Computational Storage Explained

Computational storage is an approach in data computing for IT infrastructure where data is processed at the storage device itself, enabling real-time storage and analysis of data. By adding processing power directly within the storage layer, analysis of generated data is not delayed, and the time needed to move large amounts of data from storage to separate compute units is eliminated.

This is becoming increasingly important as data continues to grow exponentially in this information era. Companies often struggle to match their computing capabilities to their storage devices. Imagine numerous sensors generating hundreds of gigabytes of data daily, but the processing power (like a CPU's memory) can only handle tens of gigabytes.

In conventional IT infrastructure, data analysis happens in phases – data is first transferred from storage to computing systems and then analysed. This sequential approach prevents real-time analysis. Computational storage solves this by processing data directly on the storage device, often using Solid-State Drives (SSDs). Unlike older mechanical storage devices like hard disk drives, SSDs have built-in processing units that can perform calculations on the data locally.

By processing data at the storage level, computational storage creates a more manageable subset of data to be sent to compute devices for further analysis. This eliminates data bottlenecks and allows for real-time insights. This approach is particularly valuable in scenarios requiring low latency, such as the Internet of Things (IoT) and edge computing applications.

Computational storage offers a solution to the growing challenge of the storage-to-compute bottleneck. As we transition to an age dominated by analytics, big data, IoT, and machine learning, ever-increasing data volumes demand real-time analysis to generate valuable insights.