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Data Fabric 2024: Modern Data Integration Components Guide

Gulbahar Karatas
Updated on Jan 2
9 min read

Data management and data integration are critical components of any organization’s digital transformation strategy. In today’s omnichannel business environment, organizations must access and analyze large-scale data from various sources in real-time. However, traditional data management approaches can frequently be too slow for these requirements. Data fabric architecture can help overcome these issues.

For organizations looking towards digital acceleration, data fabric can be highly beneficial. Since it’s a relatively new concept, many business leaders may not know about it (Figure 1). In this article, we explore the data fabric, its use cases, and its benefits.

What is data fabric?

Data fabric is a single and consistent data management framework that helps organizations manage their data. The purpose of data fabric is to reduce the complications of data management. It helps organizations solve complex data problems by eliminating inefficient and manual data integration processes and provides business-ready data for analytics. It enables users to access and share data seamlessly, regardless of where it is stored.

Data fabric software architecture that brings together and connects enterprise data from different sources, such as: 

A data fabric also lets organizations automate tasks like: 

  • Data replication.
  • Data governance.
  • Data security related to data management and integration.

What is a data fabric architecture?

A data fabric architecture refers to the overall data fabric design and structure. It includes data fabric components, technologies, and principles, as well as their integration and configuration to support a: 

  • Unified.
  • Flexible.
  • Scalable data management platform.

How do a data fabric architecture and data fabric fit together?

A data fabric gives organizations a single, flexible, and scalable platform for managing structured and unstructured data that they can use to access and analyze in real-time. Organizations can create a data fabric by implementing a data fabric architecture. The architecture enables organizations to use the platform to access and analyze data efficiently.

Why is data fabric important now?

Challenges such as lack of data access (i.e., data is accessible to the users who need it) and the complexity of data integration prevent organizations from maximizing the value of and fully leveraging their data. Traditional data integration is no longer sufficient to meet business requirements such as universal transformations, real-time connectivity, etc. Integrating, processing, and transforming organizational data with data from multiple sources is a challenge for many organizations.

Data fabric provides users with comprehensive data access in real-time; it can be visualized wherever users are located. Users can use data fabric to simplify data governance and management in multi-cloud data landscapes.

How do companies benefit from a data fabric?

  • Automate data governance: It automatically applies corporate policies to data and delivers trusted data. 
  • Facilitate data integration: It simplifies access to all data and accelerates data delivery within the organization by automating these processes.
  • Eliminate data silos: A data silo is data held by one group and not fully accessible to others. Data fabric is a unified data management framework for collecting and accessing data. It makes data accessible to other groups in the same organization.
  • Increased data managing compliance: It provides a single environment for all data and centralizes data management and governance.
  • Accelerate the digital transformation process: Data fabric reduces data integration issues, increases data quality, and simplifies data governance, sharing, and management by eliminating the need to use multiple tools. It provides you with a single, comprehensive view of your company’s data. It can accelerate your digital transformation process by maximizing the value of your data.

What are the components of a data fabric?

Data fabric is not merely a network. Typically, a data fabric consists of the following major components: 

  1. Data management layer 
  2. Data security layer
  3. Data access layer
  4. Data consumption layer

1. The data management layer

The data management layer is responsible for data organization and management across numerous storage resources. It can have data management capabilities like: 

  • Tiering.
  • Deduplication.
  • Compression helps to optimize storage resources and cut expenses.
  • Replication. 
  • Migration which can move data across different storage resources efficiently.

2. Data protection layer

The data protection layer’s job is to ensure that data is always safe and accessible. It can include data management features such as: 

  • Backup 
  • Disaster recovery
  • Archiving 

Additionally; it can include security features like data encryption to protect your data from unauthorized access or breaches.

3. Data access layer

Data access layers allow applications to access and retrieve data from diverse sources like cloud environments and data lakes. This layer can unify data access regardless of the data source. It can provide application programming interfaces (APIs) and interfaces for: 

  • Querying.
  • Inserting.
  • Updating.
  • Deleting data.

4. Data consumption layer

The consumption layer is in charge of controlling how applications and systems use data. It is typically made up of interfaces and APIs that allow programs and systems to access and use data as needed. 

