The Differences Between Data Fabric and Data Mesh

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Data Fabric:

Data and analytical processes are connected by a single, integrated architectural layer called data fabric. Data fabric uses existing metadata assets to support the design, implementation, and proper data utilization across all environments and platforms. By using automated processes, data fabric promises to speed up inference from data and provide real-time insights. It serves as a management solution, enabling frictionless access in a dispersed setting, and integrates data, analytics, and dashboarding into one.

Data fabric is a design concept, claims Gartner. The strategy makes use of continuous analytics on already-existing, discoverable, and inferable metadata assets to make it possible to design, deploy and use cohesive and reusable data in all situations.

Data mesh:

Data mesh, a highly decentralized architecture is equipped to handle challenges including data ownership issues, poor data quality, and bottleneck scalability. Data mesh aims to treat data as a product, with a separate data product owner for each source who might be a member of the cross-functional team of data engineers. Data mesh was introduced to solve the issues with conventional data lakes and data warehouses.

The Differences Between Data Fabric and Data Mesh:

Data mesh and data fabric differ in how they handle data, how they store it, and how they govern data.

Automation vs. human inclusion as a strategy:

Data mesh treats data as a product and addresses data from a people- and process-centric perspective. In order to access existing data or promote its consolidation, when necessary, data fabric makes use of both human and machine capabilities. It integrates technologies that connect sources of data access techniques with varied data sources, types, and locations. Gartner explained the idea using the example of a self-driving car: The data pipelines are passively observed by the data fabric, which subsequently makes suggestions for more effective alternatives. When the leadership is free to concentrate on innovation and the data “driver” and machine learning are both at ease with recurring circumstances, they work in tandem to automate improvisational jobs.

Centralized vs. decentralized data storage:

Data is kept decentralized within a company’s domains in a data mesh. No single point of control is required for operation because each node has access to local storage and computation power. Essentially, copies of datasets are created for certain use cases while original data is kept within domains.

With high-speed server clusters for network and high-performance resource sharing, data fabric centralizes data access.


The data mesh paradigm is a strong contender to supplant the data lake as the preeminent architectural pattern in data and analytics, claims Thoughtworks. Data Mesh gives a perspective from within an organization that is not dependent on any specific technology. To address data-related issues, its architecture uses a domain-driven design and product thinking. The goal of the data mesh culture is to link individuals together and establish a structure of federated responsibility.

Data mesh works with domain experts to manage domains, whereas data fabric uses metadata to drive suggestions. These domains are networks of microservices that can be independently deployed and interact with users. It consists of technical environments, teams, workflows, and codes.

Technical, business and operational data can all be used using data fabrics, and they are generally compatible. Visualization tools assist organizations to manage their storage costs, performance, security, and efficiency by making the technical infrastructure simple to interpret. Additionally, businesses can virtually install a single data fabric over a variety of data repositories to handle diverse downstream users and data sources.

APIs vs. controlled datasets: data access:

Controlled datasets are used to make data accessible in data mesh. The data is first copied to a shared location from the department data store.

Data is made accessible by objective-based APIs in the data fabric. The business unit that owns the data oversees copying the data into various datasets for certain use cases.

What Benefits Data Mesh Offers Data Fabric and Vice Versa:

Organizations can make use of the data fabric’s automated capabilities and implement them at different phases of the data mesh.

For instance, by automating the data preparation phases of the ML process, businesses can improve the speed and quality of models and make models available for consumption via controlled datasets, bringing machine learning applications closer to their end users.

As a result, organizations benefit most from using both strategies. Instead of leaving it entirely in the hands of data engineers, who might not have a thorough understanding of the use case for data, domain users will know how to use final models in the best possible way.

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