In Part 1 we established what a Data Fabric and Data Mesh is at a high level. Now let’s take a deeper look at how a Data Fabric and a Data Mesh is actually assembled. To do that, it’s important to first understand the components of both products.
The Data Mesh shifts the focus from centralized data lakes and data warehouses to a distributed architecture, where data is treated as a product and managed by cross-functional, domain-centric teams.
Read Data Fabric Deep Dive Part 3
Components of a Data Mesh
The key components of a Data Mesh include:
Implementing these components and embracing the core principles of Data Mesh enables organizations to build a decentralized, domain-oriented, and self-serve data infrastructure that scales effectively and promotes collaboration and data sharing across the organization.
Components of a Data Fabric
Data Fabric is a framework and sometimes productization of the Modern Data Stack consolidated into one product or integrated framework.
The core components and capabilities of a Data Fabric include:
A Data Fabric is relevant to building a Data Mesh because it provides the underlying data management and integration framework that enables the Data Mesh’s core principles to function effectively.
Here’s how a Data Fabric contributes to building a Data Mesh:
By leveraging a Data Fabric, organizations can create a robust, scalable, and efficient data infrastructure that enables domain teams to work independently while still benefiting from shared data resources and insights.
Complementary Paradigms
It’s important to note that Data Mesh and Data Fabric are not competing paradigms; rather, they complement each other. Both concepts address different aspects of data management and integration, and when combined, they can create a comprehensive and effective data infrastructure.
Data Mesh is a paradigm that focuses on the organizational and architectural aspects of managing data at scale. It promotes decentralization, domain-oriented architecture, data as a product, and self-serve data platform capabilities. The main goal of Data Mesh is to enable better collaboration, data ownership, and data sharing across different domain teams in large-scale organizations.
Data Fabric, on the other hand, is a technological framework that addresses the challenges of data access, integration, and management across disparate sources. It provides a unified and consistent view of data, making it easier for users to access, analyze, and use data across the organization.
Data Mesh and Data Fabric are complementary concepts that, when combined, can create a robust and efficient data infrastructure that delivers several tangible benefits. Data Fabric enables seamless data access and integration across sources and domains that support the decentralized approach promoted by Data Mesh. In addition, the unified view Data Fabric provides across disparate sources aligns well with the domain-oriented Data Mesh architecture, enabling teams to share and access data from other domains while working independently. Data Fabric simplifies data access, integration, and management while supporting self-serve data platform capabilities emphasized in the Data Mesh paradigm. Finally, Data Fabric can enhance data discoverability, governance, and compliance within a Data Mesh by offering metadata management, cataloging, lineage, and quality features.
Conclusion
In summary, Data Mesh and Data Fabric are complementary concepts that, when combined, can create a robust and efficient data infrastructure. As noted above, Data Mesh focuses on the organizational and architectural aspects, while Data Fabric provides the underlying technological framework for effective data management and integration. By leveraging both paradigms, organizations can build a comprehensive data infrastructure that promotes collaboration, data ownership, and efficient data sharing across domains.
In part three of this blog series, we explore what’s required to actually build a Data Fabric and Data Mesh in detail. Then in the final part of this four part series, we will look at how the Data Fabric and Data Mesh might not be as different as you may think and explore a new way of looking at these two frameworks. Spoiler: Prepare to be surprised!
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