SAP Data Foundations for Non-SAP Data Professionals

Learn how SAP data works how to design trusted SAP data pipelines for Databricks, Fabric, Snowflake, and analytics
What you'll learn

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Understand the SAP data landscape from the perspective of a modern data professional
Identify which SAP systems own Finance, HR, Procurement, Sales, CRM, Expense, and Workforce data
Explain why raw SAP table extraction often leads to incorrect reporting
Understand key SAP extraction patterns including CDS Views, OData, CDC, BW connectors, Datasphere models, and BDC data products
Recognize common SAP data project risks involving hierarchies, master data, delta loads, and lost semantics
Understand the role of SAP Datasphere and SAP Business Data Cloud in modern data architectures
Connect SAP data concepts to Databricks, Microsoft Fabric, Snowflake, and SAP Analytics Cloud use cases
Ask better questions when working with SAP teams and business stakeholders
Requirements

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No prior SAP knowledge is required.
Basic understanding of data, analytics, or business intelligence concepts will be helpful.
Familiarity with tables, APIs, data pipelines, data warehouses, or lakehouse platforms is useful but not mandatory.
Experience with platforms such as Databricks, Microsoft Fabric, Snowflake, Power BI, or similar tools is helpful.
You do not need access to an SAP system, and you do not need any experience with SAP GUI, ABAP development, SAP configuration, or SAP functional modules.
A willingness to learn how enterprise SAP data works conceptually is all you need.
Description

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SAP data projects can feel daunting, even for experienced professionals who work comfortably with Databricks, Microsoft Fabric, Snowflake, SQL, lakehouse architectures, and modern analytics platforms.
A client asks for Finance, HR, Procurement, or Workforce Cost reporting from SAP. You know how to build pipelines, models, dashboards, and AI-ready data platforms, yet the SAP landscape can still feel difficult to navigate.
Where does the data live?
Which system owns each business process?
Why do raw table extracts produce figures that do not reconcile with SAP reports?
How do CDS Views, ODP, SAP Datasphere, SAP Business Data Cloud, BSEG, ACDOCA, SuccessFactors, Ariba, Fieldglass, and Delta Sharing fit together?
Most importantly, how can you work confidently with SAP data without becoming a full SAP consultant?
This course gives youa clear, practicalframework for answering those questions.
You will learn how data is organised across SAP S/4HANA, ECC, SuccessFactors, Ariba, Concur, Fieldglass, Sales Cloud, Service Cloud, SAP BW, SAP Datasphere, and SAP Business Data Cloud. You will see why SAP data behaves differently from ordinary database or API data, why business semantics matter, and why extracting raw tables can lead to unreliable reporting.
This is not a traditional SAP functional course. It does not cover configuration, SAP GUI transactions, or module-specific implementation. No prior SAP knowledge is assumed.
Instead, it is built for modern data professionals who need to design, deliver, troubleshoot, or advise on SAP data pipelines and analytical solutions.
By the end, you will be able to navigate the SAP data landscape with greater confidence, ask sharper questions, choose more suitable extraction patterns, avoid costly design mistakes, and work more effectively with SAP functional and technical teams.
WhatYou WillLearn
By the end of this course, you will be able to
· Explain why SAP data differs from ordinary application, database, API, and file-based data
· Distinguish between SAP S/4HANA, ECC, SuccessFactors, Ariba, Concur, Fieldglass, Sales Cloud, Service Cloud, and SAP BW
· Identify the systems that typically own Finance, HR, Procurement, Sales, Inventory, Workforce, Expense, and CRM data
· Recognise why raw SAP table extraction can produce incorrect business results
· Account for business semantics, hierarchies, master data, authorisation structures, and time-dependent data
· Compare key extraction patterns, including CDS Views, ODP, CDC, OData, CDI, SAP BW connectors, SAP Datasphere models, and SAP Business Data Cloud data products
· Describe the role of SAP Datasphere in integration, modelling, semantics, and governance
· Explain how SAP Business Data Cloud provides governed, harmonised data products while preserving business context
· Understand how SAP data can be consumed in Databricks, Microsoft Fabric, Snowflake, and SAP Analytics Cloud
· Avoid common mistakes involving BSEG, ACDOCA, hierarchies, delta loads, master data, and incomplete source-system mapping
· Ask more effective questions when working with SAP functional teams, SAP Basis teams, BI teams, and business stakeholders
· Design more dependable SAP data pipelines for reporting, analytics, and AI
Who this course is for

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Data professionals who need to work with SAP data but do not come from an SAP background.
Non-SAP professionals who need to collaborate more effectively with SAP functional, technical, Basis, or BI teams
Technical project managers and solution architects who need to understand SAP data risks, terminology, and integration patterns
Databricks consultants who need to ingest, model, analyze, or share SAP data
Microsoft Fabric consultants working on SAP-connected analytics or lakehouse projects
Snowflake consultants designing SAP data ingestion, sharing, or warehouse solutions
Data engineers who need to build SAP data pipelines
BI developers and report developers working with SAP Finance, HR, Procurement, Sales, Inventory, or Workforce data
Data architects designing modern data platforms that include SAP source systems
Homepage

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