Data Fabric

What is Data Fabric?

 Data fabric is a centralized data architecture that serves authorized users with integrated, governed, fresh data for analytical and operational workloads. In simpler terms, it creates a seamless, consistent data environment that enables various users and applications to access and use data efficiently and effectively.

Enterprise data fabric provides a holistic view of data across different systems and platforms, ensuring it’s available when and where it’s needed while reducing the complexity and time it typically takes to manage and analyze large data volumes.

Data fabric simplifies data access by breaking down silos and integrating disparate data sources. The result is a more agile and responsive data management approach that supports a broad range of use cases, from real-time analytics to business intelligence. This data fabric architecture empowers enterprises to harness data’s full potential, driving innovation and competitive advantage.

Key Components of Data Fabric

Data fabric supports the following key capabilities integrated into a single platform:

• Data catalog and metadata management. This acts as a central registry for all an organization’s data assets, allowing users to find and understand the data they need to perform their tasks.
• Data access governance and security. This ensures data quality, regulatory compliance, and secure access to data throughout the organization.
• Data integration and transformation. This involves data fabric tools and processes to move data from various sources, clean it, and deliver it to users in a functional format.
• Advanced algorithms for insight generation. AI/ML algorithms continuously monitor data and generate real-time insights, reducing processing time and delivering insights faster.
• APIs and SDKs for connectivity with delivery interfaces. This component distinguishes data fabric from traditional data lakes or warehouses. Its built-in integration-readiness allows it to connect with any front-end user to deliver insights where they’re needed most.
• Data consumption layer. This user-facing interface enables front-end data consumption, allowing businesses to get maximum return from their data fabric investment.
• Analytics and knowledge graphs for processing. Data fabric ingests a lot of semi-structured and unstructured data from various sources. Analytics and knowledge graph systems transform data types into coherent formats so they can be easily processed.

How Data Fabric Works

Data fabric is much greater than the sum of its components. Its non-functional capabilities include scalability, accessibility, security, and distribution in multi-cloud, on-premise, and hybrid environments.

When properly implemented, the principles of data fabric contribute to a more streamlined and efficient data management environment. These principles include:

• Unified view. Data fabric provides a single, logical view of all an organization’s data, regardless of whether it resides on-premise, in the cloud, or in a hybrid environment. This breaks down data silos and makes it easier for users to find what they need.
• Self-service access. In an ideal world, authorized users should be able to access and explore data easily without extensive technical expertise. Data fabric empowers users through self-service capabilities.
• Location independence. By decoupling data from its physical location, data fabric allows for flexibility in storing and processing data, making it easier to adapt to changing needs.
• Interoperability. Data fabric is designed to work with various data sources and tools, promoting a more open and integrated data ecosystem.
• Automation. Repetitive tasks like data transformation and movement are automated within a data fabric, reducing manual effort and improving efficiency.
• Scalability. A well-designed data fabric can scale to accommodate growing data volumes and user demands.

Your organization’s data fabric architecture depends on its specific data needs and situation. The six common layers for modern enterprise data fabrics according to Forrester are:

1. Data management to provide governance and security.
2. Data ingestion to identify connections between structured and unstructured data.
3. Data processing to extract only relevant data.
4. Data orchestration to clean, transform, and integrate data.
5. Data discovery to identify new ways to integrate various data sources.
6. Data access to enable users to explore data via analytic and business intelligence tools-based access permissions.

Benefits of Data Fabric

The operational benefits data fabric provides to enterprises include:

• Unified data access provides a single, integrated data layer that reduces data silos and allows users and applications to access data across different sources and environments seamlessly.
• Enhanced data governance establishes and enforces consistent policies across all data sources, ensuring compliance with data privacy regulations like GDPR and CPPA and improving data quality and security.
• Improved DataOps reduces data management complexity, automating data discovery and integration tasks while optimizing data movement.
• Real-time data access and integrations support faster, more informed decision-making.
• Fewer GenAI hallucinations ensure more accurate GenAI recommendations.

Data fabric also offers several significant advantages over other data management approaches, including automated data retrieval and validation, expanded data services, and the use of innovative engines to manage and synchronize data with full support for SQL and innovative engines that manage and synchronize data, fully supporting SQL and web services. It ensures consistency, durability, and availability, meeting enterprise standards with a reliable database layer and processing engine. The architecture allows for excellent performance by running queries on small data sets and using in-memory processing.

Implementing Data Fabric

Establishing a data fabric architecture requires a multi-prong approach. Gartner recommends considering four pillars for implementation:

1. Collecting and analyzing all types of metadata.
2. Converting passive metadata to active metadata.
3. Creating and curating knowledge graphs that enrich data with semantics.
4. Ensuring a robust data integration foundation.

For instance, a healthcare organization looking to enhance its data management might begin by collecting and analyzing metadata from electronic health records, lab results, and patient feedback systems. They would then convert passive metadata into active metadata to automate patient care workflows and improve diagnosis accuracy. By creating and curating knowledge graphs, they could link medical terms and patient data, providing richer context and insights for healthcare professionals. Finally, they could ensure a robust data integration foundation by deploying advanced integration tools that enable seamless access to consistent and reliable data across all their healthcare facilities.

For enterprises wanting a unified and scalable data management solution, data fabric delivers seamless access to integrated, high-quality data across the organization.