What is Data Fabric: Key Concepts, Implementation & Use Cases

Data access and visibility limitations have long been a talking point due to fragmented data across multiple systems. These challenges are technically named under data silos. If your organization struggles with these limitations, it may be time to consider a new approach, like data fabric.

Data fabric is a data management framework that centralizes data access without requiring you to physically move data to a single repository. Let’s explore how it works, how to implement it, and best practices for success.

What is Data Fabric?

An organization will have various data sources, including data lakes, warehouses, SaaS applications, and relational databases. Moving or copying this data for centralized access and analysis is a resource-intensive task. That’s where data fabric comes in. 

Data fabric is an approach that creates a virtual layer on top of these data sources, providing a unified view of data without requiring you to physically move or copy this data to another repository. 

What is the Importance of Using a Data Fabric?

You’re probably surrounded by disconnected data sources—CRM systems holding customer data, marketing teams managing sales data, and accounting tools tracking financials.

When you need to integrate these sources for better decision-making and revenue growth, you need an architecture like data fabric.

Here are some key benefits of implementing one:

• The primary advantage is it eliminates data silos, providing seamless data access to teams across your organization. 
• Implements role-based data access controls and automated data lineage tracking capabilities. For those unfamiliar, data lineage tracks the flow of data—from its origin to how it’s transformed and where it’s ultimately stored.
• Creates a unified view of your data, enabling business users to explore it easily.
• Data management is more efficient with automatic schema management and active metadata management.
• Instead of physically moving data, the virtual layer enables a unified view, significantly reducing data transfer and centralized storage costs. 

Data Fabric Architecture

Data Integration Layer

Data fabric connects data sources through data connectors, creating a virtual layer for centralized access. It supports various data formats, including structured, semi-structured, and unstructured data, supporting both batch and stream ingestion.

Knowledge Graph

Unlike traditional tables, it has a knowledge graph to build logical relationships across different sources. This semantic layer leverages metadata to organize data, allowing users to understand the relationship between various sources in an organization. 

Data Orchestration

Data orchestration is the process of automating data flow across sources within the fabric architecture. It ensures data is processed and transformed in the right sequence to provide formatted and cleaned data for end-users.

Active Metadata Management

Instead of relying on traditional passive metadata, it activates metadata for AI/ML use cases. This enhances key processes like data discovery, orchestration, and governance, making them more intelligent and efficient.

How to Implement a Data Fabric? 

Define Business Objectives

Conduct business meetings with stakeholders and team members to identify data requirements and pain points. Also, discuss security, compliance, and governance requirements. This will help you create clear goals and a roadmap for successful data fabric implementation.

Assess the Existing Data

As a part of evaluating the current data landscape, identify data storage or accessibility gaps within the infrastructure. 

Also, identify data sources, storage systems, and integration platforms to understand your current infrastructure. This will help you determine the scale and flexibility of your existing architecture and identify areas for improvement.

Design the Data Fabric Architecture

As discussed, develop the fabric architecture addressing key components, such as data integration, data orchestration, and metadata activation.

• Data Integration: Once all the sources are identified in the above step, implement APIs, data connectors, or a third-party tool to connect them. 
• Data Orchestration: Develop data pipelines that automate the process of cleaning and transforming data in proper sequence.
• Data Catalog: Implement a data catalog that caches metadata for improved data discovery to end users. 

Data Governance

Data governance is a critical component essential for building a strong and reliable framework. It ensures that data is always accurate and reliable to use. It can be implemented through data governance frameworks, data catalogs, and metadata management.

Data Management

The data fabric is built to handle data at scale. Therefore, implement data management systems, such as data lakes, warehouses, and platforms, that organize data well and provide scalability according to usage.  

Data Accessibility 

The fabric architecture is known for simplifying data access to the right people within the organization. So, implement a universal data catalog, making it easier to point the necessary data to end-users. Leverage a self-service data portal that allows business users to access and analyze data without relying on IT teams. 

Ensure Data Security and Compliance 

• Use role-based data access controls to allow only the right users to access the appropriate data. 
• Implement robust security measures like multi-factor authentication to add an extra layer of protection to sensitive data. 
• Establishing robust governance principles such as GDPR, CCPA, and HIPAA is important to comply with privacy regulations. 

Use Cases and Examples of Data Fabrics 

Facilitates AI/ML

AI and ML are highly dependent on large, complex datasets for model training. It takes enormous time and resources to clean and transform data before feeding it to the model. Data fabric automates these steps, making data training ready. 

Real-Time Analytics

Making informed decisions is important—but making them fast also matters in today’s fast-paced world. Take customer support, for example. Answering customer queries in real-time makes customers happier than raising a ticket and taking days to get back to them. 

Data fabric enables instant decision-making through its real-time analytics features. It provides visibility into live data, allowing it to solve many business queries in a short time. 

Self-Service Analytics

Data fabric puts data in the hands of business users, removing the dependency on IT teams to extract insights. With a unified view and seamless integration into BI tools, non-tech users can easily generate reports and meaningful visualizations. 

Challenges in Implementing Data Fabric

Data Fabric vs Data Mesh

Data fabric and data mesh are popular and valuable, each with its own advantages. Data fabric centralizes data, providing a unified view of disparate data sources. In contrast, data mesh is domain-centric, handing over data ownership to the respective departments. 

Choosing between them isn’t always easy. Business leaders must understand their unique benefits and requirements to make the right call. The management should understand their unique benefits and requirements to make a wise call. Ideally, data fabric should be your approach if you want to primarily solve data silos issues. 

Isolated Systems

When data is scattered across disparate sources, integration becomes challenging without standard formats or APIs. In this case, you should manually standardize the data in each source, implement standard data access methods or connectors, and then you should unite the data. Active metadata management is also necessary to create a data virtualization layer that provides a unified view of fragmented data. 

Proper training and Adoption 

Employees and stakeholders may resist this new approach because it pushes them out of their comfort zones. Business users who are used to older tools might also refuse it because of the learning curve associated with it. Therefore, convincing them to switch can be challenging, especially when they are unaware of the concept and benefits of it. 

Best Practices for Implementing Data Fabric

• Understand your current data architecture thoroughly to find gaps that limit you from making the most out of your data. Then, decide to use data fabric, data lake, or data mesh. 
• Maintain data catalog and automated metadata management for simplified data discovery and access across your organization. 
• Provide proper training that helps your team members adopt and implement the fabric approach. 
• Keep scalability in mind throughout the shift. This will avoid growing data issues across the organization. 
• Set up real-time alerts for quality checks, integrity checks, and performance thresholds to track how well the fabric approach is performing in your organization.

Conclusion 

Data fabric is one of the best data architectures for businesses looking to centralize their data. It offers improved data quality and access. This clarity helps business users and analysts analyze data independently for faster decision-making. 

This article has covered the steps for implementing a data fabric, its associated challenges, best practices, and business use cases. Hevo’s robust features ensure smooth data integration, making it easier to manage and utilize your data.

Sign up for a 14-day free trial today and experience how Hevo can accelerate your data-driven decision-making and optimize your business processes.

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