AI-based Virtual DB Admin.

The database is a highly essential component of every IT organization. It is crucial and should handle utmost care as it stores almost every essential data within an organization. As we all know, the amount of data generated every second is really huge. Day by day, a variety of data is generated in huge volumes. Managing these data and taking care of the database without causing a failure is a critical task. That's where the Database Administrators(DBA) role comes into the picture. The role of a DBA is very crucial in an IT organization. They need to continuously manage and monitor the entire database without failure because most applications rely on the database's smooth run. Moreover, they are in control to check that the database's capacity is not exceeded and the database can support all the applications. However, an IT organization won't just handle a single database, and they might use multiple databases. So, the DBA is responsible for managing this in an entire picture.
But there is a high chance that this role of responsibility can fail. Also, the risk of any crash occurring is a much prevailing situation. Apart from that, the greater risk is in setting up the environment without causing any error. This is also a complex process as most of the application depends on multiple sources of data. Moreover, databases' availability is an essential factor in meeting the SLA and delivering with a fast response time. Also, most of the IT team depends on the DBA. Apart from their main job role, such as maintaining and installing the required database, managing the database standards, and the database access and IT team operational workflow depend on the DBA. Whenever a database issue is a record in the organization, the DBA will be accountable for it. This keeps the DBA in a lot more stressful situations. But, can all of these problems or requirements be automated rather than doing it manually?
Virtual DB Admin:
The implementation of cognitive automation can automate DBA activities. That is completely transforming the old school DBA to the modern cognitive DBA, the AI-based DBA. Machine learning and AI are applied to DBA operations, such as database management, providing database finetuning, and identifying any bottlenecks caused in the system.
DBMSs that handle large amounts of data and operate complex workloads are difficult to manage because they have hundreds of configuration settings that require experts to administer. Many organizations have started to incorporate AI and machine learning in databases to enable “autopilot” monitoring and help DBAs proactively address problems caused by mistuned databases. These emerging technologies are permeating into the work of the modern DBA, making their lives easier. Organizations are implementing database software that uses ML to help protect customer data and automate their information management. The DBA community is adopting tools that make it easier for anyone to deploy a DBMS – even those without any expertise in database administration.
Let's see a scenario where the automation of DBA helps effectively. Suppose a user wants to perform a database backup. At first, he needs to identify the right person that can work on this task. If the operator is not free, he/she needs to hold up. Moreover, this causes a great delay in the work that needs to be done. This holds upon the other work/task that can be effectively done, that is, a certain delay in work is causing a lot of hogging in other important work. How can AI resolve this situation? The user really does not need to wait or rely on a 2nd person to get a backup process done. They can just log into the system and open a ticket for a back up to be done. The AI system will automatically understand the requirement, and an automated task will be executed wherein the workflow will be to perform the automatic backup of the system. In this way, auto-remediation and auto-fulfillment of a ticket can be effectively managed using AI. By implementing AI into the system, the schedule of database backup can be effectively done by the user itself and does not require the need to rely on a DBA. In this way, other important works can be done with their priority and let AI-based cognitive automation do its work.
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