What Is a Data Fabric?

What Is a Data Fabric?

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.

Why Is Data Fabric Important Now?

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.

Why Use a Data Fabric?

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:

• The need to store data in an efficient way while making it accessible to the users, customers, and automations that need it
• Geographically dispersed data streams, storage solutions, and end users
• Incompatible data: structured and unstructured, data for specific applications, siloed, and legacy data
• Servicing a new generation of data-intensive applications that rely on artificial intelligence and machine learning, real-time analytics, and contextual customer experiences
• Optimizing data flows to and from IoT devices and edge computing deployments
• Keeping data secure and maintaining compliance

How Does a Data Fabric Work?

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.

What Are the Elements of a Data Fabric?

A data fabric will typically include the following layers:

• Data Management: Helps monitor system health, data security, and network optimization.
• Data Ingestion: Establishes pathways and processes for newly introduced data.
• Data Processing: Cleanses, refines, and transforms data, making it ready for specific uses.
• Data Orchestration: Helps the system run more efficiently by making sure only relevant data is delivered to users.
• Data Discovery: Helps surface new connections between different data sources, unlocking value and pointing to new insights.
• Data Access: Allows a variety of users—applications, automations, teams within the organization, or devices—ready access to data so it can be used frictionlessly.
• Data Security: Monitors and secures your data across your organization while ensuring compliance with security regulations.

Benefits of a Data Fabric

In addition to solving many data engineering challenges, a data fabric helps deliver the following organization-wide benefits:

• More Value From Data: A data fabric is designed to help an organization make more use of its data—for example, to deliver better experiences for customers, find operational efficiencies, and enable new business models.
• Better Use of Resources: By providing a high-level view of data across an organization and using AI to make recommendations, a data fabric can inform IT decision-making by showing how costs and resource loads accrue to various use cases.
• Improved Agility and Resilience: A data fabric can help an organization scale or pivot according to changing conditions or new realities by modeling changes ahead of time and by providing a consistent foundation on which to build data architectures.

Is a Data Fabric Similar to a Data Lake?

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.

Is a Data Fabric Similar to Data Virtualization?

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.

Data Fabric Use Cases

Here are a few examples of how organizations could leverage a data fabric to improve data accessibility:

• Large retailers can integrate complex inventory and supply chain data to make informed decisions about production and planning.
• IT consulting firms can consolidate data from customer support requests and retool sales strategies based on insights about gaps in available solutions.
• Farmers can incorporate disparate data streams, such as weather forecasts, market conditions, and soil condition, into critical decision-making.

Simplifying Your Data Fabric with Pure Storage

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:

• Pure1®: An AI-powered data storage management and monitoring solution that delivers self-driving storage across your entire technology stack.
• Purity operating environment: A unified platform that intelligently manages your data on Pure Storage® FlashArray™—in data centers, at the edge, or in the cloud—and allows you to simplify data management and eliminate storage silos.
• Pure Fusion™: A Storage-as-Code™ platform that brings the cloud operating model on premises. Provision, manage, and consume enterprise storage with ease.
• Portworx®: A complete Kubernetes data services solution for powering your cloud native applications.
• Pure Cloud Block Store™: A multi-cloud solution that delivers seamless data mobility, resilience, and a consistent user experience across your cloud environments.

By simplifying how people consume and interact with data, Pure empowers innovators to unlock the hidden potential within their enterprise data.