Data Fabric
What is Data Fabric?
Data Fabric is an end-to-end data management design that supports the integration of various and heterogenous data pipelines, services, and environments using automation.
It supports a combination of different data integration styles and leverage active metadata, knowledge graphs, semantics and ML to augment data integration design and delivery. (Source: Gartner®)
The primary goal of a Data Fabric is to support flexible, agile, and timely data management for modern applications and analytics, regardless of where data resides or how it is structured.
Benefits of Data Fabric
- Share data across organization
- Solve complex data problems
- Manage data regardless of application/platform types (where data is stored)
- Seamless access and data sharing in distributed data environment
- Greater flexibility, scalability, and efficiency in data management
- Better insights and decision-making
Do I need to change from Data Fabric to Data Mesh?
The hype about Data Mesh continues to grow, and innovative companies seek to implement the approach to their data management systems. One might ask the question: What about the data architecture we already have in place? Do we need to say goodbye to Data Fabric now? As Gartner® states in the Hype Cycle for Data Management 2023: “Several technologies and disciplines around future data architectures are currently heading into or passing through the trough: DataOps, data fabric, active metadata management, and augmented data cataloging/metadata management solutions”—stating that database features are moving towards “obsolete before plateau”. But this doesn’t mean that Data Fabric is less functional today. Gartner® rather assumes that the functions of Data Fabric will converge with Data Mesh.
The objective is the same: Greater access and use of more of the company’s data.
While one might believe Data Mesh and Data Fabric are opposing approaches, they ultimately pursue the same goal of improving data management. Data Mesh focuses on decentralized responsibility and collaboration, whereas Data Fabric focuses on a unified infrastructure and data integration. – Source
Differences between Data Mesh vs. Data Fabric
Data Mesh and Data Fabric are both concepts designed to address challenges in the modern data landscape, particularly as organizations grow in complexity and scale. While they share some overlapping principles, they are distinct in their approach and focus.
Whether you choose Data Fabric, Data Mesh, or a blend of both will depend on your specific needs and requirements—and the considerations for the architectures and technologies you already have in place. Data Mesh is about rethinking organizational structures and responsibilities around data, while Data Fabric is about creating a unified technological layer for data access and integration.
Whatever data architecture you prefer or already have in place, the AI-powered Data Product Builder complements your existing technology ecosystem and enables your team to expedite the ability to connect, discover, prep, quality check and deploy ready to use data products for business consumption.
Contact One Data now!
Your Contact: Sebastian Wernicke | Leading Data Scientist
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Success story with SCHOTT:
Learn how SCHOTT moved away from its centralized architecture and implemented Data Mesh with One Data.
Explore moreeBook Data Mesh: How Companies Can Unearth Data Treasures
This eBook offers you an overview on the basics you need for Data Mesh: its principles, what you need for implementation as well as the technical foundations for Data Mesh.
Explore moreMarco Polo - Road to Data Products Workshop
Start getting value from your data and get your first data product off the ground quickly in this one-day workshop with our team of data product experts.
Explore moreGartner® Article
Data Fabric Architecture is Key to Modernizing Data Management and Integration – D&A leaders should understand the key pillars of data fabric architecture to realize a machine-enabled data integration.
Explore more
Do you have any questions about One Data, or would you like more detailed technical information?
Your contact:
Stefan Roskos | Managing Director
Contact One Data now!
Your Contact: Stefan Roskos | Managing Director
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