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Data Fabric What is Data Fabric?
A data fabric is an architectural approach that provides a unified and consistent way to access, manage, and govern data across an organization. It essentially creates a network or "fabric" that connects various data sources, allowing them to be integrated, transformed, and shared seamlessly.
Data fabrics are particularly advantageous for organizations with a geographically diverse presence or multiple data sources.
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- What are key characteristics of a data fabric?
- How does data fabric improve business outcomes?
- What advantages does combining AI and data fabric offer?
- What data fabric products and solutions does HPE offer?
What are key characteristics of a data fabric?
Key characteristics of a data fabric include:
- Centralized management: A data fabric provides a centralized view and control over data assets, regardless of their location or format.
- Data integration: It enables the seamless integration of data from different sources, such as databases, data warehouses, cloud platforms, and IoT devices.
- Data governance: A data fabric helps enforce data quality, security, and compliance standards across the organization.
- Data automation: It automates many data management tasks, including data ingestion, transformation, and distribution.
- Scalability: A data fabric can scale to accommodate growing data volumes and complexity.
Benefits of using a data fabric:
- Improved data accessibility: Users can easily find and access the data they need, regardless of its location.
- Enhanced data quality: By enforcing data governance standards, a data fabric can improve data quality and consistency.
- Increased data utilization: A data fabric can help organizations derive more value from their data by making it more accessible and usable.
- Reduced data management costs: By automating many data management tasks, a data fabric can reduce operational costs.
- Improved decision-making: A data fabric can provide organizations with a unified view of their data, enabling better decision-making.