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Data fabric architecture:
an in-depth technical overview

April 1, 2025

Data fabric market state

30%

anticipated data fabric market CAGR during 2024-2032

Global Market Insights

400%

efficiency growth as a result of deploying a data fabric

Gartner

94%

of business leaders require more effective methods of utilizing data

Salesforce

Data fabric layers & architectural components

While data fabric architectures are unique, as they are tailored to the companies’ IT environments and specific data needs, most of them share seven core architectural components. At each layer, a data factory provides certain capabilities that enable consistent access, consolidation, and exchange of data.

Data modeling & interpretation Data access Data processing & analysis Data storage & operations Data lake Data warehouse Data marts Operational data store (ODS) Cloud data storage Metadata repository Graph database Data repositories & operational data sources Data integration & orchestration ETL/batch processing Stream processing Data virtualization Metadata Knowledge graph Data catalog Data quality Data security Data fabric architecture Data sources Data governance

Scheme title: Data fabric architecture diagram
Data source: ResearchGate

Data governance & security

With this layer in place, companies can implement robust data governance and security policies and practices to control access to enterprise data, define data access granularity, configure data refresh, mask or encrypt sensitive information, and so on. Data fabric employs an augmented data catalog to classify and inventory distributed data assets and capture and manage metadata, ensuring strict data governance.

Data lineage

Data lineage helps document the flow of data throughout its lifecycle, from source information to any data transformations that happened. It uses metadata at each step, sends it to the metadata repository, and updates the metadata in case of changes, thanks to metadata activation. This layer plays an important role in ensuring data reliability, as it enables businesses to verify data for accuracy and consistency, find the source of errors, and ensure compliance with data governance policies.

Data integration

This data fabric layer enables data preparation for the target repositories and the creation of a unified information view for analysis by consolidating it from disparate sources through a combination of data integration approaches, including ETL/ELT, API-based data integration, data replication, streaming, and data virtualization. Data connectors and API gateways link different sources where data is stored and enable a single representation of data.

Data modeling & interpretation

This component helps define the collected data, create data models, and add semantics to the data. Here, corporate data is organized with the help of knowledge graphs, and the relationships between data assets are defined. Semantic enrichment allows business users to interact with the data using business terms rather than complex SQL queries or needing to understand the schema of multiple databases.

Data orchestration

Within this layer, various workflow orchestration tools and services operate to coordinate data workflows and regulate how the data moves through various stages of processing, from collection to analysis and eventual actuation. Powered by machine learning algorithms and AI, a data fabric solution produces alerts and recommendations to users on how data should be better organized, integrated, and processed.

Data processing & analysis

The modeled and semantically defined data is transferred to a storage system or analytical solutions for further manipulation, querying, and analysis. It then can be presented in the form of reports and dashboards.

Data access

This data fabric architecture layer facilitates data delivery to multiple downstream consumers, whether they are applications, people conducting analytics and reporting, data catalogs, or data marketplaces, where business users can find the data they need. Monitoring tools within the data fabric help gauge the system’s health, providing capabilities for checking processing speeds and completed, failed, and canceled queries, locating bottlenecks, and ensuring that quality data moves smoothly throughout the system.

Data fabric use cases

A data fabric is a viable solution for diverse tasks, providing quick access to the needed data and facilitating self-service data usage for real-time insights.

Enterprise intelligence

Being environment-, platform-, and tool-agnostic, an enterprise data fabric architecture streamlines the consolidation of information from diverse internal and external sources. It’s well-suited for a multi-cloud or hybrid cloud enterprise, helping companies obtain a bird’s-eye view of their business with the possibility to drill down and drill through.

Operational intelligence

With the implemented data fabric architecture, large volumes of sensor data, security logs, clickstream data, etc., move without delays into storage repositories and then to analytics, data science, and visualization tools for further prompt usage.

Customer 360

Customer information is scattered across multiple corporate systems, including the CRM app, ecommerce platforms, and point-of-sale systems. The data fabric concept allows for its ingestion and aggregation into customer golden records (unified customer profiles), creating an all-encompassing view of customer demographics, preferences, activities, and purchase history.

Regulatory compliance

Relying on the data fabric’s embedded data governance capabilities, you can track where data comes from, how it was aggregated, who viewed it, when, and so on. With AI-powered data governance policy enforcement, a data fabric solution automates the classification of datasets, establishes rigid access controls, and supports sensitive data masking and encryption, which makes it suitable for ensuring stringent data security.

Data marketplace capability

Data lineage, a catalog-based knowledge graph of enterprise data, dynamic metadata management, and governed self-service capabilities transform the data fabric solution into an internal search system, enabling all authorized parties to access accurate and approved data.

