos

5. Asset Accounting (FI-AA)

What Needs Cleaning

Assets without acquisition values
Assets without depreciation areas
Retired assets still marked as active
Incomplete asset master records

ECC Example

Asset created but never capitalized
Depreciation area missing for a company code

Why This Is Mandatory

S/4HANA:

Uses New Asset Accounting only
Fully integrated into Universal Journal
Legacy asset inconsistencies cannot be converted

Rationale

Selective transitions often migrate only active assets. Cleaning ensures:

Correct depreciation
Accurate asset balances
Compliance with S/4 FI-AA model

6. Open Logistics Transactions

What Needs Cleaning

Open purchase orders with no activity
Incomplete deliveries
Open production orders never closed
Inconsistent goods movements

ECC Example

PO created 6 years ago, no GR, no IR
Production order technically complete but not closed

Why This Is Mandatory

S/4HANA:

Uses MATDOC for inventory
Enforces strict document flow
Real-time stock valuation

Rationale

Open logistics documents are technically migrated, not recreated. Dirty data causes:

Stock inconsistencies
Migration failures
Incorrect inventory valuation

7. Customizing and Organizational Data

What Needs Cleaning

Unused plants, storage locations
Inactive company codes
Invalid assignments (plant to company code)
Legacy controlling areas

ECC Example

Plant exists but no postings in 10 years
Company code included but not in scope

Why This Is Mandatory

Selective transitions rely on precise scoping. Invalid org data:

Complicates data slicing
Causes migration logic conflicts
Increases testing effort

Rationale

Clean org structures ensure:

Correct selective extraction
Lean S/4 system
Lower total cost of ownership

8. Custom Code & Z-Objects (Indirect Data Impact)

What Needs Cleaning

Z-tables with obsolete data
Custom programs using removed tables
Hard-coded company codes or years

ECC Example

Z-table referencing BKPF/BSEG directly
Custom report using VBFA incorrectly

Why This Is Mandatory

While not migrated as data:

Custom code processes migrated data
Dirty Z-data can break business processes

Rationale

Selective data migration often includes selective Z-data. Cleaning avoids:

Runtime errors
Incorrect reporting
Post-go-live defects

Understand your Business "WHY" for DATA CLEARLY

A Selective S/4HANA transition magnifies data quality issues because only parts of the system are migrated. S/4HANA’s simplified architecture does not tolerate inconsistencies that ECC allowed for decades.

Mandatory Data Cleaning Ensures:

Successful migration runs
Accurate financial reconciliation
Stable business processes
Long-term S/4HANA performance

Key Takeaway:

Data cleansing is not a technical task—it is a business transformation enabler.

#S4HANA #DATA #DATACLEANSING #MandatoryS4HANADATAOBJECTS #SAPECCtoS4HANA #SelectiveTransformation #DataQuality #ERPTransformation

#LegacyModernization

Learn with the experts - The new SAP Learning Community for Data and Analytics

2026-01-23T12:22:25.866000+01:00

Last month SAP launched the new learning community for Data and Analytics. This community is aimed at anyone who is working through SAP learning content or perhaps preparing for certification. You can ask questions, read insightful blog posts, and stay up to date with learning content announcements such as new or updated courses, certifications, links to deep-dive webinars and other learning events.

This community provides you with access to the entire team of SAP Data and Analytics instructors who develop the courses and certifications, and of course, deliver the SAP classes. They regularly write blog posts in the community and will answer your questions to ensure you make good progress with your learning.

The solutions that we cover in this community include:

SAP Business Data Cloud
SAP Datasphere
SAP Analytics Cloud
SAP BW and SAP BW/4HANA
SAP BusinessObjects
SAP S/4HANA Embedded Analytics
SAP HANA Modeling and Data Provisioning
Developing data models using ABAP CDS

If you run into trouble whilst working through learning content, just post a question and one of the team will get back to you.

Please note that the Data and Analytics community provides support relating specifically to the learning content of data and analytics solutions. SAP are currently launching learning communities covering all other SAP solutions. Click here to get to the home page where you see all available learning communities. If you don't see the community you are looking for, come back later. More are being added each week.

If your question is related to learning but not specifically about data and analytics topics, we also have a learning community for general questions. For example, questions relating to your Learning Hub subscription or certifications in general.

So connect with the SAP learning content development and instructor team today by joining the new SAP Data and Analytics learning community.

IMPORTANT: When you reach the community, don't forget to hit the JOIN button so that you don't miss out on content.

Introducing the SAP Analytics Cloud Dashboard Hi-Fi UI Kit

2026-01-23T18:07:46.884000+01:00

Focus of this blog:

Introduction
How the UI Kit is set up
How to get the UI Kit
How to use the UI Kit
Essential Ressources

Introduction

The new SAP Analytics Cloud Dashboard Hi-Fi UI Kit is a high-fidelity Figma kit specifically designed for creating dashboards in SAP Analytics Cloud Stories. It closely reflects Stories UI elements and is based on the SAP Fiori Morning Horizon theme.

The UI Kit includes ready-to-use dashboard templates with placeholder or example data, charts, and other UI controls. Designers can easily switch between templates, swap visualization types, and adapt dashboards as requirements evolve. This accelerates the design process while supporting a seamless transition from mock-ups to implementation and ensuring consistent results across projects.

👉 Download the UI Kit from GitHub

Why Use the SAC Dashboard Hi-Fi UI Kit?

Efficiency: Reduce repetitive design efforts with pre-built components, saving time and resources.
High-Fidelity Mockups: Design detailed and realistic dashboards that closely reflect the SAP Analytics Cloud capabilities.
Consistency: Adhere to established design standards to produce a cohesive look across various dashboards, enhancing overall user experience.

How the UI Kit is set up

The UI Kit consists of three pages for easy navigation and use.

The first page, “📓 Cover“, is a UI Kit overview that includes a cover, and a slide explaining the purpose and usage of the UI Kit, as well as semantic versioning.

"Cover" Page of the SAP Analytics Cloud Dashboard Hi-Fi UI Kit

The second page, “✏️ Analytical Pages”, provides dashboard templates. Depending on the purpose of your dashboard, you can choose to use different BI page types, or start with an empty page to design your dashboard from scratch.

