As data architecture continues to evolve, two data management concepts have emerged for their innovative approach to managing enterprise data at scale. We are going to look at these two concepts and determine which would best suit your organization.
Data Mesh
The first concept to familiarize ourselves with is the data mesh. Data mesh is a decentralized data architecture that organizes data in domains, which are collections of data organized around a particular business purpose, such as marketing or procurement, or a particular customer segment or region. By organizing such data into domains, data is widely accessible across the organization in a user-friendly manner while the people who have produced this data are given a greater degree of ownership and responsibility for its quality, accessibility, and security.
Data Fabric
The second is the data fabric. Data fabric is a centralized and unified data architecture designed to deal with the complexity of data management and minimize disruption to data consumers. At the same time, it ensures that any data on any platform from any location can be effectively combined, accessed, shared, and governed from a single location or source in a seamless manner, enabling centralized data access, governance, and processing through a layer of connected data services and pipelines.
Key Differences
The differences between the data mesh and the data fabric go beyond the decentralized vs. unified approach, though these differing approaches serve as the foundation as to how different these architectures work.
Architectural scalability
Because of its decentralized and distributed nature, data mesh is much more scalable on a domain level, allowing domains to make innovations without heavy central oversight or even add new domains as the organization or the domains see fit.
On the other hand, the data fabric offers greater flexibility for data integration and access across the organization, which makes it easier to facilitate and deploy changes across the enterprise while the parameters and the quality of the data are ensured.
Data integration
For the data mesh, data integration is handled at the domain level. On one hand, this promotes local data ownership, empowering teams to manage and share their data as needed. However, it also increases the complexity of managing and governing data across multiple domains.
Data Fabric integrates data through a centralized platform. Such centralized governance simplifies compliance and security requirements but may limit the flexibility and agility of individual domains to build custom data solutions.
Data quality implementation
Implementing data quality across the organization can be challenging in a data mesh environment as significant coordination and investment in training and tools to support decentralized data management are needed. However if implemented properly, it empowers each domain to have its own quality controls and processes while adhering to the basic quality parameters set by those at the organization’s helm.
With its centralized approach, data fabric is seen to have an inherent advantage in ensuring data quality and consistency because the data is managed and governed by a central team. Any implementation challenges often revolve around the complexity of integrating disparate data systems and the high technical overhead of maintaining the unified architecture.
Choosing Between Data Mesh and Data Fabric
As we have seen, both the data mesh and the data fabric have their benefits and drawbacks. It is up to the organization to decide which architecture best suits their present and potential data management needs.
In particular, the organization should be mindful of these factors in choosing between the data mesh and the data fabric:
Size – Larger organizations, especially those with a lot of team members and/or domains/departments tend to go with a data mesh
Data delivery issues – If data delivery requirements can be sufficiently met with a centralized data system, then the data fabric is the way to go.
Decision-making culture – Does the organization have a centralized approach to decision-making at all times (data fabric) or can department heads make their own decisions for their respective departments in department-level situations? (data mesh)
Budget – If the organization can afford to implement domain-level self-serve data infrastructure, then it would be best to implement a data mesh.
Skills and resources – A data mesh would be appropriate for an organization that has the manpower and resources needed at the domain level to define and build data products.
Choosing between data fabric and data mesh depends on your organization’s unique needs, scale, and goals so it is critical that these are taken into account to ensure the efficiency of the data management approach that will be chosen which in turn facilitates in the growth and success of your organization. By aligning your organization’s choice with your data strategy, you can unlock the full potential of your data architecture and drive meaningful business outcomes.