Data Mesh: Fundamentals

In this training, we will explain what the four principles of Data Mesh mean. You will become familiar with the challenges of implementing Data Mesh and receive recommendations for a gradual approach. Together, we will design a data product, the central element in a Data Mesh, using our Data Product Canvas and explore implementation alternatives. By the end of the workshop, you will be able to assess the sociotechnical implications of Data Mesh and design data products.

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Your benefits

  • Learn the difference between operational and analytical data.
  • Get to know the key Data Mesh principles such as "Domain Ownership," "Data as a Product," "Self-serve Data Platform," and "Federated Computational Governance."
  • Learn how to design a data product.
  • Understand the interaction between multiple data products within a Data Mesh.
  • Discover the importance of sociotechnical aspects within a Data Mesh.

The Data Mesh concept is based on domain-oriented, decentralized data architectures that enable development teams to perform data analysis autonomously. Data Mesh is a socio-technical data architecture and is presented in the form of the following four principles:

The “Domain Ownership” principle assumes that domain teams take responsibility for their data. According to this principle, analytical data should be structured into domains, similar to team boundaries, which correspond to bounded contexts. Responsibility for analytical and operational data is transferred from the central data team to the domain teams.

The “Data as a Product” principle applies the philosophy of product thinking to analytical data. This principle means that there are data consumers beyond the domain. The domain team is responsible for satisfying the needs of other domains by providing high quality data as data products. In essence, domain data should be treated like any other public API.

The third principle is to apply the idea of “Platform Thinking” to the data infrastructure. A dedicated data platform team provides domain-agnostic functions, tools, and systems for creating and consuming interoperable data products across all domains.

The “Federated Computational Governance” principle represents enterprise wide processes for data governance. With this principle, interoperability of all data products is achieved through standardization defined by the Governance Guild. The main goal is to ensure compliance with organizational rules and industry standards.


  • The motivation for Data Mesh. What are typical problems in Data Engineering that lead to the decentralization of data architectures?
  • When is Data Mesh the right approach?
  • The "Domain Ownership" principle
  • The "Data as a Product" principle
  • The "Self-serve Data Platform" principle
  • The "Federated Computational Governance" principle
  • Designing a data product


Software Architects, Data Experts

Training Objectives

  • Understand Data Mesh concepts for decentralized data architectures
  • Understand the four Data Mesh principles
  • Learn the design and implementation of data products
  • Be able to define technical and sociotechnical components for Data Mesh


By the end of this two-day workshop, you will have gained a deep understanding of the fundamental principles of Data Mesh and be able to assess the socio-technical implications of Data Mesh and design data products.