"Analytical Pages" page of the SAP Analytics Cloud Dashboard Hi-Fi UI Kit

The third page, “✏️ Components”, contains all other assets: UI elements and controls, containers and different chart types available in SAP Analytics Cloud Stories.

"Components" page of the SAP Analytics Cloud Dashboard Hi-Fi UI Kit

How to get the UI Kit

Navigate to the "SAC Fiori Horizon Theme" folder on GitHub.
Open the file "SAC_Hi-Fi_Dashboard_UI_Kit.fig".
Open the "SAC Dashboard Hi-Fi UI Kit" Figma File from GitHub
Click on the Download button.
Download the "SAC Dashboard Hi-Fi UI Kit" Figma File from GitHub
Open the downloaded file in Figma. Choose a destination folder, and you're ready to go.

How to use the UI Kit

After importing the file, we suggest making the UI Kit available within your company's Figma space. This allows you to utilize Figma’s Library feature effectively. Depending on your settings, this setup enables either just your immediate team or your entire organization to access the UI Kit via the asset panel, fostering seamless collaboration and ensuring consistent design practices across your teams.
To share a UI Kit, head to the File Settings, and click on “Publish Library...”. Choose the components you wish to include and decide who should have access to the UI Kit. Then click on "Publish" to complete the process.

Note: This feature is not available if the file has been moved to your Drafts.

"Publish library..." menu option in Figma

You can now incorporate the SAP Analytics Cloud Dashboard Hi-Fi UI Kit into your files via the libraries dialog, making all components conveniently accessible from the assets panel.

If you prefer not to publish the UI Kit internally, you can easily copy and paste the components into your own design files or use them as local assets.

Are you ready to transform your dashboard design process?

Then download the SAC Dashboard Hi-Fi UI Kit from GitHub and get started today!

Essential Ressources

SAC Fiori Horizon Theme folder on GitHub

Dashboard Design Best-Practices

How to Design Low-Fidelity Dashboards with the SAP Analytics Cloud Dashboard Schematics UI Kit

SAP Analytics Cloud compass in Q1 2026 - New Use Cases, Enhanced Workflow

2026-01-26T21:58:07.037000+01:00

2026 is going to be an exciting year for compass! For fans of Monte Carlo Simulation and scenario modelling in SAP Analytics Cloud, we shall kick the year off with three highly requested enhancements for Q1:

Supporting AVG excl. NULL and AVG incl. unbooked for drivers
Enabling simulation from dashboards with complex filters
Enabling changing of target settings after simulation creation

Let me walk you through them:

AVG excl. NULL and AVG incl. unbooked for drivers

Previously, users were only able to model the uncertainty of key drivers with SUM aggregation or SUM exception aggregation. Even though this applies for most key drivers, users wishing to simulate with averages were left out of the game. The new support of AVG excl. NULL and AVG incl. unbooked for drivers will open the door now to simulations with average prices, average discount, average hours or any KPI which should not be summed up to reflect accurate performance assessment. This means that simulation for use cases such sales promotions or energy expenses etc. are now possible.
(If you are unsure about the differences between the different AVG variants, check out this great blog from Alecsandra Dimofte.)

Before the enhancement, key drivers modelled with AVG excl. NULL or incl. unbooked will be blocked from simulation.

Now users will be able to model uncertainties for key drivers modelled with AVG excl. NULL or incl. unbooked.

Enabling simulation from dashboards with complex filters

In the past, complex filters applied on the story, page or table level would block the creation of compass simulations directly from the story table. These complex filters include:

Attribute
Range
Exclude
Advanced filter

With the latest enhancement, the restriction is lifted and users could directly create a compass simulation right off the dashboard even with this setup. Dashboard creators, especially those dependent on time range filters or exclude filter for ease of selection, will benefit from the flexibility this feature brings to their story design to provide end users with a direct trigger for compass simulation. Take note that the complex filters applied will be displayed as read-only in compass though. Users are free to simulate right away in compass or to substitute the complex filters with something simpler before proceeding, if preferred.

Range filters and other complex filters will no longer block the direct trigger of a compass simulation from a story table.

Enabling changing of target settings after simulation creation

Before Q1 2026, it was not possible to make adjustments to a compass simulation target setting once a scenario has been created. Any changes to the target setting after simulation creation would require manual removal of existing scenarios, as it would be a change in the simulation calculation.
With the latest enhancement, the workflow has become more flexible. Users can change the target settings at any point in time. After applying the changes, the prior simulation results of private scenarios will be discarded (as the prior calculation will differ from that of the current target settings), but the driver value configurations will be retained. Users seeking to simulate impact on another similar target KPI will benefit from the ability to continue right away without having to set up identical assumptions for common drivers again.
Public scenarios, on the other hand, will be marked as invalid, but both results and driver value configurations will be retained, in case there is a need to archive past simulations despite the difference in target settings.

Simulation target settings can be changed now even after scenario creation. Driver value configurations will be retained in the private scenarios.

With these three new enhancements in Q1 2026, compass users will experience more flexibility in handling, with better re-use of assumption modelling efforts, and more variety in the spectrum of possible drivers.

As usual, we are always on the look out for areas where compass could be sharpened even further. Have an opinion here? Do tell us what you think in the influence portal! In fact, our first feature in Q1 2026 was inspired by the a topic raised by @David_Nicolay_ . Thank you and do keep them coming!

SAP Analytics Cloud: Best Practices for Folder Organization and Governance

2026-02-03T20:11:33.157000+01:00

Introduction

In SAP Analytics Cloud projects, a significant amount of effort is spent on building models, stories, and planning applications. However, one critical topic is often underestimated: how these objects are organized. In many customer environments, reports and models are scattered across different folders without a clear structure, making it difficult to control access, ensure consistency, and maintain content over time. This is not just a usability issue, it is a governance challenge. A well-designed folder structure is a foundational element for scalable, secure, and maintainable SAC landscapes.

Why Folder Organization Matters in SAC?

Folder structure is not a cosmetic choice, it is a governance decision.

