Data Mesh vs Data Fabric: What is Making the Best of Metadata?

Metadata is the data that provides information about one or more aspects of the data

Metadata is the data providing information about one or more aspects of the data. It is used to summarize basic information about data that can make tracking and working with specific data easier. Data fabric is a framework that is tech agnostic that can deliver data products as one of its many outputs and data mesh is an architecture that only produces data products that are specific to business domains. The virtually unlimited amount of data, processing capacity, and tools to leverage it are a modern deluge.

Metadata is the core of a data catalog. Both data fabric and data mesh won't be successful without an active metadata framework. Both data meshes and data fabrics have a seat at the big data table. Metadata fell out of favor due to its association with static data catalogs. A data catalog is a collection of metadata, combined with data management and search tools. The data catalog has become the new gold standard for metadata and a cornerstone of data curation. A data fabric and data mesh can co-exist. Data fabric is very technology-centric, and data mesh focuses on organizational changes.

Data mesh vs data fabric:

Data fabric and data mesh are emerging data management concepts that are meant to address the organizational change and complexities of understanding, governing, and working with enterprise data in a hybrid multi-cloud ecosystem.  Both data fabrics and data mesh can serve a broad array of business, technical and organizational purposes. Metadata is data about the data or documentation about the information which is required by the users. In data warehousing, metadata is one of the essential aspects.

Data fabric is essentially a metadata-driven way of connecting a disparate collection of data tools that address key pain points in big data projects in a cohesive and self-service manner. Data fabric is a design concept that serves as an integrated layer of data and connecting processes. data fabric is akin to a weave that stretches to connect sources of data, types, and locations with methods for accessing the data. A data fabric also streamlines deriving insights from data through better data observability and data quality.

Data mesh aims to mitigate the challenges of data availability by providing a decentralized connectivity layer that allows companies to access data from different sources across locations. The data mesh accelerates the re-use of data products by enabling a publish-and-subscribe model and leveraging APIs. Data mesh helps to eliminate the challenges of data availability and accessibility at scale and allows business users.

A data fabric and a data mesh both provide an architecture to access data across multiple technologies and platforms. Metadata is the mechanism to inform stakeholders about governance policies. Data fabric and mesh approaches solve all the drawbacks of previous schemes. The emerging role of active metadata, providing knowledge graphs and catalogs, is essential to the implementation of the data management platform. Automated metadata discovery is an important part of data cataloging.