Although the public cloud offers excellent climbable and pliability, there are benefits to keeping data on-premises. For one thing, the retreat costs of extracting large amounts of data from the public cloud can prove to be uneconomic for many firms. And possession of data on-premises helps customers to control their data, being safe from issues.
Maintaining data on-premises is particularly perfect for this moment in time. COVID 19 pandemic has led to excessive growth in the amount of data used. This is indicated by an OpenVault report showing a rise in the number of users consuming more than 1 TB of data per month.
Data services management can have its own set of obstacles, remarkably if data is being operated in the usual file storage method.
While file storage systems can store anything, they are limited in the amount of information they can hold.
Organizations may need to adapt to new techniques to meet increasing demand, which can become very costly and becomes complicated to handle. As file systems only store a limited amount of metadata, it can be problematic for applications to find the data they require to run without trouble.
This is case object-based storage becomes handy. Object storage comes up with a flat structure in which files are confined into objects. Each object has exclusive metadata attached to it and can have additional labels. All things are collected in object buckets.
This is an ideal point of view for organizations that has plans to deal with data services in-house.
The reasons are as follows:
Accessibility:
Since object-storage uses a bucket structure, it is infinitely scalable. The latest research conducted by Red Hat stated that object storage evolved under the Ceph open source project could handle 10 billion objects, a number that testers expect to grow over the next few years. Simultaneously, object storage is cost-effective; when companies need to add storage, they can easily do so as necessary without investing upfront in additional storage they may not need.
Easy Searching:
Connecting unique metadata to each object makes for easy searchability. File system crawls are not required as it can be time-consuming, especially when combing extensive data. Applications can easily and quickly find the data they need, resulting in faster response times.
Efficiency:
Gripping on AI and machine learning algorithms can give organizations an actionable view into their usage pattern. Many organizations use data lakes to store large repositories of information sent into these AI and machine learning algorithms. Using object storage for data lakes helps organizations use massive data sets to be asked and used for most demanding AI and machine learning works.