Number of objects like Stories, Models, Themes, Insights are rapidly increasing in SAC environments. It is crucial to keep these objects in organized way. Otherwise, this can cause important security and governance issues. Without well-planned folder structure, important objects like corporate stories, models etc. can be located in My Files or Public Folders with ambiguous ownerships. It is not certain that who is using these files and folders and who are the owners. Without discrimination of folders, contents that should be tested first can be accidently used with Production purposes. Also with authorization complexity, unnecessary accesses can cause security issues. As a result, weak folder structure leads low maintainability, low security and high operational risk.

Core Folder Types

Understanding the core folder types in SAP Analytics Cloud is essential for building a sustainable governance model. Each folder type serves a specific purpose, and misusing them often leads to security risks, ownership issues, and long-term maintenance problems. The two primary folder types My Files and Public should be clearly differentiated and used intentionally.

My Files – Personal Workspace

My Files is designed as a personal working area for individual users in SAP Analytics Cloud. Content stored in My Files is accessible only by the owner and is not governed by role-based access control, which makes it suitable for early-stage development, drafts, experiments, and personal analysis. It serves as a sandbox environment where users can prototype ideas or perform temporary testing without impacting shared content. However, because objects in My Files are not centrally visible or governed unless explicitly shared, they are not appropriate for long-term or productive use.

As a best practice, any object that is actively used by business users should not located in My Files.

Public – Enterprise Content Area

Public is the central, governed content area in SAP Analytics Cloud and serves as the foundation for enterprise analytics. Unlike personal folders, content stored in Public is managed through role-based access control, enabling controlled visibility, clear ownership, and secure collaboration across teams and departments. Public folders support structured hierarchies, which makes them ideal for implementing environment separation such as DEV, TEST, and PROD, as well as department-based organization. All shared stories, models, planning applications, and related objects intended for long-term use should reside in the Public folder structure. By enforcing consistent naming conventions and access rules within Public, organizations can significantly improve maintainability, reduce operational risk, and establish a scalable governance model for SAP Analytics Cloud.

As a best practice, all productive SAC content should be located in the Public folder hierarchy.

DEV – TEST – PROD Folder Strategy

A clear DEV–TEST–PROD folder strategy is one of the most critical elements of governance in SAP Analytics Cloud. Separation between development - testing and production provides a strong folder discipline. Especially, the separation is crucial if you are in a single tenant landscape. Even in multi-tenant landscapes (Dev-Test-Prod), this is also recommended since the transportation between tenants is not mandatory and system-driven.

A structured folder strategy introduces a clear content lifecycle, reduces operational risk, and creates transparency for both IT and business users.

Recommended High-Level Folder Structure

/Public
├─ _DEV
├─ _TEST
└─ _PROD

Environment Responsibilities

DEV

Active development and frequent changes
Broad access for developers and consultants
Experimental and incomplete content allowed

TEST

Business validation and user acceptance testing
Limited write access
Focus on functional correctness and data validation

PROD

Approved, production-ready content only
Strict access control
Stable objects consumed by business users

Object Type-Based Folder Design

Standardized subfolder design based on object types is a key enabler for consistency and scalability in SAP Analytics Cloud. By structuring folders according to object types—such as Stories, Models, Analytic Applications, Data Actions, and Allocations—organizations create a predictable and intuitive navigation experience across DEV, TEST, and PROD environments.

Department-Based Folder Design

By grouping stories, models, and applications under department-specific folders such as Finance, Sales, Supply Chain, or HR, ownership and accountability become immediately clear. This approach simplifies access management through role-based authorization, reduces the risk of unintended data exposure, and improves discoverability for business users. When combined with a DEV–TEST–PROD structure, department-based folders ensure that each team works within a clearly defined and governed space, supporting scalability and long-term maintainability of the SAC environment.

Workspaces

Workspaces in SAP Analytics Cloud are designed to support collaboration by grouping content, users, and access rights within a shared space. They can be useful for project-based or temporary initiatives where multiple users work closely on the same set of stories or models.

However, from a governance perspective, I personally avoid using Workspaces as a primary content container. When a Workspace is deleted, all content stored within it is permanently removed, which introduces a significant operational risk. For this reason, Workspaces should be used selectively and primarily for short-lived collaboration, while long-term and productive content should always reside in the Public folder structure.

You can navigate to this blog post by @ninak1 to learn workspaces.

Authorization Best Practices (Team-Based Governance)

Effective authorization in SAP Analytics Cloud should be managed through teams, not individual users. Assigning access directly to users quickly becomes unmanageable as environments grow, leading to inconsistent permissions, security risks, and high maintenance effort. A team-based authorization model provides scalability, transparency, and long-term governance by clearly separating responsibilities and standardizing access patterns.

Recommended Team Personas

A clear persona model is essential for aligning technical permissions with business responsibilities. The following team structure covers most SAC scenarios while remaining simple and scalable:

Admin

System and tenant administration
User, team, and role management
Full access across DEV, TEST, and PROD

Developer

Model, story, and application development
Full write access in DEV
Limited or no write access in PROD

Editor / Analyst

Ability to create own stories and perform small adjustments
Typically business power users or advanced analysts
Write access to specific folders, without system-level permissions

Why “Editor / Analyst”?
Compared to “Developer,” this role better reflects business-driven content creation and controlled flexibility without exposing technical or administrative capabilities.

Viewer

Read-only access to validated, productive content
No content creation or modification rights

Folder Authorization via Teams

Folders should always be shared with teams, not individual users. This approach ensures that access rights are role-based, auditable, and easy to maintain.

Best Practices

DEV folders → Developer team (edit), Admin team (full)
TEST folders → Developer / Editor (limited edit), Viewer (read)
PROD folders → Viewer (read), Editor (controlled edit), Admin (full)

By assigning folder permissions at the team level, access changes are handled simply by adding or removing users from teams—without touching folder settings.

Role Assignment Strategy

In addition to folder access, SAC roles define what users can do at the platform level. These roles should also be assigned to teams rather than individuals.

Example Role Mapping

Admin team → BI Admin role
Developer team → Content Creator role
Editor / Analyst team → Content Creator (restricted by folder access)
Viewer team → Viewer role

Best Practice:
Assign roles and folder permissions to teams—not to individual users.

