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Modern Transformation Technologies in SAP S/4HANA Data Migrations

  • andreashalusa5
  • May 15
  • 3 min read

What It's About



SAP S/4HANA migrations require modern data strategies




Highly scalable ETL processes with Spark, PySpark & Object Stores




Object-based data migration increases efficiency and transparency



Companies that use SAP R/3 (ECC 6.0) as their ERP system are facing a significant technological leap: maintenance for SAP R/3 is only guaranteed for a few more years. The transformation to SAP S/4HANA is therefore not optional, but mandatory - and currently one of the most strategically important IT projects in many organizations.


Data Migration: A Critical Success Factor for SAP S/4HANA Transformations

The conversion from SAP R/3 to S/4HANA entails far-reaching changes - technically, procedurally and organizationally. Many companies are not opting for a pure system conversion (“brownfield”), but for a greenfield approach in which processes are redesigned and legacy issues such as outdated databases are cleaned up. At the same time, there are also numerous new implementations of SAP S/4HANA, in which previous non-SAP systems are replaced.


At the heart of all these scenarios is one topic that is particularly critical: data migration.


The available SAP tools, such as the SAP Migration Cockpit, primarily support data import - i.e. loading structured data objects into the target system. However, the upstream steps of data extraction and transformation must be solved on a customer-specific basis.



From Tables to Objects: A New Perspective on SAP Data Migration

Traditionally, data migration in SAP projects is table-based. However, this approach has numerous disadvantages:


  • technical complexity

  • time-consuming data merges

  • Difficult quality assurance


This raises a key question:


Can data migration also be object-based - instead of table-based?


The answer is yes. With modern technologies from the field of data engineering, data flows can be designed at object level - more efficiently, more flexibly and more transparently



A Modern ETL Approach with Spark, PySpark, and Object Stores

In recent projects, a modern, object-based ETL process has proven successful, built around the following components:


  1. Data extraction: The source data is extracted - ideally already object-oriented. Alternatively, flat table structures (e.g. from CSV files or relational databases) are transformed into a hierarchical object structure.


  2. Transformation Central field and value mapping forms the basis for a consistent data structure. A central repository (database or flat files) enables cross-object reusability of the mapping rules.


  3. Data Loading The transformed objects are transferred to the target system via the SAP Migration Cockpit. Proven function modules/BAPIs are used for this, which are also used manually in SAP transactions.


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Key Benefits of Object-Based Data Migration with Spark and Object Stores


  • Full Traceability & Quality Assurance Object structures remain intact throughout the ETL pipeline, allowing both IT and business teams to review and validate data transparently – turning the process from a "black box" into a "white box".


  • Scalable Data Processing with Apache Spark Using PySpark, large data volumes can be processed with high performance. Additional capabilities such as machine learning (e.g., for duplicate detection or enrichment) can be seamlessly integrated.


  • Modern DevOps Integration With GitHub for version control and Docker for containerized deployments, migration solutions can be efficiently rolled out – from small-scale projects to global SAP implementations.


  • Efficient Storage with Object Stores

    Hierarchical data structures can be temporarily stored in Object Stores, significantly reducing the need for complex database joins and improving performance.


Conclusion: Future-Proof SAP S/4HANA Data Migration

The migration to SAP S/4HANA is a complex undertaking - but modern approaches at object level offer a clear path to greater transparency, quality and scalability. The targeted use of Spark, object stores and containerized ETL processes not only enables technical change, but also a sustainable data architecture for the future.


dataXcellence accompanies you on this path with comprehensive expertise in data engineering, SAP data migration and ETL automation - from analysis to implementation.

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