More than 40% of companies use big data analytics to improve decision making and gain the competitive advantage they need to grow (Market Data Forecasting). And by the end of 2024, the global big data market reached $199.63 billion, a figure that is expected to reach $573.47 billion by 2033.
The problem is that data generation occurs exponentially. Almost every online action contributes to extremely large and complex data sets that are too large 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 as organized as you might think. Instead of being stored in folders or a hierarchical path, it is accessed through multiple paths. It is a flat data environment approach that also applies to structured data such as photos, videos, emails, etc. Most of the public cloud services you find such as: Some providers, such as OVHcloud, use object storage to distribute data across three different geographical zones.
The task is simple: manage, store and archive large amounts of unstructured data reliably and efficiently.
The big data challenge
The challenges of big data show us why object storage is becoming so important.
The sheer volume, velocity and diversity of large amounts of data place enormous strain on existing, traditional data storage servers and processing infrastructures. And with such massive amounts of data come security and privacy risks.
An even greater risk is the lack of qualified professionals with the necessary training to manage and analyze large amounts of data. It has a massive impact on data quality and storage.
And considering that we are probably at a point where we can no longer live without big data, solutions like object storage are essential.
Why object storage is essential for big data
Object storage is so well suited to big data because of its enormous scalability potential.
Object Storage can process huge, unstructured data sets and turn them into something that actually makes sense. It is far more cost-effective for managing large amounts of data and because it can store data in its native format for analytics, artificial intelligence or machine learning, it has reached a point where big data is almost impossible without object storage.
And because it’s so durable, it’s suitable for backups and archiving. Additionally, it provides rich metadata and searchability, allowing people to gain value and insights without having to sift through meaningless data.
The main use cases for object storage are:
- Artificial intelligence and machine learning
- Contents
- Logs and archiving
We believe that big data is no longer viable without object storage. Its cloud compatibility, scalability and cost-effectiveness make it the perfect solution to the big problem of big data. And when you consider that object storage can be used to store terabytes or even petabytes and more, it’s a solution with almost limitless possibilities.