Here is the link of the detailed blog post for Security Concept by @Matthew_Shaw

What to Do Next

To apply these best practices in your SAP Analytics Cloud environment, start with the following steps:

Define your governance model first
Agree on how content should be developed, validated, and consumed before creating folders.
Separate DEV, TEST, and PROD clearly
Create dedicated environment folders under Public and enforce a forward-only content flow.
Organize content by department and object type
Use department-based folders combined with standardized subfolders (Stories, Models, Data Actions, etc.).
Establish a clear naming convention
Document and communicate a simple, consistent naming standard for all SAC objects.
Adopt team-based authorization
Create teams based on personas (Admin, Developer, Editor/Analyst, Viewer) and assign both roles and folder permissions to teams—not individual users.
Review and clean up existing content
Identify unused, duplicated, or misplaced objects and move productive content into the governed Public structure.

Sources

SAP Business Data Cloud and Datasphere News in January

2026-02-05T23:27:35.276000+01:00

SAP Business Data Cloud and Datasphere News in January

This year we really started with a bang!
You will see many great features and enhancements being delivered with our three releases already in the first month of the year.

Learn more in my short top features video in January 2026 on YouTube 📺

In addition, explore the latest updates in our community news blogs and more.

My top features in January

2026 really starts with a firework of excellent new features. Therefore, this list is a lot longer than usual.

Task Chains & Scheduling

Task ports of tasks in Task Chains

You can now use task ports to provide enhanced flexibility and error handling in task chains. Task ports allow you to control the flow of task based on success or failure outcomes. Additionally, the new Ignore Error feature allows you to disregard the status of a specific task from the overall task chain status evaluation. This feature is beneficial for maintaining workflow continuity, managing errors effectively, and customizing task execution paths.
🔗 More information

Enable Auto-Retry for a Task in a Task Chain

The auto-retry feature allows up to three automatic retries for a task in a task chain. This feature will automatically retry failed tasks to help complete a task in a task chain and improve resilience.
🔗 More information

Technical user for scheduling

You can now assign a technical user to schedule data integration tasks. This allows you to minimize the number of users that manage schedules. In addition, this avoids failed schedules and tasks related to expired consent and authorization.
🔗 More information: Modify the Owner of a Schedule and Scheduling Data Integration Tasks.

Manage Tasks with REST APIs

You can use REST APIs to run task chains and retrieve task logs and existing task log history without using the Command Line Interface (CLI). In addition, you can now also use a new CLI command to get flexible and controlled log information.
🔗 More information Manage Tasks Using REST APIs and Manage Tasks and Task Chains via the Command Line.

Analytic Model: New functions in the Expression Editor (YTD, QTD, MTD)

For calculated and restricted measures, as well as for calculated and restricted structures, the new standard functions YTD, QTD, MTD and %GrandTotal are now supported in the expression editor for these measures in the Analytic Model.

Replication Flow

Reuse a target table of one replication as a source of another replication flow

You can reuse a target local table (with or without delta capture enabled) of an existing replication flow as a source table in another replication flow. It will automatically create a relationship between the two replication flows and allows now multi-step replication scenarios. This simplifies the distribution of data from SAP Datasphere to multiple downstream systems.
🔗 More information.

Support parquet format for cloud storage sources

You can now replicate data stored in parquet files when using the following cloud storage providers as the source of your replication flow:

Amazon Simple Storage Service
Google Cloud Storage
Microsoft Azure Data Lake Gen2

🔗 More information.

Partitioning of local tables with data

You can create partitions on existing local tables that contains existing data stored in a standard space (HANA Cloud storage and compute). That way you can break down your data into smaller tables and better manage tables with a large volume of data.

At deployment you can decide if the partitioning task happens

Automatically After Deployment: Once the deployment is complete, the partition creation task will start automatically.
Automatically at a Scheduled Time: You define a timestamp when the partitions must be created. Once deployment is complete, the partition creation task will start automatically at the scheduled timestamp.
Manually at Later Time: Once the deployment is complete, you will be able to create the partitions when you want.

🔗 More information.

Longer technical names of data builder objects

We increased maximum length for technical names for data builder objects and elements that now allow for more descriptive and detailed naming conventions.
The maximum length has changed for the following objects and elements:

Views, tables, E/R models: 100
Views with semantic usage Analytical Dataset (Deprecated): 60
Analytic Models: 60
Hierarchies created inside a dimension view: 20
Columns: 100
Input parameters: 100

🔗 More information.

BW Modernization

We also delivered major milestones for the BW modernization scenarios. Metadata extraction of SAP BW 7.5 is now enabled for the catalog, plus also a data lineage from SAC story down to the BW 7.5 objects.

Extension of the data product generator with

Introduction of Scenario Generator: semantical import of BW 7.5 and BW/4HANA InfoProvider and its dependencies including attributes and texts
Multiple target support: select any Datasphere tenant and space
Transport of BW subscriptions
Mass handling of BW InfoProviders

Requires BWMT 1.27 and SAP Note 3692414

🔗 More information Connecting to BW Systems and Catalog Asset Details.

📺 Check out the Top Features Playlist: https://www.youtube.com/playlist?list=PL3ZRUb1AKkpS8bwmAREXo78O2xccXzoiy

Webinar Series: The Next Era of Business Data

SAP Business Data Cloud is part of a comprehensive strategy for enterprise data designed to address complex enterprise data management challenges. We provide an overview of SAP Business Data Cloud and guide you through its integral pillars and strategic vision.
👉 Sign up now: SAP BDC: The Next Era of Business Data

🔹Jan 29 | Discover SAP Business Data Cloud in 2026 - recording

🔹Feb 05 | The Smart Path to SAP BW Modernization - recording

🔹Feb 19 | Empowering SAP Datasphere Users with SAP Business Data Cloud Innovations

🔹Feb 26 | From Data to Decisions: Analytics with SAP Analytics Cloud and SAP BDC

🔹Mar 12 | From Data Silos to Business Insight: The Power of BDC Connect

🔹Mar 19 | An Architect's Blueprint for SAP Business Data Cloud as the Foundation for Enterprise AI

