What Is Data Fabric?

Data fabric, which is gaining traction in the market, refers to an agreed-upon process that integrates an organization’s data, processes, analytics, and more into a unified, interconnected architecture. Data fabric standardizes data governance practices across on-prem and cloud environments — including hybrid and multi-cloud.

Purpose of Data Fabric

Up to 68% of the average organization’s data is not analyzed, according to IBM — and up to 82% of organizations run into integration problems due to siloed data and different data types coming from various sources. This spells trouble for data-driven organizations.

With a cohesive and coherent data fabric, enterprises can better manage their data and utilize an integrated data, metadata, and analytics infrastructure — and ultimately unleash maximum value from their data.

Why Use a Data Fabric

If you’re looking for a modern and efficient way to manage your data, using a data fabric might just be the way to go. This approach allows you to bring together data from different sources, giving you a clear and comprehensive view of your data landscape in real-time. This not only simplifies the data management process but also speeds up data processing, enabling you to make faster and better-informed decisions.

Data fabrics can easily scale up to accommodate the ever-increasing amount and variety of data that businesses generate today— helping you become more productive, make better decisions, and stay ahead of the competition. Using a fabric also gives users secure, compliant access to the right quality data to perform their data tasks.

Benefits of Data Fabric

“Fabric” refers to the integrated layer of data and connecting processes across all data environments — including hybrid and multi-cloud platforms.

By weaving together continuous analytics, automated technologies, AI models, and machine learning across complex data environments, enterprises can boost data trust, make better decisions, and drive digital transformation. Here are a few ways how:

Business Insights: A unified infrastructure allows for better data visibility — and insights that come from that visibility.

Governance: Improved data access and control drastically streamlines data management initiatives, giving time back to governance teams.

Security and Cost: A data fabric enables businesses to better protect their data and reduce cost from maintaining and managing data — particularly in multi-cloud environments.

Data Fabric Architecture

Data fabric architecture is a revolutionary approach to data management that enables organizations to create a unified data infrastructure, regardless of where their data is stored. This architecture involves connecting various data sources, including cloud-based data warehouses, on-premise databases, and SaaS applications, to create a centralized data fabric that allows for seamless data access, integration, and processing.

The data fabric architecture enables businesses to manage their data in a distributed environment while providing a unified view of the data landscape. This, in turn, simplifies data management and accelerates data processing, enabling organizations to gain insights and make informed decisions faster. Additionally, data fabric architecture can easily scale to accommodate increasing data volumes — reducing the operational cost and complexity of physically integrating and centralizing data.

Data Fabric Vs. Data Mesh

Data mesh describes another data management process that is often confused with data fabric but tackles the problem of distributed data differently. While data fabric takes a universal interconnectivity approach — weaving together a continuous, unified infrastructure for data management — data mesh is a centrally created architecture for use across distributed data silos. Data mesh does not necessarily act on the issue of interoperability, however.

Ultimately, both approaches make data more accessible and secure, but data fabric alone focuses on a holistic, interactive architecture.

Data Fabric vs Data Lake

Data lakes and data fabrics are two different approaches to managing data, and while they may seem at odds, they can actually coexist quite well. A data lake is a centralized repository that allows for the storage and analysis of large volumes of structured and unstructured data. In contrast, a data fabric is a distributed architecture that allows for the seamless integration and sharing of data across multiple sources and platforms.

Data lakes are great for storing and processing large amounts of data, while data fabrics help to ensure that this data is accessible and available in real-time to users and applications. In practice, this means that data lakes can act as the primary source of data for a data fabric, while the data fabric provides the necessary connectivity and agility to access and analyze the data in real-time.

For example, a data lake could be used to store and process large amounts of customer data, while a data fabric could be used to integrate this data with other sources, such as social media, to provide a more complete view of customer behavior. By leveraging both data lakes and data fabrics, organizations can gain greater insights and efficiencies from their data, while also ensuring that it is accessible and available in real-time to those who need it.

Relationship Between Data Fabric and Data Integration

A data fabric depends on automated, AI-driven integration that improves over time. An effective fabric automates multiple integration styles, scales data management across an enterprise, reduces storage costs, and maximizes performance. The resulting architecture:

  • makes difficult-to-locate data easily accessible in multi-cloud and hybrid environments
  • eliminates data silos
  • eliminates multiple and manual tools
  • future-proofs data management practices, as new sources are added
8 Steps to implementing a data fabric

Data Fabric Use Cases

Building a data fabric for your organization helps:

  • increase interoperability efforts
  • boost security by integrating IT systems
  • create unified views of customer data, driving better decisions and customer marketing
  • improve targeted marketing

BigID and Your Enterprise Data Fabric — How It Works

BigID introduces an ML-driven, semantic approach to enabling a data fabric for your organization. Here are just a few ways that BigID can help your enterprise future-proof digital transformation and data management practices by building a seamless data fabric.

Cover all your data — everywhere: Automatically connect to all data types — including structured and unstructured data — in on-prem, multi-cloud, and hybrid environments.

Get a single view of metadata: With an unmatched discovery foundation, BigID can scan all your data, everywhere, to create a unified data catalog — and a single view of all your metadata.

Classify data based on deep learning: BigID specializes in classification methods that go beyond pattern-based discovery. Automatically classify more types of data with NLP and NER — and AI insight based on deep learning.

Exchange and share data: Enable collaboration and data sharing among employees in real time.

Add context to data: Layer technical, business, and operational metadata to see data attributes and relationships.

Leverage active metadata for better interoperability: With ML-augmented metadata, gain insights from your metadata, empower your organization to take action on it, and drive better business decisions.

Check out a demo to see BigID in action — and find out how we can help you build an ML-driven data fabric.