Data Architecture with SAP — Data Fabric

Defining Data Fabric

While the term “Fabric” can be understood as “the structure of something; the parts of something that hold it together and make it what it is”, the term “Data Fabric” have some variants and changed over time. I would like to approach the term Data Fabric using different definitions.

Fig. 1: Tweet about Data Fabric, Source: Twitter, 2022

Data Fabric Architecture

There are different perspectives, which components are included in a Data Fabric architecture and how they should be composed. Unlike a Data Warehouse or a Data Lake, technologies and concepts are not as mature and defined here. Therefore a lot of individual vendor-specific implementations can be found and finally a data fabric don’t even need to be build on components of one single vendor even if a thight integration and a unified user experience would be prefered. Gartner and Forrester defined reference architectures how a Data Fabric should look like.

Fig. 2: Elements of a Data Fabric
Fig. 3: Generic Data Fabric Reference

Data Fabric with SAP

SAP itself started in 2014 propagating SAP HANA Smart Data Access (SDA) as a functionality for In-Memory Data Fabric together with BW 7.4. Today I see a more differentiated view while SAP communicates the term “Fabric Virtual Table” as a extended approach to SDA, able to virtualize or replicate as needed in the sense of “HANA Cloud is a data fabric that virtualizes others in the cloud so you can access. By default, not replicate data, just virtualize. Unified access layer via SQL to remote sources supported”, as Tammy Powlas commented from an SAP webinar.

  • Integrate any data — solve the data deluge and integrate any kind of data
  • Ensure data quality — Discover, prepare, and govern all your data assets in the same tool
  • Democratize data — through self-service data preparation and automation capabilities
  • Any cloud or on-premise — deploy on any mix of hyperscalers, hybrid, or on-premise
  • Reuse any engine — orchestrate any SAP or non-SAP data processing engine
Fig. 4: Coverage of SAP technologies for Enterprise Data Fabric over the years
Fig. 5: Cloud-based SAP Data Fabric
Fig. 6: Data consumption from Data Fabric
  • Spaces — deliver a selfe-serve infrastructure to handle data management more decentralized and business-oriented
  • Data Sharing and Data Marketplace — enables to share and integrate external data and internal Data from other Spaces
  • Prebuild business content — make use of SAPs experience in integrating SAP sources and build analytical content
  • Business Modeling — the Business Layer enables non-technical users to focus on KPIs and re-use of business logic based on a semantic layer

Conclusion

Be aware, Data Fabric makes sense with a mindset of data democracy and a good data culture in the company. It should not be seen as introducing SAP HANA Cloud or SAP Data Warehouse Cloud automatically means having a Data Fabric. It means that this technology is organized as the single access point for the whole organization. Driving data quality, establishing high data security standards and a good data governance.

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Peter Baumann

Peter Baumann

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As a Consultant for Data & Analytics Strategy I help my customers with topics around Data Strategy. Opinions reflect my personal view. I work @INFOMOTION