🔹Mar 26 | Design. Govern. Deliver. Data Products - Data Product Studio

🔹Apr 16 | Breaking Down the SAP BDC Pricing Model

My favorite blogs in January

What a great blog about seamless planning by @Max_Gander and @marc_daniau.
The blog shows how to run SAP HANA APL time‑series forecasts in SAP Datasphere and feed the results into a seamless SAP Analytics Cloud (SAC) planning model as a live forecast version:
🔗 Generating and Integrating Automated Predictive Library (APL) Forecasts in a Seamless Planning Model

@Phil_from_Madrid wrote a nice piece about implementing a "Shift-Left" architecture within the SAP business data fabric shifts responsibility for data quality to the source, aiming for data to originate as a governed, high-quality asset. While this approach accelerates time-to-insight through the creation of reusable data products, it necessitates development teams taking on new data-modeling tasks and managing increased technical debt upstream.
🔗 The consequences of implementing strictly Shift-left architectures

This blog from @vitvas details a method to execute ABAP code directly from an SAP Datasphere Data Flow without relying on the BW Bridge. This approach uses virtual elements in CDS views and a generic OData connection to trigger complex logic or external processes in the source system.
🔗 How to call AS ABAP from SAP Datasphere

More blogs about SAP BDC and Datasphere to check out 👇

Find more information and related blog posts on the topic pages for SAP Business Data Cloud and SAP Datasphere. Also check out the on SAP Help for SAP Business Data Cloud and SAP Datasphere, including for troubleshooting and analysis guides, onboarding guides, and more.

Everything about SAP migration and Palantir : A basis POV

2026-02-12T17:18:56.953000+01:00

In this blog, I discuss the Palantir platform and its value proposition in the context of SAP landscapes and SAP modernization programs.

Disclaimer: The information shared in this blog is derived from publicly available sources. The views expressed are based solely on my personal understanding and interpretation and should not be considered official guidance, implementation instructions, or vendor-approved documentation.

1.Understanding Palantir

Palantir is an enterprise platform that helps organizations use their data to run real operations and make decisions. It is not just about reporting or creating dashboards. It is ment to help users take action, track decisions and measure imapact using trusted data.

The core parts of Palantir are :

1. Data layer:

This is where Palantir stores data as datasets (like very large Excel tables) and every data has lineage. That is , Palantir always remembers where data came from, what transformations were applied, what dataset produced what dataset.

2. Object layer (Ontology):

Source:https://blog.palantir.com/how-anyone-can-integrate-sap-data-in-hours-17f23d0bd2e8

In this layer, Palantir converts datasets into:

Objects (Customer, Material, Asset, Employee, Purchase Order)
Links (Customer to Orders, Vendor to Deliveries, Asset to Maintenance)
Actions (approve, update, assign, escalate, create)

This Ontology creates a digital model of the organization, thus enabling business users to understand the data in business context rather than just a raw tables.

The typical use case of Palantir involves time-bound projects that support decision-making processes, such as inventory optimization. alert monitoring and resolution, sales territory planning, supply chain risk management.

2.Palantir architecture

Palantir’s standard architecture is built on three integrated platforms:

1. Foundry

2. AIP

3. Apollo

All these together are positioned as an Enterprise Operating System.

Foundry is the core data operations layer for data management, transformation logic, Ontology development, analytics, and workflow building.
AIP (AI Platform) adds the generative AI layer, enabling secure access to LLMs and providing tooling for agents, automations, AI apps, and governance through evaluation frameworks. (For more information on AIP, refer to URL https://www.palantir.com/docs/foundry/architecture-center/aip-architecture/ )
Apollo runs underneath as the continuous delivery and infrastructure orchestration platform, enabling large-scale, zero-downtime upgrades across the entire service mesh.

Conceptually, Palantir describes AIP and Foundry as a set of nine capability areas spanning the Ontology system, data and workflow services, analytics, automations, and product delivery. These capabilities are supported by shared foundational components such as storage, compute, networking, security, governance, and workspace, all powered by Apollo. The combined stack is positioned as the backbone for operational, AI-enabled applications across industries like healthcare, aviation, utilities, and defense.

3. How to access the Palantir platform

Refer to below documentation from Palantir site to access the platform

https://www.palantir.com/docs/foundry/getting-started/login/

4.Palantir and data connectivity

Foundry positions itself as more than a standard ETL or ELT tool. It is designed as an enterprise-grade data integration backbone that reduces the long-term cost of integration by combining connectivity, transformation, pipeline management, and governance into a single operating layer. The goal is to take data from complex environments and continuously deliver curated, operationally trusted datasets, not just move data into storage. A key part of this is Data Connection which is foundry’s application for synchronizing data from external systems into Foundry for use across the data integration, modeling, and Ontology layers. Data Connection also supports outbound connectivity, enabling writeback to external systems via exports and Webhooks.

At the connectivity layer, Foundry supports structured, semi-structured, and unstructured sources, and multiple ingestion patterns such as batch, micro-batch, streaming, and CDC. A “source” in Foundry represents a connection to an external system such as Postgres, S3, Linux file systems, SAP, or REST APIs. Connectivity can be established directly when the source is reachable from Foundry, or through a Foundry agent when sources are hosted in a separate private network such as on-prem. Agents are downloadable programs installed inside the customer network and managed from Data Connection. They provide controlled network access to private systems and securely ingest data into Foundry using restricted tokens. Once a source is created, teams configure syncs (most commonly batch syncs) through the Data Connection UI, schedule runs, and write the results into versioned Foundry datasets with full lineage and auditing.

Beyond ingestion, for transformation, Foundry adds production-grade pipeline management through tools like Pipeline Builder, which provides point-and-click pipeline creation, Git-style change management, scheduling, and health checks so only compliant datasets reach production. Data quality and governance are treated as first-class features through role-based and marking-based controls, end-to-end lineage, and built-in diagnostics when issues occur. Finally, Data Connection supports outbound patterns such as dataset exports, outbound Webhooks for triggering external workflows from Foundry applications, and inbound event listeners (beta) for low-latency event feeds from systems that cannot directly integrate with standard Foundry APIs. This combination makes Foundry a bidirectional integration layer that not only brings data in, but also operationalizes insights by pushing results back into the enterprise landscape.

