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In the era of big data and AI and where in most organizations moving to cloud architecture major concerns revolve around data storage for analytics, security, and regulatory requirements. Storage requirements have become much more complex and require a holistic approach for resolution. The organizations are facing challenges in terms of storing data for long periods, high cost of storage, variety of data sets (structured, unstructured, semi-structured) at higher volume and velocity.
Today we look more in detail about concerns revolving around big data storage, challenges faced, and how SDS could help us to manage this data explosion.
Data Storage Challenges
Rapid transformation of IT world wide is empowered with cloud computing, Artificial intelligence, machine learning, big data and 5G technologies which are instrumental in bringing the digital age currently we are in and this is further speeding up post Covid era. Businesses and services are becoming more intuitive and diverse and generating large volumes of data in a variety of forms which has posed a major challenge to the traditional storage technologies.
We will look more in detail about such challenges in the upcoming section.
- High TCO due to rapid growth – Once the optimum storage capacity is reached in traditional storage the hardware needs to be refreshed and refreshment costs are quite high. To establish newly purchased storage equipment, a lot of capital and manpower is needed. Accessories of traditional storage systems are quite expensive and to expand capacity of existing storage original disks sets are required to be procured.
- Inflexibility of capacity expansion – Scaling out traditional storage is a tough task as it can be scaled only with per disk group or array of disks. The performance of storage is restricted with the storage controller. The capacity expansion results in performance degradation.
- Not able to meet high throughput demands – traditional storage architecture performance is limited with storage controllers, which prevents high throughput performance. This could lead to instability and performance degradation when data amount exceeds 100 million objects.
- Maintenance and operations difficulties – Traditional storage architecture works in silos, and it makes it tough to troubleshoot. Different applications require separate storage devices.
Data Explosion: Software Defined Storage (SDS)
Software Defined storage is software-based consolidation of storage devices in storage pool which enables elastic expansion and allocation based on demand. SDS architecture uses software applications to manage and control resources of storage eliminating dependency on physical hardware. SDS enables allocation dynamically, virtualization and storage resources abstractions.
In SDS, software layer acts as transitional between application and underlying storage devices. This layer is capable of handling multiple tasks – such as data storage, access management and has a unified and simplified application interface.
When file or data is saved SDS gets a request and deploy intelligent algorithms to store data at appropriate locations. Distribution of data across several storage devices, performance optimization and utilization capacity. Software Defined storage creates redundant copy of data to meet high availability, fault tolerance, resiliency to protect against hardware failure.
Benefits of Software Defined Storage
- Single click operation – SDS is a single solution to manage operations and maintenance using a single click operation for ease of data management. No new software licenses to be purchased while refreshing hardware, only physical devices to be changed.
- Flexible expansion of Distributed architecture- SDS is adoption of distributed architecture to enable organizations for scaling out storage on demand to manage growing storage and performance requirements. This approach helps to eliminate need for pre-assessment of existing storage resources
- High performance for parallel processing – High throughput and IOPS performance is delivered via multi-node parallel data processing. Multiple storage nodes work in coordination to process and handle data requests. Distribution of workloads across nodes to achieve higher performance as compared to traditional storage systems
- Single interface to manage entire cluster – SDS provides a single interface to manage an entire cluster of storage nodes or pools. Providing a unified view and control over all resources from a single window.
- Software Defined storage For lower TCO – Lowering TCO is one of the key benefits of SDS using storage virtualization on X86 standard servers which are more affordable and have ease of availability. Platform consolidation bring down costs of hardware, expenses incurred on maintenance, and complexity of management associated with multiple storage resources
Conclusion
The exponential growth of data generation by organizations and industries led to the need for a robust and effective solution to manage storage. Existing storage devices cannot keep up with the needs for scalability on demand, agility, fault tolerance, redundancy, capacity, and performance management. Software Defined Storage solutions emergence provides dynamic allocation of storage resources, single interface management and many other benefits. seamless scalability of both capacity and performance to accommodate ever changing demands of storage resources can be met using SDS solutions.