The artificial intelligence (AI) industry is continuously revealing new innovations. These innovations range from robotics to data analytics and more. AI has begun to contribute to every industry. Regardless of the industry, the use of AI generates big data. And the biggest challenge of big data is effective storage and backup. Disaster recovery plan for this data is also important because it’s crucial for enterprise operations; as loss of this data can result in disruption of operations or interruption in innovations.the biggest challenge of big data is effective storage and backupClick To Tweet
AI-based technology can generate huge data lakes. These data lakes require effective data analytics and that too requires AI and machine learning. Analyzing a large data lake, in turn, requires a lot of computational capacity as well.
Addressing AI Storage & Backup Requirements
Storage: Locally & in the Cloud
There are two major options for enterprise data storage: Local infrastructure and enterprise cloud storage. The best storage is decided by use case requirements.
AI generates a lot of data and AI-based data analytics require high IOPS and reduced latency. On-premises infrastructure possesses the capability to support performance intensive workloads at greater speeds as compared to cloud-based storage solutions. Cloud-based storage solutions emphasize on the cost-effectiveness of the solution. For research-based workloads, enterprises can spare the budget in order to acquire results. That’s why, in comparison, an enterprise NAS storage would be the better choice instead of cloud storage for research environments.
Backup: Locally & in the Cloud
Similar to storage solutions, enterprises can acquire backup appliances to store their data locally and they can also opt to store them in the cloud.
Enterprise data can be classified into three types based on access frequency: Hot data, Cold data and archival data. Each data type has its own backup requirement. Hot data is data that is frequently accessed while cold data is infrequently accessed data. Archival data is the kind of data that’s rarely accessed and mostly kept for either compliance reasons or future reference.
local backup infrastructure also reduces latency and supports high IOPS. This makes it the primary choice for hot data backup; however, as enterprises can tolerate latency for cold data, it is better to use cloud services for cold data backup
Similar to local storage solutions, local backup infrastructure also reduces latency and supports high IOPS. This makes it the primary choice for hot data backup; however, as enterprises can tolerate latency for cold data, it is better to use cloud services for cold data backup. The same applies for archival data. As the loss of this data doesn’t disrupt operations, enterprises can endure latency in archival data recovery as well.
Hybrid Solution for Storage & Backup
Evidently, research data storage and backup requirements simply cannot be addressed by a single kind of solution (either local infrastructure or cloud). It has to be a combination of both in order to properly facilitate both requirements: storage and backup.
This means, that the enterprise will acquire two sets of hybrid solutions. One will be a combination of a NAS appliance with cloud connect services that store cold data in the cloud and keep the hot data locally. This will be the hybrid storage solution.
The other set will be a combination of backup appliance with cloud backup. This backup solution will keep hot data backed up locally while backing up the cold and archival data on the cloud. The enterprise can also opt to create replicas of the hot data stored locally in the cloud. These sets of solutions will effectively address the data requirements of research environments and will optimize the process by supporting AI based innovation.