Object Storage Vendors, Benefits, Use Cases And How It Works

We will talk about object storage vendors, its advantages, and real-world applications in this blog.

What is Object storage?

A computer data storage architecture called object storage, sometimes referred to as object-based storage, is made to manage substantial volumes of unstructured data. It organises data into discrete pieces, each of which is accompanied by metadata and a unique identifier that can be used to find and retrieve the data unit. This is in contrast to other architectures.

Although they can be kept on-site, these units or items are usually kept in the cloud, which allows for easy access from any location. There are limited restrictions on object storage’s scalability because of its scale-out capabilities, and storing vast amounts of data is less expensive than with other solutions like block storage.

Email, media and audio files, web pages, sensor data, and other digital content that is difficult to store in standard databases make up a large portion of today’s unstructured data. Therefore, it has become difficult to develop economical and effective ways to handle and store it. The recommended approach for storing backups, data vaults, and static content is increasingly object storage.

Object storage defined

A data storage architecture known as object storage divides unstructured data into units called objects and keeps them in a data environment that is fundamentally flat. Applications may easily access and retrieve each object by using its unique identifier, metadata, and data.

How does object storage work?

With object storage, a file’s data blocks are stored as a single object in a flat data environment called a storage pool, together with any pertinent metadata and a unique identifier.

item storage systems will use the metadata and unique identifier to locate the necessary item, like an image or audio file, when you wish to retrieve data. Additionally, metadata can be customised to offer more information that can be used for other reasons, like data analytics retrieval.

RESTful APIs, HTTP, and HTTPS can be used to find and get objects and query object metadata. Finding the precise data you require is quick and simple since objects are kept in a global storage pool. Additionally, even for petabyte or exabyte demands, the flat environment allows for rapid scaling. Storage pools can be dispersed over several locations and object storage devices, providing for infinite scalability. As your data expands, you just add more storage devices to the pool.

Object storage is a perfect fit for storing unstructured data on cloud architecture because of its advantages, such as its elasticity and scalability. What exactly is cloud object storage, then? Object-based storage as a cloud service that is available whenever needed is exactly what it sounds like. Actually, the majority of significant cloud service providers use cloud object storage as their main storage type.

Advantages of object storage

Massive scalability

The flat design of object storage is easily scalable and does not have the same drawbacks as block or file storage. Since object storage is practically infinite, adding more devices will allow data to grow to exabytes.

Reduced complexity

Because object storage lacks directories and files, it eliminates a lot of the complexity associated with hierarchical systems. Because you don’t need to know the precise position, finding data is made easy by the absence of intricate trees or partitions.

Searchability

Since metadata is a component of objects, it can be easily searched and navigated without the use of an additional application. Additionally, it is far more adaptable and customisable. Objects can be tagged with information and properties, including cost, consumption, and policies for tiering, retention, and automated destruction.

Resiliency

Data may be automatically replicated and stored across several devices and locations with object storage. This can help support disaster recovery plans, prevent data loss, and guard against outages.

Cost efficiency

Cost was a consideration when object storage was developed, offering larger data storage at a lower cost than file- and block-based systems. Because object storage only charges for the capacity you want, you can keep expenses under control even when dealing with massive volumes of data.

Disadvantages of object storage

Performance and Latency

• Compared to block storage, object storage may have higher access latency, making it unsuitable for fast transactional workloads.
• Unsuitable for Random Access: It performs less well in situations that need for frequent, small-scale random read/write operations.

Integration’s Complexity

• Application Modification: Object storage APIs such as OpenStack Swift and Amazon S3 are not natively supported by many legacy apps. Applications may need to be re-architected for compatibility.
• Learning Curve: Implementation may be slowed down if teams require training in order to embrace object storage systems and standards.

Restricted Use Cases

• Not Suitable for Databases: Workloads requiring high IOPS (Input/Output Operations Per Second) or database storage are not best served by object storage.
• Problems with File Sharing: Its lack of support for file system semantics like hierarchical directories and locking might make file sharing difficult.

Issues with Data Consistency

• Eventual Consistency: Because some object storage systems employ eventual consistency models, modifications to data may not be instantly reflected in all systems, which could lead to disputes.

Financial Aspects

• API Fees: For applications that perform operations often, public cloud object storage services frequently charge for API calls (such as PUT, GET, and LIST).
• Network Costs: There may be substantial network egress fees associated with moving huge amounts of data into and out of storage.

Object storage vendors

Applications of object storage

Object storage is used by customers for many different purposes. These are typical usage cases.

Analysis

Cloud object storage allows you to gather and store almost any kind of data, and you can use big data analytics to learn important things about your business, clients, and target market.

Lake of Data

Cloud object storage is the core of a data lake due to its great durability and nearly infinite scalability. Paying simply for what you need allows you to easily and nondisruptively expand storage from gigabytes to petabytes of content. It boasts inbuilt encryption, access control capabilities, scalable performance, and user-friendly features.

Data from cloud-native applications

Cloud-native apps leverage technologies like serverless and containerisation to quickly and adaptably fulfil user requirements. Usually composed of discrete, loosely linked microservices, these apps communicate internally by exchanging state or data. Cloud storage services address persistent issues with data storage in the cloud environment and offer data management for these kinds of applications. You may add as much content as you want and access it from anywhere with object storage, which speeds up application deployment and expands your user base.

Archiving data

Long-term data preservation is greatly enhanced by cloud object storage. With its improved durability, instantaneous retrieval times, improved security and compliance, and increased data accessibility for sophisticated analytics and business intelligence, it can be used to replace on-premises tape and disc archive infrastructure. Large volumes of rich media information can also be cost-effectively archived, and you can save required, regulatory data for a long time.

High-quality media

Reduce the expense of storing rich media data, like digital photos, music, and movies, and speed up apps. By utilising storage classes and replication characteristics, object storage allows you to design an affordable, globally replicated architecture that distributes media to users that are spread out.

Recovery and backup

Object storage systems can be set up to replicate content, making backup devices available in the event that a physical device fails. This guarantees uninterrupted operation of your systems and apps. Additionally, data can be replicated across several datacenters and geographical locations.

Machine learning

Machine learning (ML) is the process of “teaching” a computer to draw conclusions or make predictions. Models are trained using algorithms, and then they are integrated into your application to produce insights at scale and in real time. Since a production model usually learns from millions to billions of sample data items and generates conclusions in as little as 20 milliseconds, object storage is necessary for machine learning due to its size and cost effectiveness.

Object Storage vs File storage