Time to read: 4 minutes 56 seconds | Published: October 8 , 2025
Object storage What is object storage?
Object storage stores data as objects instead of files or blocks. Each object has data, metadata, and a unique identification for easy retrieval. Object storage is flat and scalable, unlike directory-based or block-based storage. It is great for storing massive volumes of unstructured data like video, backups, and cloud apps. It is ideal for modern data storage due to its excellent scalability, durability, and quick access over distant networks.
What are the benefits of object storage?
Object storage offers several benefits, making it a popular choice for managing large volumes of unstructured data. Here are some of the key advantages:
Object Storage Benefits:
Scalability & cost-effectiveness: Object storage can scale infinitely by adding new nodes, making it ideal for handling large datasets. Being software-defined allows the use of cheap commodity hardware with pay-as-you-go pricing.
Durability & Reliability: Data replication over many nodes or data centers ensures high availability, built-in redundancy, and little data loss.
Metadata management & flexibility: Configurable information improves organization, searchability, and retrieval for each object. Object storage handles tiny files and massive multimedia information.
Accessibility & integration: Supports HTTP-based RESTful APIs for seamless integration with cloud services, online apps, and current data architectures like AI, analytics, and Big Data.
Security and compliance: Encryption at rest and in transit, access restrictions, immutability, and audit logging ensure GDPR and HIPAA compliance.
Versioning and geographic distribution: This feature supports versioning for data recovery and auditing while allowing data to be stored across multiple locations for improved accessibility and disaster recovery.
Overall, object storage is a versatile and robust solution for managing large-scale, unstructured data in a cost-effective and efficient manner.
What are object storage use cases?
Here are some popular use cases and examples for object storage.
- Backup/Archive: Object storage is mostly used for backup and archiving. Backup and long-term archiving solutions must be dependable and affordable to preserve data from unintentional loss, cyberattacks, and hardware failures. Versioning, lifespan controls, and multi-location redundancy make object storage durable. Object storage is essential to company data management since it stores financial, legal, and consumer data to comply with GDPR and HIPAA.
- Content delivery and static media storage: For fast global access to static material like photos, movies, and documents, object storage is extensively utilized. CDNs employ object storage to cache files to minimize latency and increase user performance. Netflix and YouTube store and serve video material effectively using object storage. E-commerce websites employ object storage to store product photos, user-uploaded content, and static web assets for a smooth shopping experience.
- Analytics/Big data: Big data processing and analytics are built on object storage in data-driven companies. Object storage can store organized and unstructured data as a data lake, helping organizations evaluate patterns, forecast user behavior, and enhance decision-making. Amazon and other e-commerce platforms use AI-driven algorithms to propose products based on user purchase history, clickstream data, and behavioral analytics. Finance, healthcare, and IoT applications with big datasets benefit from object storage's scalability.
- Media storage/Streaming: Media and entertainment sectors require object storage to store and broadcast big multimedia assets. It can access high-resolution films, photos, and music without lag. Object storage helps Spotify and Apple Music store and deliver millions of music, podcasts, and audiobooks to millions of consumers. News agencies and photographers organize and archive digital media using object storage and metadata tagging for simple retrieval.
- Regulatory storage compliance: In banking, healthcare, and law, regulatory compliance is crucial. Immutability, encryption, and audit logging make object storage suitable for storing sensitive data and satisfying regulatory standards. To comply with SEC 17a-4, GDPR, and HIPAA, banks keep transaction logs, tax data, and audit trails in object storage. Object storage is favored for legal and regulatory reasons since it securely stores data for lengthy periods without unauthorized changes.
- Disaster recovery/Business continuity: Object storage is essential to disaster recovery methods for businesses facing cyberattacks, natural catastrophes, and system failures. Object storage helps firms recover rapidly from data loss by duplicating data across many sites. To backup databases, apps, and system settings, offer object storage-based disaster recovery options. This reduces downtime and speeds up important activities, ensuring company continuity.
For large companies that need to store, handle, and safeguard unstructured data, object storage is powerful and flexible. Its scalability, durability, and cost-effectiveness make it crucial across sectors for backups, content distribution, analytics, media storage, compliance, and disaster recovery. Object storage will enable current IT architecture by allowing organizations to store and retrieve data effectively as data grows rapidly.
What does HPE offer for object storage?
HPE offers a comprehensive suite of object storage solutions tailored to meet the demands of modern data-intensive applications. These solutions are designed to provide scalability, high performance, and seamless integration across various workloads.
- HPE Alletra Storage MP X10000: The HPE Alletra Storage MP X10000 is a software-defined, scale-out data system that combines data intelligence, high performance all-flash object storage, exabyte-scale capacity, and simple and intuitive management. This solution is engineered to accelerate data-intensive workloads, such as data lakes, digital repositories, and backup scenarios requiring rapid recovery. Its disaggregated multiprotocol architecture allows organizations to scale from terabytes to exabytes on the same hardware, eliminating scalability limitations and enhancing operational efficiency.
- HPE Solutions for Scality: In collaboration with Scality, HPE provides object storage solutions that serve as on-premises alternatives to public cloud storage. These solutions cater to a range of needs, from massive-scale deployments to single-node edge scenarios. They are particularly effective for media repositories, data, medical imaging, and high-performance computing archives. Additionally, they offer simple enterprise backup target storage, providing a cloud experience with lower risks and costs, along with more control and insight for unstructured data at any scale.
