"A data architecture needs to have the robustness and ability to support multiple data management and operational models to provide the necessary business value and agility to support an enterprise’s business strategy and capabilities." (Sonia Mezzetta, "Principles of Data Fabric", 2023)
"A data strategy must align with the business goals and overall framework of how data will be used and managed within an organization. It needs to include standards for how data will be discovered, integrated, accessed, shared, and protected. It needs to address how data will meet regulatory compliance policies, Master Data Management, and data democratization. There needs to be an assurance that both data and metadata have a quality control framework in place to achieve data trust. A data strategy needs to have a clear path on how an organization will accomplish data monetization." (Sonia Mezzetta, "Principles of Data Fabric", 2023)
"Data Fabric focuses on Self-Service data access via active metadata leveraging a composable set of tools and technologies. It offers the ability to discover, understand, and access data across hybrid and multi-cloud data landscapes with automation and Data Governance. It is primarily process and technology centric with flexibility in supporting diverse organizational models. On the other hand, Data Mesh is organizationally and process driven. It requires a technical implementation approach to execute its design. Data Mesh is at a higher level and Data Fabric is at a lower level. Data Fabric is capable of fulfilling Data Mesh’s key principles." (Sonia Mezzetta, "Principles of Data Fabric", 2023)
"Data Fabric is a distributed and composable architecture that is metadata and event driven. It’s use case agnostic and excels in managing and governing distributed data. It integrates dispersed data with automation, strong Data Governance, protection, and security. Data Fabric focuses on the Self-Service delivery of governed data." (Sonia Mezzetta, "Principles of Data Fabric", 2023)
"[Data Fabric] is not a single technology, such as data virtualization. […] It is not a single tool like a data catalog and it doesn’t have to be a single data storage system like a data warehouse. It represents a diverse set of tools, technologies, and storage systems that work together in a connected ecosystem via a distributed data architecture, with active metadata as the glue. It doesn’t just support centralized data management but also federated and decentralized data management. It excels in connecting distributed data. Data Fabric is not the same as Data Mesh. They are different data architectures that tackle the complexities of distributed data management using different but complementary approaches." (Sonia Mezzetta, "Principles of Data Fabric", 2023)
"Data Fabric supports a federated, decentralized, or centralized organization. To participate in Data Fabric, metadata is contributed in an automated manner and knowledge is populated from it to propel data management. Data Fabric is different from a Data Mesh design in that it supports decentralized, federated, and centralized organizations. Data Fabric’s objectives are to help an organization to evolve to a more mature level of data management by leveraging active metadata, which is a core prerequisite." (Sonia Mezzetta, "Principles of Data Fabric", 2023)
"Data Mesh is a design concept based on federated data and business domains. It applies product management thinking to data management with the outcome being Data Products. It’s technology agnostic and calls for a domain-centric organization with federated Data Governance." (Sonia Mezzetta, "Principles of Data Fabric", 2023)
"I emphasize this point as there are views in the industry that Data Fabric is a centralized storage architecture, which is not the case from my point of view. A Data Fabric architecture is driven by the needs and direction of the business architecture." (Sonia Mezzetta, "Principles of Data Fabric", 2023)
"Where Data Mesh differs from Data Fabric is that it has fixed requirements for the Self-Service platform focused on organizing and managing Data Products by business domain. Another difference is Data Fabric supports managing data as an asset and as a product. A Data Product can be composed of assets that have been governed and managed in a Data Fabric architecture. Data Fabric does not have these fixed requirements, although it inherently supports isolating data and Data Governance enforcement via metadata by business domain. You can think of a Data Mesh Self-Service data platform as supporting separate, independent companies (business domains), although the key criteria are that it does not create data silos and attains data sharing across these companies in a secure, quick, and easy manner. In Data Mesh, Data Products are created and managed by federated business domains and a data platform requires capabilities that enable data and policy federation. This is where a Data Fabric solution can also address Data Mesh’s requirements." (Sonia Mezzetta, "Principles of Data Fabric", 2023)
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