Supercharge Data Mesh through Data Fabric— Why and How?
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ThoughtWorks first introduced the term, which defines Data Mesh as “a shift in a modern distributed architecture that applies platform thinking to create self-serve data infrastructure, treating data as the product.”
Data Mesh is a methodology through which organisations can empower business units and initiatives to drive solutions.
The critical foundational elements that define Data Mesh
Extension of existing data platform technology that allows organisations to distribute ownership and operational overhead, thus managing the data with astute governance and integration in a cohesive, interactive, and predictive way.
Enable organisations to transform analytical and operational data stores into more meaningful data paradigms. Provides an extension to Data-warehouse, Data Lakehouse or Data Lake service, or Silo data marts, allowing to create unifying view across data analytics and providing a prudent delivery system.
Date lakes, data warehouses, advanced analytics, and data platforms are being organised daily to try to meet the ever-growing needs of the organisation's vision. Getting the actual value out of it is becoming a challenge, especially without disrupting the existing enterprise structure and landscape. With the information flowing from every direction, companies must be ready with integration and automation tools to drill into their data ecosystem to generate real-time business values and decisions. This is where Data Fabric plays a crucial role.
Data Fabrics provides unified data delivery patterns through tools and tech abstracting underlying data complexities. It is a Canonical architecture that is scalable and performant to support any data consumption patterns. It leverages metadata, AI, and machine learning to create a comprehensive, real-time data view.
Critical components of Data Fabric include:
Unified data access: A semantic data definition connecting diverse data sources by enabling consistent access to data across the organisation.
Single source of truth: Utilise semantic knowledge graphs, data quality, metadata management, and ML/AI overlay by effective data governance and quality process to create a single source of truth
Metadata-driven architecture: Metadata provides context, lineage, and governance across all data assets.
Scalability and flexibility: Adapts to changing data landscapes and scales with data growth.
Here's how combining the two can supercharge your data strategy:
1. Enhanced Data Discoverability and Accessibility
Data Fabric's unified data access layer can significantly enhance the discoverability and accessibility of data products managed by different domains in a Data Mesh. By integrating disparate data sources and providing a comprehensive metadata catalogue, Data Fabric makes it easier for teams to find, understand, and use data from other domains, fostering collaboration and innovation.
2. Automated Data Integration and Quality
Data Fabric's intelligent data integration capabilities can automate and streamline data ingestion, transformation, and synchronisation across domains. This reduces the manual effort required for data integration and ensures consistency and quality, aligning with the Data Mesh principle of treating data as a product.
3. Consistent Governance and Compliance
While Data Mesh advocates for federated governance, ensuring consistent compliance and quality across domains can be challenging. Data Fabric's metadata-driven architecture provides a centralised view of data lineage, policies, and compliance requirements, enabling organisations to enforce governance standards without stifling the autonomy of domain teams.
4. Scalability and Flexibility
Data Mesh's decentralised approach allows organisations to scale their data management efforts efficiently. With Data Fabric's ability to adapt to changing data landscapes and scale with data growth, organisations can achieve unparalleled flexibility and scalability in their data architecture.
5. Real-time Insights and Decision-making
The real-time integration and processing capabilities of Data Fabric can provide domain teams with timely insights, enhancing their ability to make data-driven decisions. This real-time data access is crucial for maintaining the relevance and value of data products in a Data Mesh.
Use Cases should be leveraged through Data Mesh and Data Fabric.
Data Products
Customer 360-Degree View
Self Service Analytics
Canonical Data model for enterprise
While Data Mesh decentralises data management, fostering agility and domain-specific expertise, Data Fabric ensures seamless integration and access to data across these decentralised domains.
Data Mesh methodology is about data as a product and domain-oriented data ownership. It empowers businesses to develop and deploy data solutions through Data fabric by implementing technologies like data virtualisation tools, API gateways, and data governance platforms.
