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Author's profile photo Wolfgang Albert Epting

Data Mesh with SAP Business Technology Platform Part 2 – SAP HANA Cloud

Summary: In my first blog with the title: More than just a hype: Data Mesh as a new approach to increase agility in value creation from data I explained the four principles of Data Mesh at a high level and mapped some helpful capabilities of the SAP Unified Data & Analytics Portfolio. In the current and subsequent episodes, I will look at individual SAP products and explain in more detail which of their technical capabilities can support the introduction of a Data Mesh. Having started the series with SAP Data Warehouse Cloud in the previous blog, this time I will talk about SAP HANA Cloud and later on continue with SAP Analytics CloudSAP Data Intelligence Cloud and SAP Master Data Governance.

To understand this blog, it is at least necessary to have read my first blog or another respective publication to be familiar with the four principles of Data Mesh and to understand why centralized  monolithic data architectures suffer from some inherent problems.

SAP%20HANA%20Cloud%20-%20Build%20a%20trusted%20Data%20Foundation

SAP HANA Cloud – Build a trusted Data Foundation

SAP HANA Cloud is a single database as a service (DBaaS) foundation for modern applications and analytics across all enterprise data and the cloud-based data foundation for SAP Business Technology Platform. Several of its functionalities predestine SAP HANA Cloud to be used in Data Mesh scenarios. In the following, I will explain the three, from my point of view, most relevant:

Applying Domain-Driven Design strategies to data:

“Each domain can model its data according to its context, share this data and its models with others, and identify how one model can relate and map to others.” This statement comes verbatim from the book “Data Mesh – Delivering Data-Driven Value at Scale” by Zhamak Deghani and expresses that the principles of Domain-Driven Design1) used in software development are now also pentrating the analytical data space. Going into more detail about this concept at this point would go beyond the scope of this article. If you want to know more about adapting it to the world of data and analytics I recommend to read this article from Martin Fowler.

At this point it is of higher importance to derive the requirements towards technology to enable the domains to model its data as efficiently as possible? A data and technology platform must provide multi-models in order to cover the complexity of todays reality and enable smart processing on top. Multi-model data platforms represent the intersection of various data models such as JSON documents, graph networks, and relational tables in a single data platform. With a multi-model database, domains can unify various data types and models into a single solution, without having individual technologies for each specific purpose.

With SAP HANA and SAP HANA Cloud, we are a long-term player in this domain offering a comprehensive solution, infused with additional features like geospatial analysis, enterprise search, machine learning and predictive modelling. SAP HANA Cloud seamlessly blends smart multi-model data to power intelligent data products.

Forrester Research Inc., a leading global research and advisory firm, researched, analyzed and scored vendors and positioned SAP as a Leader in “The Forrester Wave™: Multimodel Data Platforms”.

Smart Multi Modelling:

Innovate without boundaries

Data Mesh needs Data Virtualization:

Data virtualization enables domains to quickly implement data products by working with virtual models on any data source. Transparency across data sources increases usabiltiy and data virtualization minimizes data replication, which makes the creation of data products significantly easier than with traditional alternatives. Iterating over multiple data product versions to fully meet all business needs is becoming much faster. Developing intelligent data products with embedded machine-learning capabilities becomes quick work in SAP HANA Cloud.

SAP HANA Cloud virtualizes data from full landscapes, including on-premise and cloud applications and third-party sources. SAP HANA Cloud Virtualization and Replication transfers data from source systems in the form of virtual tables which point towards supported remote sources (see SAP Note 2600176). Real Time accessibility allows the domain teams to visualize oprational data on the fly and to capture delta changes at the moment it is posted.

Real-Time Data Virtualization:

Federate%20on-premises%20and%20cloud%20data%20sources

Federate on-premises and cloud data sources

Shifting left security and compliance governance:

Through the idea of shifting security and compliance governance to the left, Data Mesh makes data more secure and compliant at speed and scale. What exactly does this statement mean? Rather than using insecure data and trying to address compliance requirements after the product is ready, domain teams can address them before they begin to develop. Compared to traditional paradigms, the Data Mesh principle “Data as a Product” reverses the model of responsibility to a certain extent. Unlike being a task of the centralized infrastructure team, Data Mesh moves this responsibilities closer to the source of the data. Security and privacy, as well as governance, are shifted to the left so to speak to become part of the day-to-day work for decentralized domain teams.

This increases efficiency, since decentralized teams know their data best due to their deep domain knowledge and therefore also know exactly how to govern and protect it. SAP HANA Cloud provides these teams with state-of-the-art methods such as k-anonymity, l-diversity and differential privacy to fully anonymize and effectively protect personally indentifiable information. These procedures are summarized as “Real-Time Data Anonymization” because they anonymize data in motion leaving the sources untouched. This means that different anonymization methods and strengths can be applied to different attributes, which makes it much easier to supply the domain teams with suitable, trustworthy data.

The anonymization is implemented in views over the real data, so that the combinatorics and nesting depth can be determined case by case. By creating anonymization reports, Chief Data Protection Officers can control at any time whether company’s compliance requirements are being met.

Real-Time Data Anonymization:

Innovate with confidence

Further information:

The author would like to thank Volker Häntjes for the collaboration on this topic and his contributions to this article.

1) Eric Evans, Domain-Driven Design: Tackling Complexity in the Heart of Software, (Upper Saddle River, NJ: Addison-Wesley, 2003).

Here the links to my complete blog series aboot Data MEsh with SAP Business Technology Platform:

If you have any further questions about SAP Business Technology Platform or specific elements of our platform, leave a question in SAP Community Q&A or visit our SAP Community topic page.

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      2 Comments
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      Author's profile photo Jeremy Collier
      Jeremy Collier

      I admire the valuable information you offer in your articles. Thanks for posting it..

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      Author's profile photo Wolfgang Albert Epting
      Wolfgang Albert Epting
      Blog Post Author

      Thank you very much for your feedback.

      Data Mesh is on everyone's lips at the moment and an important topic in order to effectively overcome the bottlenecks and weaknesses of central data provisioning.

       

      Kind regards

      Wolfgang Epting