Although data mesh and data fabric architectures address similar issues, they do differ quite dramatically in their approach.
A data fabric model relies on data centralization with metadata-driven policies defining data management and governance. In contrast, the data mesh model takes a decentralized approach by providing access to data with built-in governance for different groups.
At its core, a data fabric is a technology solution that addresses data management by linking together various data management tools. These tools may include data cataloging, security, governance, quality, lineage, master data management (MDM), and extract, transform and load (ETL/ELT) products.
The data fabric achieves this by physically connecting these tools together at the metadata layer. This allows each tool to know what data the other tools have, resulting in a more integrated and cohesive data management system.
Overall, the data fabric offers a way to streamline data management and improve the efficiency of data-related tasks. By creating a more connected and integrated system, businesses can benefit from improved data quality, better governance, and a more streamlined workflow.
Enterprise architects can choose to build multiple domain-specific data fabrics to create a mesh architecture but this approach, particularly in terms of relying on metadata, is becoming less and less popular.
Data Mesh is more about addressing a need for technology that allows for the sharing of analytics and analytical data, as well as data for AI and ML, among groups in different business domains. As data products are built on different technology stacks, there must be some form of interoperability to ensure that data can still be used for analytics and AI.
Moreover, governance and policy as code must be built into this mesh approach to ensure that data is trustworthy. Without governance, data can become unreliable, leading to a lack of trust among users.
By enabling data to be shared easily and securely, businesses can make better decisions, improve operational efficiency, and drive innovation. As such, investing in the right technology is crucial for businesses that want to thrive in the data-driven economy of today and tomorrow.
As more and more data pours into the enterprise a model of data collaboration is likely the next great leap of innovation. Utilizing characteristics of both data fabric and mesh models, data collaboration allows data to be liberated from its initial source, so that it can be easily productized to suit the needs of all and any groups. Data being liberated from application silos also allows for analytics and AI to be incorporated more easily and more economically. More on a data collaboration model can be found here.