The consumption layer can integrate data consumption from many data sources. This layer provides the following features:

  • Unified view of data: Unifies data from all sources, regardless of format or location.
  • Querying and analysis: Allows for efficient data querying and analysis by ensuring that data is correctly indexed and optimized.
  • Data security: Security and access controls are provided to ensure that only authorized business users and apps can access the data.
  • Performance optimization: Performance is improved, and data duplication is eliminated via:
    • Data caching, where data can be stored locally in a cache, minimizing the need for repeated queries to the same data.
    • Data virtualization to access and integrate the data without needing physical migration or duplication.
    • Data federation: Allows organizations to access data from different sources as if they were in a single location via middleware or connectors.

Data fabric vs. data lakes vs. databases for operational workloads

Because they store and manage data, “data fabrics,” “data lakes,” and “databases” can be confused. However, their use cases and features can differ:

  • A data fabric is a way to connect and manage enterprise data from various sources and technologies. It is often used in large, complex environments with many different kinds of data and systems.
  • A data lake, or cold data, is a central place where business users and data engineers can store data at any size. It is made to handle large amounts of data and is often used to store raw data that will be analyzed later.
  • A database is a way to store and manage information in a structured way, usually with software. It is designed for fast data querying and retrieval and is usually used for operational tasks like running a website or an app.

Data fabric vs. data lakes vs. data databases comparison table

In Table 1, you can find a summary of some similarities and differences between data fabrics, data lakes, and databases.

FeatureData fabricData lakesDatabases
Data integration
Allows data from different sources to be merged and accessed in real-time.
Allows all structured and unstructured data of any size to be stored.
Allows structured data to be stored and worked on.
Data governanceOffers data management and compliance.There are no specific elements for data governance.Provides for the management and compliance of data.
Data securityOffers security and access control.No special features to protect data.Offers security and access control.
Performance OptimizationCaching, data virtualization, and data federation.No performance enhancements.Optimized for certain uses.
Real-time ProcessingEnables real-time data for decision-making.Not optimized for real-time data processing.Optimized for certain uses.

Table 1: Data fabric vs. data lake vs. databases.

Why use data fabric? Key data management benefits of a data fabric architecture

Data fabric can enable organizations to manage data regardless of where it is stored. Data fabrics can provide the following data management benefits:

1. Data accessibility

A data fabric lets organizations access and manage data from different sources in a unified and consistent way, including: 

  • databases
  • data lakes
  • cloud storage. This can make it easier to get to and use data for business analytics and other uses.

2. Data governance

A data fabric enables organizations to enforce governance policies across their data pipelines. Policies regarding: 

3. Data integration

A data fabric can automatically combine structured and unstructured data from different sources into a single, unified view.

4. Data agility

Data fabric solutions can make it easy for organizations to change their data architecture quickly as their needs change. This can help businesses adapt to changes in the business world and stay competitive. 

For example, suppose an organization needs to add new data sources, like IoT devices or social media. In that case, the data fabric can integrate their data into the existing architecture.

5. Data scalability

A data fabric solution can allow organizations to scale their data infrastructure to meet the demands of: 

For example, an organization may store customer data in multiple databases and file systems. A data fabric can bring all of this data together and use it for analysis.

6. Cloud-integration

A data fabric technology can allow organizations to move and manage data across:

  • multi-cloud
  • hybrid cloud
  • and on-premises environments

This can provide flexibility and reduce vendor lock-in.

Data fabric does not need to collect and analyze all forms of metadata

Data fabric helps firms manage and combine data from several sources. However, it does not require collecting and analyzing every piece of information. How useful the data fabric is for collecting and analyzing metadata depends on the use case and needs of the organization. For example, metadata can be useful in the data fabric:

1. Data governance

Organizations can improve governance and compliance across unstructured and structured data sources by collecting and analyzing metadata. Metadata can trace data lineage, ownership, and appropriate use.

2. Security

Metadata can play an important role in security, both when it is stored (at rest) and transported (in motion). Metadata can encrypt data, implement access limits, and monitor data activities. Metadata, for example, can be used to trace who has viewed a specific file and when, as well as to detect sensitive data and apply data masking.