Self-service BI & analytics

Thanks to flexible, AI and ML-enabled automated data integration, a data fabric democratizes data access for diverse teams, from data engineers and data scientists to sales managers and marketing specialists. This provides more freedom for business users to perform data activities without IT teams’ help.

AI/ML

Data fabric architecture accelerates and simplifies data preparation for further AI/ML model training. As ML engineers and data scientists have access to large amounts of data scattered across multiple systems, all while adhering to strict security regulations, they can effectively train accurate ML models with high-quality datasets without compromising data security.

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Data fabric applications across industries

As an industry-agnostic design concept, a data fabric solution can be implemented in any sector.

Healthcare

Healthcare organizations can use a data fabric concept to collect and integrate electronic health records (EHR), genomic research data, and IoT device data from wearables and in-hospital monitoring equipment to develop a comprehensive view of a patient’s health and treatment history and outcomes and ensure better compliance with complex regulations, such as HIPAA.

Healthcare

Retail

Data fabric solutions help access data from ecommerce systems, point-of-sale systems in physical stores, CRM platforms, mobile apps, and social media for retailers to track sales trends, customer preferences, and inventory levels in real time.

Retail

Finance

Banks and other financial institutions can build a data fabric solution to link investments, insurance, tax, and other corporate applications to collect information on banking and credit card usage, facilitate risk assessment and compliance monitoring, improve lending decision-making, and identify fraud in transactions.

Finance

Insurance

Insurers can use a data fabric architecture to connect personal information, risk profiles, and claims histories, providing prompt data access to personalize insurance products, make better pricing decisions, and identify fraudulent claims.

Insurance

Manufacturing

By integrating data from inventory management systems, IoT devices, production line sensors, supply chains, and RFID data solutions, manufacturers can craft an end-to-end view of their manufacturing process to spot early-stage bottlenecks, predict equipment failure, and inform product development teams based on market and social media data.

Manufacturing

Automotive

Automotive industry companies can use a data fabric concept to integrate, process, and analyze data from vehicle sensors, onboard diagnostics (OBD) systems, mobile applications, and third-party services and facilitate tasks like predictive maintenance, intelligent mobility and telematics, and crafting usage-based insurance plans.

Automotive

Data fabric benefits for different user types

Business units

  • Data-driven decisions made at the speed of business
  • Data democratization and more independence from IT teams
  • Automation of time-consuming manual data management processes
  • Real-time or near-real-time insights
  • Less dependence on IT teams

Data management teams

  • Higher productivity due to automated data management as well as reduced time for integration design, deployment, and maintenance
  • Faster resolution of data requests from business users or analysts
  • Intelligent integration of various data sources
  • Simplified data experimentation, including the development and deployment of machine learning models
  • Improved DataOps, incorporating diverse elements of the data lifecycle (from data discovery and integration to data transformation and movement)

Overall organization

  • Maximized value of organizational data
  • Breaking down data silos and creating a single source of reliable information
  • Enhanced data quality, security, and governance
  • A flexible data environment that evolves and scales in alignment with your business needs
  • Reduced data management and operational costs thanks to efficient data handling, delivery, and usage

Data fabric implementation steps

1

Auditing current data architecture

Analyzing existing systems, workflows, and data sources to integrate

Defining clear objectives and requirements, such as how data should be integrated from disconnected sources, how metadata should be collected and activated, what quality checks frequency to establish, etc.

Identifying data access requirements and data governance rules

Collecting and analyzing all types of metadata available across the organization

2

Planning & technology selection

Data fabric solution conceptualization

Choosing a deployment strategy, tools, and technology for collecting, managing, storing, and accessing data

Budget planning, taking into consideration infrastructure and software acquisition, development, and implementation costs

Defining the data governance framework, which encompasses metadata management, data lineage, and data integrity

3

Data fabric architecture development

Developing data ingestion pipelines

Designing and developing data storage solutions

Setting up data processing workflows

Building data analytics and data visualization solutions

Creating data catalogs and metadata management systems

Configuring data encryption, access control, data masking, and auditing

Putting in place monitoring systems, validation guidelines, and data quality checks

4

Testing & deployment

Testing data fabric performance, security, usability, and compatibility

Identifying bottlenecks and troubleshooting issues

Checking user access and permissions, as well as data recovery mechanisms

Setting up monitoring tools

5

Post-launch activities

Monitoring data governance compliance

Communicating the benefits of the data fabric to business and data teams to encourage individuals to use it for their data-related tasks

