Data Mesh vs. Data Fabric: A Tale of Two New Data Paradigms
Data is one of the most critical components of any business, as it allows us to personalize and customize our products for potential consumers. Yet, as important as data is, studies have shown that about 50‑70% of data collected by organizations goes unused and becomes what Gartner calls Dark Data. We can attribute this large amount of unused data to the inefficiencies in the systems that manage them.
This post discusses how methods like Data Meshes and Data Fabrics, which have emerged in the past decade, can help mitigate the problems associated with data management.
At the end of this post, you should understand what Data Meshes and Data Fabrics are, their differences, and why one may overtake the other.
According to IBM, a Data Mesh is a decentralized data architecture that organizes data by a specific business domain, providing more ownership to the producers of a given dataset. By decentralizing data, a Data Mesh offers an alternative to the central data lake and team culture that has been present in companies for decades. It is important to note that Data Meshes are language‑agnostic and technology‑agnostic as it is an approach that focuses more on organizational changes.