How to juggle Data Mesh and Data Fabric to enable data-driven actions and business decisions?
Modern applications for Big Data confront us with new challenges, usually related to where data resides, how it can be used and who could benefit from its use.
In modern data architectures approaches, Data Mesh and Data Fabric stand out among the others. These approaches can be defined as frameworks or designs that help us facing these new challenges. Due to their abstract nature, they are not strictly related or defined by a particular product, technology or industry, but can assume various forms depending on their interpretation and the use-case.
Data Fabric is the architectural framework able to ensure a unique and unified view of different services and technologies by giving a simplified access to organizational data and by making information available in the right way, in the right time and to the correct user.
At a technological level, Data Fabric is composed by a stack of services between the data source and consumer, integrated by different processes related to the data lifecycle which can be divided in different layers.
This approach can ensure multiple benefits:
business users are enabled to take decisions and actions with a data-driven approach, making the experience faster and more personalized;
data management can benefit of automated and less expensive operational activities on data lifecycle;
from an organizational perspective, it reduces the gap between data experts and the business side.
Data Mesh is an architectural framework based on the concept of “domain”, where data is treated as a product and maintained by the team that has the functional knowledge of the information. A domain can be seen as a high-level category associated to a specific business function – i.e.not systems or applications.
Every domain is defined by its own internal process and pipelines running on a common infrastructure, and it is characterized by the data it exposes and by the actions that can be performed on it.
This approach can also benefit different areas:
at a business level, it enables data democratization with a self-service approach;
it helps data management in simplifying the way data can be retrieved;
inside the organization, it allows for faster data-exchange between producers and consumers.
While there is no general rule to define which approach to use when choosing between Data Mesh and Data Fabric, there are some cases in which one of the two frameworks could be a better option than the other. We, at Reply, can support you in choosing the best framework for your needs. For instance, when a Data Lake turns into a Data Swamp, it is usually due to a lack of organization, governance, and accessibility. In this case, moving towards a Data Mesh approach could help to keep data organized and usable.
On the other hand, a Data Fabric approach comes to hand when the automation of many tasks of the product lifecycle is needed. However, Data Mesh and Data Fabric are not mutually exclusive approaches. In fact, there are certain cases in which both frameworks can be a suitable option, In this cases, a mixed architecture between the two would be the most suitable choice.