5.Palantir's HyperAuto and SAP

Palantir also extends integration beyond basic syncing through HyperAuto, which programmatically generates pipelines for ERP and CRM data, rapidly normalizing and harmonizing it into curated datasets that can feed the Ontology and power downstream analytics, workflows, and AI use cases. HyperAuto is Palantir’s source-to-value automation layer designed to accelerate how enterprise data is extracted, standardized, and made usable in Foundry and the Ontology. HyperAuto V1 (also known as SDDI or Bellhop) was the first generation of this capability and is still supported for several source types such as Salesforce, Oracle NetSuite, and SAP, although SAP customers are strongly advised to use HyperAuto V2, which is specifically released and optimized for SAP.

Architecturally, HyperAuto automates the journey from raw source systems to curated, Ontology-ready data using three building blocks: secure connectors for high-scale data transfer, guided source exploration for rapid discovery and bulk sync creation, and automatic pipeline generation that transforms raw extracts into curated Foundry datasets and Ontology object types. HyperAuto analyzes ERP metadata to generate robust, extensible pipelines that denormalize and reconstruct business objects through automatically created transformations, so that the teams do not need deep expertise in proprietary ERP schemas. The generated pipelines typically include source-specific preprocessing, standardized cleaning libraries, core enrichment steps such as renaming and de-duplication, and derived elements like join tables and time-series datasets. HyperAuto can also generate the Ontology automatically, enabling users to immediately search, analyze, and build operational applications on top of SAP and non-SAP data.

In addition, HyperAuto can publish the integrated and curated data to external cloud platforms such as Google BigQuery, Snowflake, AWS Redshift, and others, or push it into the Foundry Ontology in just a few clicks, where data is organized into easy-to-understand semantic objects so business users can self-serve and make operational decisions.

For more information refer to URL https://www.palantir.com/docs/foundry/hyperauto/v1-getting-started/

6.Connecting SAP and Palantir

Palantir uses the Palantir Foundry Connector 2.0 for SAP Applications, which is:

An SAP-certified ABAP add-on
Installed in SAP using SAINT
Exposes SAP data through HTTPS (SICF services)
Works via the SAP application layer (not direct DB access

Foundry defines the extraction logic (tables, filters, schedules), and the sync jobs are executed by Foundry’s Data Connection services. SAP security remains SAP security, because Foundry calls SAP using an SAP user with SAP authorizations and is DB agnostic as reads happens from SAP application layer rather than DB layer.

Supported SAP extraction objects include:

SAP application tables
CDS views
BW InfoProviders / BEx queries
Extractors (ODP-enabled)
SLT (ODQ queues)
Function modules/BAPIs

Installation steps in SAP (At high level):

Components:

SAP Add-on (ABAP) installed using SAINT
Foundry Agent installed in customer network and managed via Foundry Data Connection

SAP Prerequisites:

The source must be SAP NW 7.4 SP5 or 7.5 systems. If the source is below 7.4 SP5 a remote agent pattern required.

Generally supported deployment patterns for data extraction are:

Direct to SAP ECC/S/4/BW
Via SAP SLT Replication Server (ODQ queues / CDC)
Remote via Gateway (RFC based)

At SAP level:

For Palantir add-on installation follow below steps:

Login to client 000
Run SAINT
Load package: FOUNDRY-SAPCONN-INST-SP00SPXX.SAR
Select components: PALANTIR and PALCONN
Execute install (dialog for preparation, background for remaining phases)
Run Post installation wizard through t-code /n/PALANTIR/POST_INST . It will automatically below tasks.

Activate SICF services
Generate roles and assign to Foundry SAP user
Maintain default parameters (/n/PALANTIR/PARAM)
Check ICM settings and generate test URLs
Health checks (connector, authorization, SLT, RFC, jobs)

At Palantir Foundry level:

In Foundry Data Connection, create SAP source using YAML:

type: magritte-sap-source
url: https://<host>:<port>/sap/palantir
usernamePassword: <username>:{{password}}

Followed by this, sync between source and Foundry must be configured. Create syncs for required object types (tables/CDS/BEx/SLT/functions). Example:

type: magritte-sap-source-adapter
sapType: tcode
obj: ZTEST_ALV

(For more information please refer https://www.palantir.com/docs/foundry/sap/install-sap)

7.Sample demo screen shots of integrating SAP data with Palantir's HyperAuto to extract SAP ECC data into a S3 bucket

Source: https://blog.palantir.com/how-anyone-can-integrate-sap-data-in-hours-17f23d0bd2e8

NOTE: I have only provided the screen shots with high level explanation so that the readers could get a look and feel of how SAP data gets extracted into Palantir. I would strongly recommend the interested readers to read it in depth at above blog for complete understanding

Below are the high-level steps involved:

The upcoming steps and screenshots demonstrates how HyperAuto accelerates SAP integration by allowing us to explore SAP tables visually, select only what we need, and automatically generate ingestion syncs and transformation pipelines. In this example, a data engineer at a manufacturing firm builds a supply chain view by ingesting SAP data, cleaning and integrating it in Foundry, and exporting the curated datasets to an S3 bucket.

Connect SAP to Foundry using the official SAP-certified connector and ensure the SAP source is available in the Data Connection app.

Step 1-2:

a. Access the SAP source in Data Connection: In Data Connection, locate your SAP source (typically listed with type magritte-sap-source) and open it.

b. Explore SAP data in HyperAuto: Launch Explore and create syncs. HyperAuto reads SAP tables on-demand (including custom Z tables) and provides a “shopping cart” style selection flow. This approach helps avoid unnecessary heavy queries and reduces risk of disruption to the ERP system.

Step 3:

a. Start with a pre-packaged workflow: From the Workflows tab, select a pre-built workflow such as Supply Chain Disruption. HyperAuto automatically adds the required SAP tables and relationships needed for the workflow and prepares logic for the use case.

b. Add extra tables (customization): Extend the workflow by selecting additional tables. Example: add MARM (Units of Measure for Material) from the Modules tab.

