Object storage is an approach to data storage meant to overcome the limitations that other file systems have in the face of the voluminous data generated by the growing number of users and devices in the world today. This storage system differs from others by organizing data using metadata rather than using a file hierarchy.
Contrastingly, traditional file systems store data in a hierarchy and follow a file path to locate requested data. This file storage process is usually the domain of the application using the data. Object storage, however, maintains a table of file metadata, cataloging many aspects and characteristics of these files. By using metadata as a file organization method, applications are unburdened from locating files, and instead request data from the object storage which uses the metadata table to locate the appropriate information.
This object storage approach shows its advantage by storing structured and unstructured data as well as the contextual information describing these objects. This shows up in the general case of data lakes, which store massive amounts of data. Object storage can beneficially remove data silos, it makes analyzing diverse datasets more accessible and easier.
How object storage works
Unlike file storage systems that organize data into a hierarchy, object storage stores data objects in a flat system. These objects then are referenced and retrieved using a Universally Unique Identifier (UUID) or Globally Unique Identifier (GUID) of 128-bit integers, significantly large enough to allow for a wide range of unique IDs, important because of the large number of objects stored.
Also unlike file storage systems, object storage uses metadata linked to data objects via UUIDs to find user requests. Files on the other hand retain very little metadata, relying on the hierarchy to locate requested data. The main advantage, object metadata allows for extensive (unlimited) description of the object, for example, video files can include lists of cast and crew. So, a user can locate video files by actor name.
Hierarchical file storage systems run into difficulties when scaled to capacities ranging in the petabytes. At this size, file storage performance degrades considerably, and the common solution is to split data into logical unit numbers (LUN), or collections of physical or virtual storage devices. While this improves storage performance, it also adds more complexity, until the complexity begins to cause difficulties as well. Instead, metadata search capabilities are used to overcome the scaling of data, and the ensuing technical challenges that crop up in file storage.
Object storage reliability is further enhanced by the technique of Erasure Coding. In essence, Erasure Coding volumes divide data into fragments, or shards, each is packed with error correction data, and then each are placed on different disks. By diversifying the data and packing it with redundant information, it ensures that files can be restored even after many disk failures. In short, Erasure Coding is a modern RAID system.
Benefits of object storage
The main benefit of object storage is the near unlimited scalability and storage of massive volumes of unstructured data. Metadata allows these systems to organize unstructured data in a highly searchable format.
Scalable modernized infrastructure emancipates cloud consumers from storage limitations
Efficient storage helps decrease costs and improve ROI
Future-proof data infrastructure against capital costs
Secure data and ensure compliance through automated features
Improve data visibility and accurate reporting.
Object storage use cases
Object storage use cases tend towards two general kinds, those workloads traditionally fulfilled by file, block, or tape storage devices, and the new innovative solutions that programmatically access object storage to derive insights. Traditional applications include backups, archiving, content storage, enterprise file servers, and collaboration. New innovations include mobile, social, IoT, cloud native apps, and AI/ML or cognitive apps.
Active Archiving — Active archiving is a space between deep storage archiving and real-time transparent data access, which tend to cause conflicting complications. Active archiving is typically configured as a tier architecture using tape storage behind disk storage, and replication. Object storage is used to flatten the tier architecture while adding resilience and simplifying administration.
Backup Repositories — Backup data is a practical concern for many organizations, which requires capacity management, space reclamation, and data replication. These data backup practices tended to use costly storage arrays and tape silos. With object storage, resilience features allow organizations to move away from replication and use low cost storage, with the benefit of a seemingly “endless” pool of storage.
Cognitive and analytic systems — With the advent of AI and machine learning followed the rise of cognitive systems that learn and reason based on human, experience, and environmental interactions. To do this, cognitive systems have been designed to interpret and understand unstructured data, found in images, speech, social media, etc. However, current data storage systems suffer performance issues with the type and scale of data that cognitive systems consume to determine their predictive responses. Object storage provides the metadata architecture that helps to facilitate the performance requirements of these complex and heavy workloads.
Enterprise Files Services — Enterprise file services have been a necessary tool for organizations for many decades, however, the dependence on RAID schemes using block storage has steadily introduced complexity in large-scale data protection efforts. Object storage removes this older, siloed approach in favor of a unified back-end, with metadata that allows easy access without locking into one solution, effectively achieving economies of scale.
Internet of Things Data Repository — The Internet of Things refers to the connection of a wide variety of devices that penetrate many aspects of life, each with a unique identifier, to eliminate the human element in gathering data. The amount of data generated by IoT systems tends towards the Big Data size, and storing this data requires a more robust system than what block or file storage can support. Object storage supplies the robust storage back-end where data of all varieties can be stored, retrieved, and archived.
When to use object storage
By comparing the key difference between block storage, object storage, and file storage, IT teams can narrow down the types of storage that will help them achieve their business goals. However, underneath each category there are many options, such as solutions that range from consumer grade block storage, to enterprise SAN block storage.
More expensive when volume goes up
Less expensive the more volume goes up
More expensive when volume goes up
Moderate manageability, more when configurations extend to SAN types
Metadata makes high volume searchability easier
Hierarchical storage makes smaller volumes highly manageable
Suitable for scaling
Highly suitable for high volumes
Not suitable for scaling
Highly accessible for large volumes
Highly searchable metadata
Highly suitable to real-time data transactions, performance use cases
Highly expansive data repositories, with less than real-time modifications
Workstations and smaller database applications, without plans to scale
Object storage vs. block storage vs. file storage
Object storage can be compared to two other common storage formats, block, and file storage. These formats aim to store, organize, and allow access to data in specific ways that benefit certain data applications. For instance, file storage, commonly seen on desktop computers as a file and folder hierarchy, presents information intuitively to users. This intuitive format, though, can hamper operations when data becomes voluminous. Block storage and object storage both help to overcome the scaling of data in their own ways. Block storage does this by “chunking” data into arbitrarily sized data blocks that can be easily managed by software, but provides little data about file contents, leaving that to the application to determine. Object storage decouples the data from the application, using metadata as a file organization method which then allows object stores to span multiple systems, but still be easily located and accessed.
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