Data storage is the foundation of all computing, but the way we interact with it has changed dramatically. We began with simple file systems on local disks, a model that served personal computers well. But as data exploded in volume and applications demanded more, the underlying storage architectures had to evolve.
At the physical level, all storage is made up of blocks. Drives store data in fixed-size chunks, and higher-level systems give those chunks structure and meaning. From this concept, two dominant network storage architectures emerged: block storage and object storage.
Block storage delivers consistent, low-latency performance, making it the foundation for databases and virtual machines. Object storage, built for the scale and flexibility of the cloud, provides immense durability and efficiency for the massive volumes of unstructured data driving modern apps and services. While file storage still plays a role for shared user and application data, block and object storage power the core of today’s datacentres.
Understanding the differences between them is key to designing scalable, resilient, and cost-effective systems. Here’s how they differ, where each excels, and how to pick the right one.
What is block storage?
Block storage sits closest to the hardware layer. It presents a storage volume to an operating system as a logical disk, which the OS can partition, format with a file system (like NTFS, ext4, or XFS), and mount like a local drive.
Think of it as a warehouse full of numbered shelves. The storage system doesn’t care what’s being stored – it only tracks the shelf numbers. When the OS needs to write data, it says, “Put this on shelves 101 through 103.” To read it back, it just asks for those shelves. This direct access makes block storage ideal for workloads that require high performance and low latency.
Direct, random access
Any block can be read or written independently without affecting others. Modifying a single cell in a massive database means only the affected blocks are rewritten, not the entire file. That efficiency is why block storage underpins transactional workloads such as databases, VM disks (VMDKs, VHDXs), and other performance-critical systems.
File system dependence
Block storage itself doesn’t understand files or folders. It’s the file system layered on top that maps human-readable file structures to the underlying blocks. This tight integration allows applications to use block storage like a local drive through standard file I/O.
Strong consistency
Most enterprise block systems confirm writes only after data is safely stored or mirrored to redundant media. Mechanisms like journaling and write-ahead logging ensure that once a write is acknowledged, data is immediately and consistently available for subsequent reads – crucial for transactional integrity.
What is object storage?
Object storage takes a completely different approach. Instead of managing fixed-size blocks, it handles data as self-contained units called objects. Each object bundles together:
- The data itself (a photo, video, log, or backup).
- Rich metadata describing that data (type, size, creation date, tags).
- A globally unique identifier (object ID).
Think of object storage as a valet parking system for your data. You hand over a file, get a claim ticket (the ID), and don’t need to know where it’s stored. When you ask for it again, the system finds it instantly. Metadata serves as the notes on the ticket – describing what you stored and when.
Flat address space and API access
Object storage removes traditional folder hierarchies. All objects exist in a flat, logical namespace, typically organized into buckets or containers. This structure scales almost infinitely since the system doesn’t navigate directories. Objects are accessed programmatically via RESTful APIs over HTTP using commands like GET, PUT, and DELETE. This API-driven design makes it the natural choice for cloud-native environments.
Durability and redundancy
Durability is a core principle. Instead of relying on RAID, object systems use replication or erasure coding. Replication stores full copies of each object across multiple nodes or sites for immediate availability. Erasure coding breaks objects into fragments and parity blocks distributed across the cluster, allowing recovery even if several drives or nodes fail. It achieves high fault tolerance with less overhead than full replication.
Immutability and versioning
Many object systems support immutability, where objects cannot be changed or deleted for a defined retention period (WORM). This protects backups and archives from ransomware, accidental deletion, or, sometimes, rogue sysadmin. Versioning takes it further – when an object is updated, a new version is created instead of overwriting the old one. Previous versions remain accessible, ensuring full data history and recovery options.
For example, DataCore Swarm combines versioning with WORM policies to provide tamper-proof, immutable object storage.
Main differences between block and object storage
| Feature | Block Storage | Object Storage |
|---|---|---|
| Data organization | Fixed-size blocks managed through a file system (NTFS, ext4). | Self-contained objects with metadata and unique IDs in a flat namespace. |
| Access method | Mounted as a volume via iSCSI, Fibre Channel, or NVMe-oF. | Accessed via APIs (HTTP/S, S3, Swift) using object IDs. |
| Performance profile | Low latency and high IOPS for random read/write workloads. | High throughput for parallel or sequential workloads, higher latency. |
| Scalability | Scales within arrays or clusters but can be limited by controller or architecture. | Designed to scale across thousands of nodes and regions with minimal architectural limits. |
| Cost efficiency | Higher cost per terabyte due to performance hardware and SAN complexity. | Lower cost per terabyte using commodity hardware and erasure coding. |
| Common use cases | Databases, VMs, transactional systems, high-performance computing. | Backups, archives, media libraries, analytics, and cloud-native applications. |
How to choose
As usual, it’s not about which is “better,” but which fits your application. Ask yourself:
- How does the application access data?
Apps that mount a volume and use a file system (like SQL or VMware) require block storage. Cloud-native apps that use web APIs fit naturally with object storage. - What’s the I/O pattern?
Millions of small, random reads/writes per second call for block storage. Sequential writes or streaming workloads – like video or backup – benefit from object storage. - What kind of scaling do you need?
Need a few terabytes added to a SAN? Block storage handles that. Expanding across petabytes, regions, or clouds with durability requirements? That’s where object storage shines.
In most modern environments, both coexist: block storage for high-performance workloads and object storage for large-scale, durable data.
How DataCore and StarWind can help
For high-performance block storage:
StarWind Virtual SAN (VSAN) provides shared block storage with low latency and high availability for virtualized and hyperconverged environments. It mirrors data between nodes to keep databases and VMs online even during hardware failures.
DataCore SANsymphony adds performance and flexibility to existing SANs with features like storage snapshots, continuous data protection, dedupe and compression, auto-tiering, and intelligent caching.
For scalable object and file storage:
DataCore Swarm delivers scalable object storage for private clouds and archives. Its erasure coding ensures durability, and immutability features protect backups from tampering. Swarm’s S3-compatible API allows broad integration.
Conclusion
The discussion isn’t block vs. object – it’s block and object. Each serves a distinct role in a balanced storage strategy. Block storage powers high-speed, transactional systems, while object storage provides scale, durability, and data protection for the long term.
Understanding where each fits lets you design infrastructure that performs well today and scales for tomorrow.
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