3. Data analytics

Metadata can be used to analyze and learn from diverse data sources. Metadata can help understand data schema, combine data from diverse sources, and analyze streaming data in real-time.

Data fabric vs. data virtualization vs. data federation

Data fabric, data virtualization, and data federation are sometimes confused. In this section, we explain these terminologies.

What is the difference between data fabric and data virtualization?

Data fabric is a concept that is confused with data virtualization. Both are data architectures used to manage organizational data. Data virtualization is the fastest way to integrate solutions for transforming data sources to gain real-time insights. For more detailed information, you can check our related article. On the other hand, data fabric is a management architecture that provides comprehensive management for broader use cases such as IoT analytics, data science, and customer 360. Data virtualization contributes to data fabric architecture work better.

Data virtualization

The image depicts virtualization. It processes data from data sources for agile analytics.

Figure 2. Virtualization connects data sources to analytics.1

Data virtualization is a method that allows businesses to access and use data as if it were stored in a single location, even if it is distributed across multiple data sources. This is accomplished by constructing a virtual layer on top of the underlying data source, which gives a consistent and uniform data representation.

Data federation

The image depicts data federation. The difference of data federation is that it transfers current data from databases for business intelligence.

Figure 3. Data federation connects data in databases to business intelligence.2

Data federation is similar to data virtualization since it gives a single, consistent view of the data, but the way it does this is different.

Data federation connectors

In data federation, the data stays in its original place and is accessed through a set of middleware or connectors. These connectors make it possible for applications and systems to access and use data from different data sources, but the data stays where it came from. The connectors are in charge of putting the data from the different sources into a common format and showing it in the same way every time.

Data virtualization vs. data federation

In contrast, virtualization creates a virtual layer that sits on top of the underlying data sources and provides a unified and consistent view of the data. The data is typically moved or replicated into the virtual layer and accessed and used from there.

Data fabric vs. data virtualization vs. data federation comparison table.

Table 2 summarizes the differences between data fabric, virtualization, and data federation.

FeatureData fabricData virtualizationData federation
Data managementOffers a versatile, scalable, and unified platform.Provides a virtual layer.Provides connectors.
Data integrationIntegrates data from multiple sources.Allows data integration.Allows data integration.
Data locationMerges multiple data sources.Virtualizes data access.Connectors retrieve data from original location.

Table 2: Data fabric vs. virtualization vs. data federation.

How does data fabric function with AI/ML?

Artificial intelligence (AI) and machine learning (ML) can work with data in several ways. Here are a few examples:

  • Data preparation: Data fabric can collect and combine data from several sources to give AI and ML models a single view for data analysis. Data fabric can also be used for data pre-processing and cleaning to improve data quality for AI and ML models. 
  • Real-time data analysis: Data fabric feeds AI and ML models real-time data. This can provide real-time model-based decision-making and action.
  • Data governance: Data fabric can handle data governance and compliance to ensure ethical and lawful data use in AI and ML applications.
  • Data security: Data fabric secures data at rest and in motion across all sources. AI and ML applications must protect sensitive data.
  • Hybrid and multi-cloud: Data fabric can combine data from on-premises, cloud, and edge settings to create a flexible and scalable AI and ML architecture.

Real-world data fabric can perform instant complex queries 

Data fabric can speed up query processing via partitioning, indexing, caching, and materialized views. Additionally, data fabrics can process big data using distributed processing and parallelism. Here are some examples:

  • Real-time analytics: In financial services and e-commerce, a data fabric can combine data from numerous sources, execute complex calculations, and produce near-real-time results. 
  • Internet of things (IoT): Data fabrics can analyze and respond to sensor data in real-time in IoT use cases. In a smart city, a data fabric can assist in analyzing sensor data from traffic cameras and lights to optimize traffic flow and eliminate congestion.
  • Ad-hoc reporting: A data fabric can help you make ad-hoc reports quickly by combining data from different sources and running complex calculations. For example, a retail company can use it to quickly report sales by category and region for a certain time period.

If we have missed anything, feel free to add your comments.

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Gulbahar Karatas
Gülbahar is an AIMultiple industry analyst focused on web data collections and applications of web data.

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