Step 4-5:

a:Discover relationships without knowing SAP schema: HyperAuto helps explore linked tables visually. It also translates technical SAP table/field names into human-readable descriptions (like an automatic data dictionary). Use table relations to discover and add missing dependencies (example: add the Clients table related to Material Descriptions).

b: Configure syncs: Review the selected tables and configure ingestion settings. Choose ingestion strategies such as:

Snapshot (full extract)
Append / incremental (delta ingestion)

Step 6-10:

a.Then confirm by selecting Ingest & Integrate Data → Create syncs.

b.Auto-generate pipeline code (PySpark): HyperAuto generates:

ingestion sync configuration
transformation pipelines
PySpark code for cleaning and integration

All code is version-controlled via Git and created through a pull request workflow, enabling review and controlled deployment.

Step 12-15:

a.Run raw ingests: Using the SDDI/HyperAuto flow, run the syncs to ingest raw SAP data and supporting metadata.

b.Review pipeline lineage: Open the pipeline graph in Data Lineage to visualize dependencies between raw datasets and transformed outputs.

Step 16-18:

a.Build the cleaning and integration pipeline: Select all datasets and trigger a build. HyperAuto runs the generated transformations to produce clean, denormalized, analytics-ready datasets.

b.Validate the final datasets:Preview the cleaned datasets and review the transformation logic that produced them.

Step 19-21:

a.Create an S3 data connection source

In Data Connection, create a new S3 source, select an agent for the export, and configure bucket details. Save it in a project folder with correct permission.

Step 22-29:

a.Configure an export task: Create a Data Connection Task of type export-s3-task, define the output path, and add the finalized datasets as inputs. Configure outputs for tracking export results.

b.Run the export (or schedule it):Build the export task to push curated datasets to S3. This can later be automated on a schedule or triggered by upstream dataset updates.

At the end of the workflow, we will have:

SAP data ingested and integrated using HyperAuto
Cleaned, denormalized datasets ready for analytics
Data exported to S3 (or optionally used directly in Foundry tools)

This creates an extensible data foundation that can be enriched with other enterprise systems and advanced data types (time-series, geospatial, etc.) for broader operational intelligence.

8.Palantir’s SAP migration narrative

Palantir positions SAP migrations (especially ECC to S/4HANA) as slow and expensive largely because teams work in silos. Extraction, transformation, validation, business SMEs, and compliance often operate independently. When validation fails, organizations can lose weeks doing “data archaeology” to identify root causes and coordinate fixes across teams.

Palantir’s claim is that AIP shifts migration from a linear, phase-by-phase process into a context-connected, end-to-end workflow where data understanding, mapping, transformation, and validation are linked. Validation becomes continuous rather than a final gate. SMEs can describe fixes in plain language, and AI propagates those corrections through the pipeline quickly. This is often explained using the “octopus” analogy, meaning multiple specialized AI capabilities coordinated by a single contextual brain that understands legacy structures, target requirements, business rules, and compliance.

It claims to complete an ECC to S/4 HANA conversion within 2 weeks of time.

9.Palantir’s position on testing

In practical terms, Palantir claims that when testing fails, we do not lose weeks coordinating across teams. The platform preserves end-to-end context such as source, mapping logic, transformation, business rule, and target expectation. This helps identify the root cause faster. Fixes can then be applied by SMEs and automatically propagated through the pipeline.

Palantir’s testing model is central to its positioning:

Testing is not a separate phase at the end.
Validation is continuous and embedded throughout the migration pipeline.
Data quality, mapping correctness, and compliance checks update continuously as transformations run.

10.My point of view on SAP migration and Palantir usage:

Based on my understanding, I believe Palantir might be of use for selective data migration for SAP environment to certain extent than for typical brownfield.

a. Why this aligns more with SDT than brownfield

Palantir's strengths are largely aligned to a Selective Data Transition (SDT) or data transformation-led migration model, where complexity is dominated by:

data harmonization
mapping rules
transformation logic
repeated reconciliation and validation cycles

In a true brownfield conversion, the core work is fundamentally different. The focus is on:

system conversion using SUM/DMO
technical readiness and remediation
custom code adaptation
integration compatibility
downtime optimization
controlled cutover execution

Since brownfield conversions typically do not involve large-scale remapping, consolidation, or business-unit redefinition of data, Palantir’s key value propositions (continuous transformation, SME-driven mapping, and AI-based validation loops) are not the primary effort drivers.

b. Where Palantir could be strongest

Palantir’s value proposition could becomes far more credible in SDT scenarios, where organizations are not just converting ECC technically but actively reshaping their data, organizational structures, and business processes while moving to S/4HANA.

In these programs, migration is not a single system conversion event. It is an extended transformation journey where enterprises often:

run legacy and target environments in parallel
migrate in waves
consolidate business units
remap master data
continuously validate outcomes

In this context, Palantir’s narrative around contextual awareness, continuous validation, and SME-driven rule definition aligns with real SDT challenges.

c. Summary

Palantir is a non-SAP platform (Foundry and AIP) focused on integration, semantic modeling (Ontology), analytics, and AI-driven operational workflows.
It prepares S/4-ready datasets outside SAP using governed pipelines for mapping, transformation, and continuous validation.
It is a DB agnostic tool as the data gets extracted / read from SAP application layer rather than from the underlying DB.
It is not a replacement for SAP-native brownfield conversion tooling like SUM/DMO.
SUM DMO brownfield conversions are much simpler and straightforward .
Palantir needs relatively deeper functional knowledge when compared to assisted selective data migration using SNP or other similar tools.
The often-cited claim of completing a migration in two weeks typically does not represent the full end-to-end SAP migration timeline. In reality, a standard SAP migration (Bluefield or Brownfield) following the SAP Activate methodology includes several critical phases such as unit testing, functional testing, system integration testing (SIT), mock runs, and cutover rehearsals, all of which significantly extend the overall timeline.
Palantir is an intelligent platform for data integration, analytics, and AI-driven decision support, rather than a typical SAP migration tool .
It can significantly accelerate SDT or bluefield-style (to certain extent) programs by:

reducing discovery time
enabling AI-assisted mapping
minimizing reconciliation cycles through continuous, context-aware testing

11. Sample use cases Palantir highlights

Palantir also positions its architecture as delivering value beyond migration by enabling enterprises to build operational applications mid-stream. For example, a customer returns processing app can be built during migration with Palantir with below steps:

It pulls customer data from ECC.
It enriches it with intelligence from other integrated systems.
It writes validated transactions into S/4HANA.
This is coordinated through Palantir’s unified data layer (Ontology).

