Managing Big Data Storage: The Role of Object Storage

More than 40% of companies use big data analytics to improve decision-making and get the competitive edge they need to grow (Market Data Forecast). And by the end of 2024, the global big data market reached a value of $199.63 billion, a number expected to reach $573.47 billion by 2033.

The issue is that the creation of data is exponential. Almost every action online is adding to the extremely large and complex datasets that are too vast and dynamic for traditional data processing software to manage.

Object storage is one solution. Read on to find out more.

What is Object Storage?

Object storage is big data’s answer to storing and managing ever-growing unstructured data in units called objects.

These objects, or simply unstructured data, can be standard data, metadata, or a unique identifier. But it’s not organised how you might think. Rather than being stored in folders or a hierarchical path, they’re accessed through multiple paths. It’s a flat data environment approach that also applies to structured data such as photos, videos, emails, etc. Most of the public cloud services you’ll find, such as OVHcloud, use object storage to spread data across three distinct geographic zones.

The role is simple: to reliably and efficiently manage, store, and archive high quantities of unstructured data.

The Big Data Challenge

The challenges of big data tell us why object storage is becoming so essential.

The sheer volume, velocity, and variety of big data are creating a massive strain on existing, traditional data storage servers and processing infrastructures. And with such massive data sets come security and privacy risks.

An even bigger risk is that there’s a lack of skilled professionals with the required training to manage and analyse big data. It’s having a massive impact on data quality and storage.

And considering we’re arguably at a point where we can’t live without big data, solutions like object storage are essential.

Why Object Storage is Essential for Big Data

Object storage works so well for big data because of its massive scalability potential.

Object storage can handle huge, unstructured datasets and turn them into something that actually makes sense. It’s far more cost-effective for managing big data volumes, and because it can store data in its native format for analytics, artificial intelligence or machine learning, it’s at a point where big data almost can’t be without object storage.

And because it’s so durable, it’s suitable for backups and archiving. On top of that, it provides rich metadata and searchability so people can extract value and insights without searching through pointless data.

The leading use cases for object storage are:

• Artificial intelligence and machine learning

• Content
• Logs and archiving

We’d argue big data can’t survive without object storage anymore. Its cloud compatibility, scalability, and cost-efficiency make it the perfect solution to the big problem of big data. And considering you can use object storage to store terabytes or even petabytes and beyond, it’s a solution of almost limitless possibilities.