Deriving value from analytics investments depends on having an agile and trusted data fabric. A data fabric is generally a custom-made design that provides reusable data services, pipelines, semantic tiers or APIs via combination of data integration approaches in an orchestrated fashion. Data fabrics can be improved by adding dynamic schema recognition, or even cost-based optimization approaches. As a data fabric becomes increasingly dynamic, or even introduces ML capabilities, it evolves from a data fabric into a data mesh network.

What does Data Fabric enable?

  • Data fabric is more of a designed approach, mostly tending toward use cases and locations on either “end” of a thread. The threads may cross and do handoffs in the middle, or even reuse their parts, but they are not built up dynamically. They are merely highly reusable, normalized services.

  • Data mesh is a fully metadata-driven approach. Statistics in the form of metadata accumulation are kept relating to the rate of data access; platform, user and use case access; the physical capacity of the system; and the utilization of the infrastructure components. Other data points include the reliability of the infrastructure, the trending of data usage by domain and use case, and the qualification, enrichment and integrity (both declared and implied) of the data.

How Does This Impact Your Organization and Skills?

  • Data engineer: The human capabilities of data engineers will be augmented by AI/ML processes that identify nearly all of the initial pain points for data refactoring, modeling, schema production and data quality recognition. 

  • Data scientist: This role will benefit from data fusion outputs that create alerts about expanding data assets. These alerts will be specifically tuned for the current project — but, can also begin to recognize data that a given scientist utilizes in terms of data patterns.

  • Data modeler: Data modelers, data integration developers and database administrators responsible for data modeling will model less, and verify more. 

  • Information architect: Information architects working with data fabric will need to focus on identifying the required functionality of a data asset and imputing it as metadata.

We've got you covered!

Relevant Sessions

  • Upgrade to a Data Fabric Design to Transform Your Data Management and Integration Architecture
  • Cloud is the Future of Data Infrastructure
  • Magic Quadrant for Data Management
  • Data Lakes, Data Warehouses and Data Hubs Are Not The Same: Know Their Capabilities and Purpose
  • How to Avoid Data Lake Failures

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