Piethein Strengholt, author of Data Management at Scale, recently published an article presenting six data-mesh governance topologies and domain granularity. Each topology adapts the data mesh strategy to balance requirements like data ownership, organization structure, pace of change, technology, and others.
Data mesh is an enterprise data architecture that adapts and applies the learnings in building distributed architectures to the domain of data. Data mesh recommends creating self-serve data infrastructure, treating data as a product, and organizing teams and architecture based on business domains.
According to Strengholt, organizations transitioning to data mesh must make trade-offs. Depending on their context, they could value quality over control, simple over complex structures, etc. Organizations must adapt the data mesh concept to implement what works best for them today and use one of the following topologies as a starting point:
Source: https://towardsdatascience.com/data-mesh-topologies-and-domain-granularity-65290a4ebb90
Applying the purest theoretical form of data mesh looks ideal, but it is hard to achieve because it requires standardization and managing concerns about capability duplication, network access, etc. Strengholt named this implementation a fine-grained, fully federated mesh topology. It is often used in organizations born on the cloud, that are relatively young, and have highly skilled software engineers.
Organizations that value quality and compliance over agility can implement the fine-grained and fully governed mesh topology because it adds a central distribution layer to address distribution concerns. In Strengholt's experience, many organizations, particularly financial institutions and governments, tend to use it. However, organizations with hard-to-maintain legacy systems or lacking highly skilled software engineers can implement the "hybrid federated mesh" topology where federation happens on the consuming side domains and centralization in the others.
Organizations in supply-chain management, product development, or transportation industries require a high level of specialization. The value-chain-aligned mesh topology addresses this scenario by grouping domains that work together. Each value chain will behave like a larger domain, and crossing its limits requires data products to adhere to centralized standards.
Large-scale organizations with complex architectures and many applications require multiple governance levels, alignments, and decompositions, Strengholt wrote. Implementing some data mesh will require more trade-offs. In the coarse-grained aligned mesh topology, domains hold many applications, and boundaries are set by mirroring business units or regional viewpoints. This topology contradicts a pure data-mesh implementation and introduces new risks like creating silos.
Finally, some large-scale organizations can implement the coarse-grained governed mesh to overcome complexity, peer-to-peer distribution, and interoperability deviation. This topology adds a central distribution layer and relaxes controls within larger boundaries.