{"id":18808,"date":"2023-08-31T10:00:40","date_gmt":"2023-08-31T14:00:40","guid":{"rendered":"https:\/\/enterprise-knowledge.com\/?p=18808"},"modified":"2024-02-09T14:06:17","modified_gmt":"2024-02-09T19:06:17","slug":"what-is-a-data-fabric-architecture-and-what-are-the-design-considerations","status":"publish","type":"post","link":"https:\/\/enterprise-knowledge.com\/what-is-a-data-fabric-architecture-and-what-are-the-design-considerations\/","title":{"rendered":"What is A Data Fabric Architecture and What Are The Design Considerations?"},"content":{"rendered":"

\"Components<\/a><\/p>\n

Introduction<\/h2>\n

In today\u2019s data-driven world, effective data management is crucial for businesses to remain competitive. A modern approach to data management is the use of data fabric architecture. A <\/span>data fabric<\/a><\/strong> is a data management solution that connects and manages data in a federated way, employing a logical data architecture that captures connections relevant to the business. Data fabrics help businesses make sense of their data by organizing it in a domain-centric way without physically moving data from source systems. What makes this possible is a shift in focus on metadata as opposed to data itself. At a high level, a semantic data fabric leverages a knowledge graph as an <\/span>abstraction architecture layer<\/a> <\/strong>to provide connectivity between diverse metadata. The knowledge graph enriches metadata by aggregating, connecting, and storing relationships between unstructured and structured data in a standardized, domain-centric format. Using a graph-based data structure helps the business embed their business data with context, drive information discovery and inference, and lay a foundation for scale.<\/span><\/p>\n

Unlike a monolithic solution, a data fabric facilitates the alignment of different toolsets to enable domain-centric, integrated data as a service to multiple downstream applications. A data fabric architecture consists of five main components:<\/span><\/p>\n

    \n
  1. A data\/metadata model<\/span><\/li>\n
  2. Entity extraction<\/span><\/li>\n
  3. Relationship extraction<\/span><\/li>\n
  4. Data pipeline orchestration<\/span><\/li>\n
  5. Persistent graph data storage.\u00a0<\/span><\/li>\n<\/ol>\n

    While there are a number of approaches to designing all of these components, there are best practices to ensure the quality and scalability of a data fabric. This blog post will enumerate the approaches for each architectural component, discuss how to achieve a data fabric implementation from a technical approach and tooling perspective that suits a wide variety of business needs, and ultimately detail how data fabrics support the development of artificial intelligence (AI).<\/span><\/p>\n

    Data\/Metadata Model<\/h2>\n

    Data models \u2013 specifically, ontologies and taxonomies \u2013 play a vital role in building a data fabric architecture. An <\/span>ontology<\/a><\/strong> is a central aspect of a data fabric that defines concepts, attributes, and relationships in a domain that is encoded in a machine and human-readable graph format. Similarly, a taxonomy is essential for metadata management in a data fabric, storing extracted entities and defining controlled vocabularies for core business domains like products, business lines, services, and skills. By creating relationships between domains and data, businesses can help users find insights and discover content more easily. Therefore, to effectively manage taxonomies and ontologies, business owners need a Taxonomy\/Ontology Management System (TOMS) that provides a user-friendly platform and interface. A good TOMS should:<\/span><\/p>\n