The global disk-based data fabric market is projected to grow from USD 2.4 billion in 2024 to USD 5.5 billion by 2030, implying a 14.9% CAGR over 2026–2030 as enterprises seek unified control over increasingly fragmented data landscapes.
Disk-based data fabric is an architectural approach that virtualizes and unifies data stored across on-premises disk systems, private clouds, and multiple public clouds, providing consistent access, governance, and security regardless of where data physically resides. Market growth is fueled by surging data volumes from transactional systems, analytics, and IoT devices; the spread of hybrid and multi-cloud strategies; and the need to support real-time analytics while meeting stricter data privacy and security regulations, especially as many organizations struggle with disconnected, overlapping technology stacks.
A primary driver is the exponential increase and diversification of enterprise data, which overwhelms traditional, siloed storage and integration methods. Organizations now generate and consume data from ERP and CRM platforms, edge devices, logs, and external feeds, making unified data management crucial for analytics, AI initiatives, and regulatory reporting. At the same time, most enterprises intentionally blend private and public clouds and keep critical workloads on-premises, making a fabric layer that can span heterogeneous infrastructure a strategic necessity.
This environment is complemented by heavy investment in cloud and AI-ready infrastructure, as IT leaders modernize data platforms to support advanced analytics and machine learning. As businesses pivot toward AI-driven decision-making, disk-based data fabrics that can reliably feed high-quality, governed data into AI pipelines become central to extracting value from these investments, directly linking data fabric adoption to competitive advantage.
Yet, integrating data fabric solutions with entrenched legacy systems remains a major challenge that slows market expansion. Many organizations still rely on older storage arrays, mainframes, and legacy automation or manufacturing systems that were never designed for modern, interconnected data architectures, creating technical obstacles in connectivity, data mapping, and performance.
These integration complexities extend project timelines, raise implementation risk, and increase total cost of ownership, discouraging some enterprises from embarking on large-scale data fabric initiatives. Substantial customization, migration work, and compatibility testing are often required to bring disparate systems into a unified fabric, which can divert resources from other transformation priorities and delay ROI.
A key trend reshaping the market is the deeper integration of AI and machine learning into data fabric platforms to automate data operations. Intelligent fabrics increasingly handle tasks such as schema discovery, data quality checks, anomaly detection, policy enforcement, and optimized data placement, reducing manual effort and improving reliability. As AI agents and analytics depend on trusted, timely data, fabrics that embed these capabilities are gaining traction as foundational components of enterprise AI strategies.
Another important trend is the extension of data fabric architectures to the edge, where significant volumes of data are now generated by sensors, machines, and distributed devices. Edge-enabled disk-based fabrics support local aggregation, filtering, and initial analytics near data sources, sending only relevant or refined information back to core or cloud environments. This reduces latency and bandwidth usage and is critical for use cases such as industrial automation, autonomous systems, and smart city applications that require near real-time decision-making.
Within the market, security management is emerging as the fastest-growing segment. As cyber threats intensify and regulations like GDPR and HIPAA tighten, organizations need unified mechanisms to control access, encrypt data, monitor activity, and detect anomalies across diverse storage locations and platforms.
Disk-based data fabrics provide a centralized layer for defining and enforcing security and governance policies, offering consistent controls over distributed datasets and enabling real-time visibility into potential risks. By consolidating security management across silos, these solutions strengthen overall cyber resilience while simplifying compliance, making security-focused capabilities a key driver of segmental growth.
By Organization Size
Small & Medium Enterprises (SMEs)
Large Enterprises
By Deployment Mode
Cloud
On-premises
By Application
Security management
Risk management
Customer experience management
Governance management
Others
Key Companies
NetApp, Inc.
Dell Technologies Inc.
Hewlett Packard Enterprise Company
IBM Corporation
Pure Storage, Inc.
Cisco Systems, Inc.
Hitachi Vantara LLC
Huawei Technologies Co., Ltd.
Microsoft Corporation
Oracle Corporation
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