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Data fabric architecture:
an in-depth technical overview
February 19, 2026
Data fabric architecture is a data management framework that facilitates seamless data integration, curation, and governance across complex data environments, providing a unified view of corporate information scattered across disparate systems and improving data availability for analytics and AI initiatives.
With a proven track record in data integration, data analytics, and business intelligence, Itransition offers data management services to help companies set up the right data fabric infrastructure and ensure its scalability, efficiency, and security.
Data fabric market state
21.9%
the anticipated data fabric market CAGR during 2026–2034
Fortune Business Insights
50%
of CEOs say that they have disconnected data platforms and technologies
IBM
55%
of companies invest in data management and architecture
Deloitte
>61%
of large businesses have started implementing data fabrics
Global Growth Insights
Key components of data fabric architecture
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.
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 active 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 a data fabric architecture in place, big data volumes, including sensor data, security logs, and clickstream data, move without delays into storage repositories and then to analytics, data science, and visualization tools for further use.
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.
Do you want to build a reliable and scalable data fabric architecture?
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.
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.
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.
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.
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.
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.
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
- Effortless integration of new 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 truth
- Enhanced data quality, security, and governance
- Flexible data environment that evolves and scales in alignment with business needs and data workloads
- 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 Azure, Power BI, Microsoft 365, and other Microsoft services
- 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
Start your data fabric project now
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
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
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
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.
Looking for a trustworthy data management partner?
FAQs
A data lake and a data warehouse are storage repositories that ingest and integrate data from various sources
and are designed for maintaining different data types. An enterprise data warehouse is built for structured data, while a data lake supports structured, semi-structured, and unstructured data.
A data fabric represents a design approach that integrates data pipelines and on-premises, cloud, hybrid, and multi-cloud
environments. It uses ML, active metadata, application programming interfaces (APIs), and other technologies to
create a unified data view and facilitate the enforcement of uniform data governance and security policies regardless
of data volume, location, or type.
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.
Data virtualization is an underlying technology of a data fabric solution, creating a data abstraction layer and integrating the required metadata from data sources instead of physically moving data into a storage system. This capability enables real-time data access while offering improved 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.
Data fabric helps organizations manage data assets across diverse data architectures, including centralized architectures that rely on unified platforms, such as data lakes or data warehouses, decentralized architectures where data is distributed between systems across business functions, and hybrid architectures.
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