Creating documentation with guidelines for controlled and coordinated data fabric usage by existing and new employees

Encouraging regular and on-demand knowledge sharing through training sessions and workshops to increase user adoption after implementation

Gathering user feedback to spot inefficiencies and adoption roadblocks

Top platforms for data fabric solutions

  • 150+ on-premise and cloud data source connectors
  • A unified multi-cloud data lake connectivity that supports data of any format
  • Data shortcuts, virtualization, and mirroring capabilities
  • Native integration with the Microsoft 365 ecosystem
  • AI-driven Copilot support for report generation, data explanations, and insight summarization
  • AI-powered insights and graphics integrated into Microsoft 365 applications
  • Offering automated solution deployment with AWS Cloud Development Kit (AWS CDK)
  • Hot, warm, and cold storage tiers
  • A zero-trust approach with fine-grained attribute-based access controls (ABAC) and a modern identity, credential, and access management (ICAM) architecture
  • Data governance at scale, secure identity aggregation, and data access regulation at the column, row, and cell levels
  • Multi-availability zone (AZ) deployment using Elastic Load Balancers, ensuring high availability and fault tolerance
  • Data encryption at rest and in transit using TLS and client-side encryption
  • Support of artificial intelligence and machine learning to build, train, and deploy ML models
  • Native integration with Google services, for instance, BigQuery, Dataproc Metastore, and Data Catalog, as well as with open-source tools, such as Apache Spark and Presto
  • Data exploration workbench, data lineage, data quality, and data profiling capabilities
  • Data organization features (lake, zone, asset setup)
  • Highly specific access control, including row-level and column-level controls, column data masking to hide certain information for user groups, as well as the ability to configure and propagate IAM permissions
  • Generative AI-powered insights and semantic metadata search to discover data using the human language
  • Built-in data intelligence, business glossary, and global, faceted search to automate data discovery, classification, and metadata enrichment

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Build a robust data fabric solution with Itransition

At Itransition, we deliver full-scale data management services, crafting custom data integration, governance, storage, analytics, and visualization solutions to help companies collect, connect, and access data for informed business decisions.

Business intelligence

Business intelligence

We deliver robust business intelligence solutions, allowing enterprises to consolidate various types of corporate data and visualize it through customizable dashboards and role-specific reports.

Data warehousing

Our developers build data warehouses and other storage solutions to integrate data and keep it in a consistent and organized format for querying and analytical purposes.

Data analytics

We deliver powerful analytics solutions and integrate them into your business environment to let you analyze corporate data, create what-if scenarios, and predict upcoming trends.

Data management

Data management

We help businesses operate data throughout its lifecycle, from ensuring its high quality to migrating, visualizing, governing, and protecting data assets from cyberattacks and data breaches.

Optimize data integration & management with data fabric

Optimize data integration & management with data fabric

The data fabric concept proves to be an optimal solution for modern data-driven companies, allowing them to manage data assets regardless of their size, type, and location. However, deploying the enterprise data fabric architecture is a time and resource-consuming endeavor, the success of which depends on your data ecosystem maturity, the comprehensiveness of your data management strategy, and the tech expertise at your disposal.

Itransition experts make sure your data fabric project progresses smoothly, suggesting the best-suited tech stack and implementing the data fabric in line with your needs to augment and automate data integration and delivery processes. With the data fabric designed by Itransition, you can eliminate data silos and democratize data access, creating a holistic view of your enterprise information.

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FAQs

Data fabric architecture represents a data management and data integration design approach that leverages metadata to integrate data pipelines, on-premises, cloud, hybrid, and multi-cloud environments, and edge device platforms to view and govern data regardless of its location, type, and volume.

A data lake and a data warehouse are storage repositories that ingest and integrate data from scattered sources. They differ in the type of data they store. A data warehouse is built for structured data, while a data lake supports unstructured, semi-structured, and structured data. Data fabric vs data lake or data fabric vs data warehouse is not an either-or choice. A data fabric is a broader data management and integration design concept that connects disparate data sources, including data lakes and warehouses, to produce a unified view of corporate data.

Data virtualization is an underlying technology of a data fabric solution, creating a data abstraction layer. It allows the data fabric to integrate the required metadata and lets users view and interact with the data in real time without physically moving it from various sources. Thanks to data virtualization, the data fabric ensures extensive data governance and security.

A data fabric concept relies on centralized data management, providing dedicated features to enable dependable data access, consolidation, storage, and exchange across the organization. A data mesh, in contrast, focuses on decentralized ownership, making different business domains responsible for hosting, preparing, and serving their own data products and making decisions independently based on that data and their needs.

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