- Unified File and Object Storage Solutions: HPE's storage portfolio includes unified storage solutions that integrate file and block services, addressing the evolving needs of unstructured data. These solutions are built for scalability and flexibility, offering data protection, cloud extensions, and software-defined architectures. They are designed to harness unstructured data at any size, driving value through the right infrastructure.
HPE's object storage offerings provide robust, scalable, and efficient storage solutions that enable organizations to manage and derive value from their unstructured data effectively and support a wide array of applications and workloads.
Object storage vs file storage vs block storage
What is the difference between object storage, file storage, and block storage?
- Object Storage: Best for economical, scalable storage of large unstructured data (e.g., backups, media, and big data).
- File Storage: Ideal for collaboration and shared files, offering a user-friendly structure for accessing data.
- Block Storage: Suited for high-performance applications that require low-latency access, such as databases and VM storage.
| Feature | Object Storage | File Storage | Block Storage |
|---|---|---|---|
| Data Structure | Stores data as objects, each containing data, metadata, and a unique identifier. | Organizes data in a hierarchical file and folder structure. | Divides data into fixed-size blocks, each with a unique identifier but no metadata. |
| Storage Architecture | Flat address space, stored in a distributed pool. | Managed by a file system (e.g., NTFS, ext4, HFS+). | Provides raw storage volumes, requiring a file system to be installed. |
| Metadata | Supports rich metadata for efficient organization and search. | Limited metadata (file name, permissions, timestamps). | Minimal metadata, primarily tracks block locations. |
| Access Method | Accessed via HTTP-based RESTful APIs. | Accessed using file paths and mounted to operating systems. | Accessed via low-level protocols like iSCSI, Fibre Channel, or FCoE. |
| Performance | Optimized for large-scale, unstructured data storage rather than high-speed transactions. | Moderate performance, depending on network and system load. | High performance with low latency, suitable for high IOPS workloads. |
| Scalability | Highly scalable by adding more storage nodes, supports massive data volumes. | Limited scalability, as performance can degrade with large numbers of files. | Scales well but requires additional management and hardware upgrades. |
| Durability & Availability | Data is distributed and replicated across multiple nodes/data centers for high availability. | Availability depends on the underlying storage system and network setup. | Typically includes redundancy features like RAID, snapshots, and backups. |
| Cost-effectiveness | Cost-efficient, using commodity hardware with pay-as-you-go pricing models. | Moderate cost; can become expensive at scale. | Can be costly due to hardware and management requirements. |
| Best Use Cases | Cloud storage, backups, multimedia storage, big data, and analytics. | File sharing, collaboration, home directories, and content management. | Databases, virtual machines (VMs), transactional applications, and high-performance workloads. |
FAQs
What are the drawbacks or limitations of object storage?
Small random writes or file-system semantics with object storage typically experience higher latency and lower IOPS compared to block or file storage. Managing metadata, versioning, and consistency may be more complex, while frequent access and data retrieval may be more costly.
How does object storage ensure durability and data protection?
Object storage offers replication across devices, nodes, and regions, as well as integrity checks (checksums). Versioning and immutable snapshots or retention policies ensure that data is always protected.
How do I manage access, permissions, and security in object storage?
Essential security controls include access controls at bucket or object level (RBAC, IAM, ACLs), encryption at rest and in transit, strong authentication and identity management, support for immutable/worm policies or legal hold, and audit logs and versioning to track, delete, and restore objects as needed.
How do I size or scale object storage for large workloads?
When considering size and scale for an object storage solution, it’s a good idea to estimate current plus projected data growth (unstructured tends to grow fast), volume of objects (small vs large objects affect overhead), and metadata size and indexing performance. You will also want to consider the amount of throughput (read/write bandwidth) required and geographical distribution or redundancy (replication/geo-replication) needs.
How is metadata used in object storage and why does it matter?
Metadata enables rich indexing, filtering, search, lifecycle rules, retention policies, cost tracking, and governance. Without strong metadata, retrieval and manageability suffer, especially at scale. Custom metadata can also be used to accelerate AI initiatives, such as Retrieval-Augmented Generation (RAG).
Can object storage be used for AI/ML workloads, RAG, or big data training?
Yes. Object storage often forms the data lake foundation: large datasets for training, storage for model artifacts, logs, and embeddings. Cold or historical datasets are stored in object storage, while hot/frequent access parts are cached or placed in faster tiers.
What are best practices for deploying and operating object storage?
Best practices for deployment include using lifecycle policies to automatically move data across tiers and versioning and immutable objects for protection. For optimal operation, it’s important to organize objects and metadata well (naming, namespaces, tags), monitor performance (latency, throughput, request rate) and plan for scale and manage metadata and indexes efficiently.
What are “object storage classes” or “tiers,” and how do I choose among them?
Object storage classes or tiers let you balance cost vs. performance and access frequency. Common tiers include hot (frequently accessed) data, warm (infrequently accessed) data, and archived or cold data storage for long-term retention. When choosing appropriate tiers, consider how often data is accessed, retrieval latency requirements, durability guarantees, and cost of retrieval or egress.
What consistency models do object storage systems support?
Some offer strong consistency (e.g., after write, reads see latest version), others eventual consistency, depending on architecture and region replication. It’s important to check how the particular solution handles consistency, particularly for read-after-write or overwrite scenarios.
What protocols/APIs are used with object storage?
Object storage systems are typically accessed via HTTP/HTTPS REST APIs. Many are compatible with Amazon S3 APIs. Tools, SDKs, and third-party ecosystems (data movement, big data, analytics) integrate through those APIs.