The data fabric aims to simplify data complexity through automating data integration, data governance, and data processing. Tools for data fabric design and management include data pipelines with various integration styles; workflow management, orchestration and policy management; active metadata and machine learning (ML) augmented data management; augmented data cataloging; data virtualization.

As the data fabric increasingly spans data across multiple clouds, data centers, and edge systems, it typically is built with the help of container-based technologies (such as Kubernetes) and related services mesh technologies.

The data fabric should provide a single environment for accessing and collecting all data, no matter where it’s located and no matter how it’s stored – eliminating data silos. As compared to the manual process of data pipeline creation which leads to slow, error-prone, redundant work, the automation of pipeline creation allows data engineers to better service data consumers.

Enriching data with business semantics and metadata level governance fosters a collaborative, self-service environment that allows for new and accelerated time to value in customer 360 views, fraud detection, IoT analytics, and many other use cases.