In this post, I’m going to cover logical data management and its impact on data mesh architectures. But there’s a lot of confusion in the marketplace today between different types of architectures, specifically data mesh and data fabric, so I’ll begin by explaining the difference between these two.
The Core Idea of a Data Mesh
Data mesh was designed around the understanding that different functional domains, or business domains, own and understand different data sets. In a data mesh, each of these domains, or “data domains,” are responsible for creating their own “data products.” The data mesh architecture provides a controlled, efficient way to share these data products across the organization.
A logical approach to data management bolsters this process. It involves creating a data abstraction layer above the underlying data sources, facilitating the formulation of canonical, business-centric views around each data product. These refined views can then be exposed as a data service via an API, ensuring equal access to enterprise data.
The Role of Data Fabric
Although data fabric might sound similar to data mesh, it diverges in its foundational principles. Unlike a data mesh, which is based on domain-based data products, a data fabric focuses on data ingestion, data transformation, data quality, and data delivery. These capabilities are interrelated and built on top of the underlying data. In the context of a physical data fabric, these capabilities use independent tools. In contrast, a logical data fabric leverages all these capabilities to establish a comprehensive, powerful infrastructure.
Understanding these nuances can aid businesses in choosing the right approach for their data management needs.
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