Charts, graphs, and data analytics about data fabric and modernizing data.

What is Data Fabric Architecture?

Understanding Data Management and Integration for Unified Data Storage

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

Unlike traditional data integration methods that rely on point-to-point connections and rigid architectures, the framework embraces a more flexible and dynamic paradigm, where data flows freely and securely across the entire organizational landscape, ensuring robust data security.

At its core, data fabric serves as a seamless layer that integrates disparate data sources, applications, and infrastructure for data virtualization.

The data fabric architecture enables data discovery in a distributed environment while providing a unified view of the data infrastructure for the organization. This architecture connects various data sources, including cloud-based data warehouses, on-premise databases, and SaaS applications, to create a centralized data fabric that allows seamless data access, integration, and processing.

This, in turn, simplifies data management and accelerates data processing, which enables organizations to gain insights and make informed decisions faster with specific data. Data engineers and data scientists can leverage this architecture to streamline their workflows and improve data analytics capabilities.

Additionally, data fabric architecture can easily scale to accommodate increasing data volumes — reducing the operational cost and complexity of physically integrating and centralizing data.

Purpose of Data Fabric

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

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

With a cohesive data fabric, businesses can better manage their data, utilize connected data, metadata, and data analytics, and ultimately unleash maximum value with improved data quality.

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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. Its cohesive visibility gives you a clear and comprehensive view of your data landscape in real time. This simplifies the data management process and speeds up data processing, enabling you to make faster and better-informed decisions.

Using data fabrics can help with easy scaling up to accommodate the ever-increasing amount and variety of data that businesses generate today — helping them 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

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:

Enhanced Data Accessibility and Insights

A unified infrastructure allows for better data visibility — and insights that come from that visibility. Data fabric provides organizations with a unified and integrated view of their data assets, enabling stakeholders to access, analyze, and act upon data more efficiently and effectively. This empowers decision-makers with timely and actionable insights. As a result, it drives better decision-making, innovation, and competitive advantage.

Improved Operational Efficiency and Agility

Data fabric reduces complexity and inefficiencies in data operations by streamlining data integration, governance, and management processes. Improved data access and control drastically streamlines data management initiatives, giving time back to governance teams. This enables organizations to respond more quickly to changing business needs, scale their data initiatives, and drive operational excellence and agility.

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Accelerated Innovation and Time-to-Value

A data fabric enables businesses to better protect and reduce the cost of maintaining and managing data — particularly in multi-cloud environments. It enables organizations to unlock the full potential of their data assets to fuel innovation and drive new business opportunities. By democratizing data access and fostering a culture of experimentation and collaboration, the data architecture empowers teams to innovate, iterate, and deliver value to customers faster and more effectively.

Enhanced Privacy and Security Compliance

Data fabric incorporates robust governance, security, and compliance mechanisms to ensure the privacy, integrity, and confidentiality of sensitive data assets. This includes implementing access controls, encryption, and data masking techniques to protect data at rest and in transit. Moreover, data fabric enables organizations to enforce regulatory compliance with data privacy laws such as GDPR, CCPA, and HIPAA by providing visibility into data lineage, usage, and consent management.

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 fabric and data lake are two different approaches to managing data, and while they may seem at odds, they can actually coexist quite well. The latter is a centralized repository that allows for storing and analyzing 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. In practice, this means that they can act as the primary data source for a data fabric. Data fabrics, on the other hand, help ensure that this new data is accessible and available to users and applications. They provide the necessary connectivity and agility to access data and analyze it in real time.

For example, a data lake could store and process large amounts of customer data, while a data fabric could integrate this data with other sources, such as social media, to provide a complete view of customer behavior.

These data lakes and fabrics facilitate the creation and delivery of data products. By leveraging them, organizations can gain greater insights and efficiencies from their real-time data, while ensuring that it is accessible and available to those who need it.

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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, streamlines data delivery 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

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’s how BigID helps build a seamless data fabric solution to future-proof digital transformation and data management practices for your business.

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 data types with NLP and NER — and AI insight based on deep learning within the data management architecture.

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.