As IoT devices and real-time applications continue to grow each year, most of the data is no longer created inside centralized data centers. Instead, it’s produced and consumed at the edge, closer to where sensors, users, and devices operate. Various sources estimated that in 2025, more than 50% of all enterprise data will originate at the edge, an astonishing total of around 90 zettabytes.
Edge storage keeps that data local. Instead of sending everything back to the core data center, it stores and processes information nearby, reducing latency, cutting bandwidth use, and improving reliability.
Why is edge storage important?
The traditional centralized model worked well when most applications could tolerate a few hundred milliseconds of delay. Today’s workloads: self-driving cars, autonomous systems, smart manufacturing, real-time analytics – can’t. Sending continuous sensor or video data to a central site for processing creates latency, consumes bandwidth, and risks downtime if the connection drops.
Edge storage decentralizes that model. Data is captured, processed, and stored locally or near the point of generation. This approach helps to:
- Cut latency and improve responsiveness for applications and services.
- Reduce bandwidth use by filtering and summarizing data locally.
- Keep operations running autonomously even if the central link is interrupted.
That’s why edge storage has become foundational for modern distributed systems.
What technologies are used in edge storage?
Edge storage solutions are built on a variety of technologies, often utilizing a clustered architecture to ensure high availability and performance. These technologies are designed to be efficient in resource-constrained environments at the edge. Key components include:
- Direct-Attached Storage (DAS): Simple and cost-effective, DAS connects storage directly to a single server. While effective for small-scale deployments, it can lack the scalability and redundancy of more complex setups.
- iSCSI: This protocol enables the use of existing Ethernet network infrastructure to transfer data, making it a flexible option for building a storage area network (SAN) at the edge without needing dedicated fiber channel hardware.
- NVMe-oF: For applications demanding the highest performance, NVMe over Fabrics (NVMe-oF) provides a way to remotely access high-speed NVMe storage with minimal latency, making it ideal for real-time applications.
- High Availability (HA): A critical component of any edge storage solution, HA ensures that data remains accessible even if a hardware component fails, guaranteeing business continuity.
- Hyperconverged Infrastructure (HCI): Though it’s not really a single “technology” but an architectural approach, HCI combines compute, storage, and networking into one unified platform. It simplifies management, reduces physical footprint, and is especially useful for edge deployments where space and resources are always limited.
Benefits of edge storage
Edge storage provides both technical and operational advantages that directly translate into better performance, efficiency, and reliability across distributed environments.
- Lower costs
Instead of transmitting raw data to a central site (or to the cloud), edge systems process and filter it locally. Only relevant results, like exceptions, summaries, or alerts are sent upstream. This drastically reduces network usage and lowers ongoing bandwidth costs. - Real-time response
Keeping data close to where it’s generated allows for immediate processing and decision-making. In industrial automation even milliseconds do matter a lot, so edge storage ensures that control systems can act instantly rather than waiting for data to travel to and from the cloud. - Scalability
Edge deployments can start small, using commodity hardware, and grow gradually as data volumes increase. This modular approach avoids large upfront investments and enables predictable scaling as new devices or locations come online. - Regulatory compliance
Many industries must keep sensitive data on-site for privacy or legal reasons. Edge storage makes this easier by processing and storing data locally while still integrating with centralized analytics. For example, hospitals can store patient data securely within local systems while sharing only anonymized insights. - Business continuity
If connectivity to the central data center fails, edge nodes continue operating autonomously. Local storage ensures that factories, retail systems, or smart city sensors don’t stop collecting and using data during outages.
Typical edge storage use cases
Edge storage is a versatile technology with applications across numerous industries.
- Telecommunications: Edge storage is widely adopted in virtualized radio access networks (vRAN) and 5G infrastructure, enabling low-latency data processing at cell sites.
- Manufacturing: In smart factories, edge storage handles massive amounts of data from IoT sensors on the factory floor, enabling real-time quality control, predictive maintenance, and operational optimization.
- Security and surveillance: Edge storage is deployed allowing to process video streams locally and analyze tons of footage from cameras in real time. Only relevant clips or metadata are stored or sent upstream. This drammatically reduces overall storage needs and saves bandwidth.
- Retail: Retailers use edge storage to process data from point-of-sale systems, inventory trackers, and video surveillance cameras. This allows for instant insights into customer behavior, inventory levels, and store security without relying on a central cloud.
- Healthcare: Hospitals and clinics can use edge storage to manage patient data, medical imaging, and real-time monitoring devices, ensuring that critical information is accessible instantly for doctors and staff.
- Transportation: From managing data in autonomous vehicles to monitoring traffic flow in smart cities, edge storage is essential for processing the data from sensors and cameras, enabling quick decisions and a seamless user experience.
What we have to offer?
StarWind and DataCore deliver a portfolio designed for both edge and core environments, offering high availability, flexibility, and simplified management.
StarWind HCI Appliance – A compact, hyperconverged platform combining compute, storage, and virtualization for remote offices, IoT sites, or branch locations.
StarWind Virtual SAN – A software-defined storage solution that enables the deployment of compact 2-node HCI clusters at the edge on any supported x86 hardware.
DataCore Swarm – Object storage that provides a secure and scalable archive for edge-generated data.
DataCore Pulse8 – Kubernetes-native high-availability storage for applications running at the edge or in hybrid environments.
Together, these solutions provide an edge-to-core architecture that ensures consistent performance, resilience, and control across distributed infrastructures.
Conclusion
As organizations generate more data outside traditional data centers, edge storage has become essential for handling it efficiently. Processing and storing data locally reduces latency, lowers bandwidth use, and keeps critical systems running even when connectivity to the core is lost.
Together, technologies like software-defined storage and hyperconverged infrastructure make it practical to deploy reliable, high-performance storage at remote or space-constrained locations. Whether used in manufacturing, healthcare, retail, or telecom environments, edge storage is now a key part of modern IT design, bridging the gap between local processing and centralized management.
