A data fabric is a unified architecture that captures the end-to-end integration and management of all data within a system, including sources, storage, pipelines, analytics, and applications.
The metaphorical “fabric” in a “data fabric” refers to the idea of viewing your organization’s data as a single integrated network layer versus a siloed collection of point-to-point connections. Approaching your data as a fabric can help you better optimize performance, improve data mobility, and streamline data operations.
In practice, a data fabric is created by taking a data-centric approach to IT architecture and using data integration and management software to define new architectures.
About a decade after big data was named the next big thing, organizations are realizing that collecting and storing data is just the start of reaping data’s benefits. Following through on big data’s promise—game-changing insights, cutting-edge experiences, new business models, and artificial intelligence everywhere—requires a new approach to data management. This approach integrates information from all sources and makes data available when and where it’s needed, no matter the user or endpoint—all while keeping data secure wherever it resides or when it’s in transit.
Naming data fabrics one of the top technology trends for 2022, Gartner said data fabrics can simplify an organization’s data integration infrastructure and create a scalable architecture that reduces integration challenges. A data fabric can also reduce data management efforts by up to 70%, therefore accelerating time to value.
A data fabric can unlock the hidden potential of big data within your hybrid cloud environment by making data accessible across your on-premises, public cloud, private cloud, and edge environments.
Here are some common data management challenges that a data fabric can address:
Data management software integrates data flows, users, endpoints, storage, and network architecture into a data management layer that provides visibility and an interface for control and management. The software learns an organization’s entire data estate, flags bottlenecks, and makes recommendations to improve performance and access.
With the software, data engineers can see a high-level view or dig deeper to improve performance for individual use cases. The software also establishes a common data landscape and a set of APIs to integrate with applications, data streams, and use cases.
A data fabric will typically include the following layers:
In addition to solving many data engineering challenges, a data fabric helps deliver the following organization-wide benefits:
Many organizations are choosing data lakes to solve data-access issues, but a data lake is a top-down approach and is defined as having one master repository of data. Data lakes can create extra work in terms of streaming and uploading and make data more difficult to access and manage. In addition, some data may be needed far enough away from the data lake to introduce high latency. In practice, a data fabric can help organizations get more from a data lake.
These two concepts are more complementary than oppositional. Data virtualization creates an interface for managing, moving, and working with data. A data fabric, on the other hand, is an all-encompassing method for optimizing every part of data operations: performance, cost, resource efficiency, security, growth, and change management.
Here are a few examples of how organizations could leverage a data fabric to improve data accessibility:
Setting up a data fabric that fully covers your entire hybrid cloud environment is no small feat. You have to integrate data across disparate sources throughout your on-premises, public cloud, private cloud, and edge environments, all while maintaining data governance and security.
Got gaps in your data fabric or looking to set up one of your own? Pure Storage has the solutions you need to create and support a modern data fabric:
By simplifying how people consume and interact with data, Pure empowers innovators to unlock the hidden potential within their enterprise data.
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