Object storage, also known as object-based storage, is a data storage architecture that manages vast amounts of unstructured data by segmenting it into pieces called objects and storing them on-premises or in the cloud.
Unstructured data includes photographs and videos on Facebook, emails on Outlook, audio files on Spotify and even files in online collaboration services like Dropbox. Object storage, unlike other architectures, treats pieces of unstructured data as distinct units, complete with data, metadata and a unique identifier that analytics software can utilize for easy access and retrieval. In contrast to hierarchical or tiered storage, this results in a flat data architecture where users can retrieve and analyze any object in the network, regardless of the file type, based on its properties and function.
Why is object storage important?
Modern businesses generate and analyze massive amounts of unstructured data, including photos, videos, emails, web pages, sensor data, audio files, and other types of digital content that do not fit easily into traditional databases. As businesses expand, they have to oversee rapidly expanding but isolated pools of data from various sources that are used by a wide range of applications, business processes, and end users.
In a Seagate-sponsored whitepaper titled “Data Age 2025,” market research firm IDC predicted that unstructured data would account for up to 80% of all data worldwide by 2025. As a result, finding efficient and affordable ways to store and manage it has become problematic.
Object-based storage has become the preferred method for storing static content, data archives and backups. Because of its scale-out capabilities, object storage has few scalability limitations when compared to traditional file or block-based storage. Object storage also improves data durability and resiliency by storing objects across multiple devices, systems and even data centers and regions. Cloud object storage allows data to be accessed from anywhere.
One drawback of object storage is that it isn't meant for transactional data because it wasn't made to take the place of network-attached storage (NAS) for file access and sharing. It also doesn't support the locking and sharing features that are required to keep a single, up-to-date version of a file.
What are the use cases for object storage?
According to AWS, customers use object storage for a wide range of applications. Common usage scenarios include:
- Analytics: Cloud object storage allows you to collect and store virtually unlimited amounts of data of any type, as well as perform big data analytics to gain valuable insights into operations, customers and the market.
- Data lake: With cloud object storage, you can increase storage from gigabytes to petabytes of content in a seamless and nondisruptive manner, paying only for what you need. It offers scalable performance, user-friendly features, native encryption and access control capabilities.
- Cloud-native application data: Cloud-native applications use technologies such as containerization and serverless to meet customer expectations in a timely and flexible manner. Object storage enables you to add any amount of content and access it from anywhere, allowing you to deploy applications faster and reach a larger audience.
- Data archiving: Cloud object storage can be used to replace on-premises tape and disk archive infrastructure with solutions that provide increased data durability, faster retrieval times, improved security and compliance, and greater data accessibility for advanced analytics and business intelligence.
- Rich media: By combining storage classes and replication features, you can design a cost-effective, globally replicated architecture for delivering media to remote users.
- Backup and recovery: Object storage systems can be configured to replicate content, ensuring that if a physical device fails, duplicate object storage devices become available. This ensures that your systems and applications continue to function uninterrupted.
- ML: Machine learning requires object storage due to its scale and cost efficiency, as a production model typically learns from millions to billions of example data items and produces inferences in as little as 20 milliseconds.
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