Palantir also highlights the flexibility of deploying AI in ways that augment human expertise rather than replacing it, with appropriate levels of automation based on task complexity and risk tolerance. For example, with Palantir AIP a procurement lead could type, “for New London products, swap overseas suppliers for the highest-volume domestic alternative before S/4 upload.” The pipeline reexecutes, validation metrics update, and the change is implemented without filing a ticket, scheduling a sprint, or involving external consultants.

This supports the idea of migration as business continuity plus continuous improvement rather than a disruptive cutover, and again fits more naturally with SDT than pure brownfield conversion.

12.Relationship between Palantir and SAP Business Data Cloud (BDC)

Palantir and SAP Business Data Cloud (BDC) are connected through a strategic partnership intended to combine SAP’s governed, zero-copy business data foundation with Palantir Foundry’s Ontology and AIP.

The joint positioning is:

SAP BDC provides a trusted SAP semantic layer and harmonized data products.
Palantir extends this into an enterprise-wide ontology that fuses SAP and non-SAP data.
Together they enable AI-driven operational intelligence, simulation, and execution.

13. Palantir and archive data access

Palantir can support archive data access by acting as a unified governed layer that pulls data from SAP archives and legacy sources and exposes it in a searchable, analytics-ready way. This is useful for reporting, audit, and compliance after ECC decommissioning.

However:

Palantir does not replace SAP’s archiving mechanism itself.
The value lies in consuming archived and legacy data through connectors, blending it with non-SAP sources, and organizing it in the Ontology so users can query it like live business entities.

Hope you enjoyed reading!

Do not forget to click on LIKE if you enjoyed this blog!

Comprehensive Overview: Analyze ESG Data Application in SAP Sustainability Control Tower

2026-02-17T14:51:37.820000+01:00

Disclaimer: This blog post reflects the state of the SAP Sustainability Control Tower as of February 17th, 2026.

The Analyze ESG Data application in SAP Sustainability Control Tower (SCT) has evolved into a central workspace for exploring and reporting sustainability data. Recent enhancements – such as configurable drilldown dimensions, public analytics configurations, extended period selection, and flexible export options – make it easier than ever for users to navigate ESG metrics, create consistent views, and share insights across teams. This overview summarizes the key capabilities that support faster, clearer, and more collaborative sustainability reporting and provides a practical, user-focused supplement to the SAP Help Portal documentation.

Getting Started: “My Views” & Filters

The application requires users to select a reporting period and structure before displaying data, ensuring relevant and targeted analysis:

Once you have selected the relevant Period and Reporting Structure you can also save this using the “My Views” functionality and set it as default. These views are only visible to you, and don't affect other users.

Manage Overviews: Customization and Collaboration

The “Overview” dashboard organizes metrics into groups, which are defined in the “Manage Overviews” section. Users can switch between public and private overviews, edit groupings, and add or remove metrics to tailor the dashboard to their needs.
Public overviews allow teams to share standardized metric groupings, while private overviews support individual customization.
Metrics can be reordered within groups for better visibility, and the system provides clear feedback when no data matches the selected filters.

Analytics: Deep Dive, Drill-Downs, and Shared Views

Users can access the “Analytics” tab by selecting a metric for a prefilled view or starting from scratch via the analytics tab, supporting both guided and exploratory analysis.

Option 1: Selecting a metric from the “Overview” page:

Option 2: Select the metric within “Analytics” tab

Independent whether you clicked on the metric from the “Overview” tab or you selected it directly within the “Analytics” tab, the metric is visualized with a grouping by “Period” per default (if nothing else is applied).
Public and private analytics views allow admins and data managers to predefine drill-down configurations for each(!) metric, which can be set as defaults for all users. This ensures consistency in reporting and facilitates knowledge sharing across teams.

The “Manage Analytics” section provides a centralized location to view and edit all saved analytics configurations, with public views editable by authorized users. Any public view can be edited by anyone with the required authorizations.

Advanced Filtering, Drill-Downs, and Export

Users can filter and drill down by multiple dimensions, such as period, reporting structure, and energy source type, with up to five drill-downs per metric. This flexibility supports detailed analysis and custom reporting.

Within the “View Settings” you can also create additional filters by “Period”, “Reporting Structure” or “Drill-down Dimension”. Please note that filters within “View Settings” overwrite settings in “My Views” if applied. For filtering within “View Setting” there is a current limit of 12 nodes for multi-selection in hierarchies, set for performance and visualization reasons. Depending on customer feedback this limit might be expanded.

If filtering is applied via the “View Settings” and “My Views” is overwritten, this is also highlighted as an info:

Export functionality enables users to download chart data as PNG images and tables as Excel files, making it easy to share insights and integrate with other reporting tools.

Drill-downs for each metric can be set up in the “Manage Metrics” application for up to 5 drill-down dimensions. Select the relevant drill-down dimension(s) and by clicking on saving, this directly calculates the drill downs. Once it has been calculated and saved, the drill-downs can then directly be visualized in the “Analytics” tab of the Analyze ESG Data application

Upcoming Features & Further Resources

Key priorities for upcoming releases include displaying targets within the “Analytics” tab, enabling multi-selection in the global filter for reporting structures, support for more chart types with saved custom chart views and saving “Analytics” settings for the whole page across multiple charts further enhancing flexibility and usability.
If you are driving a project with SCT, please upvote or submit improvement requests via SAP Influence to ensure that future updates align with real-world needs and use cases.

For further details, consult the SAP Help Portal and the SAP Roadmap for SCT:
- Using the Analyze ESG Data Application | SAP Help Portal
- SAP SCT Roadmap
- Don’t forget to subscribe the What’s New section within SAP Help:
  What's New in SAP Sustainability Control Tower | SAP Help Portal