In George’s article he talks about how cutting through the hype around AI has become a major job for IT leaders. He provides an in-depth guide on where and how to add AI to your IT infrastructure and how it will help. Technology providers are investing huge sums to infuse AI into their products and services. The industry press touts the gains companies stand to make by infusing AI in IT infrastructure -- from bolstering cybersecurity and streamlining compliance to automating data capture and optimizing storage capacity. AI, we are told, will make every corner of the enterprise smarter, and businesses that fail to understand AI's transformational power will be left behind. Bill Saltys and Bharath Terala weigh in on AI automation in storage and data management.
Bill Saltys, senior vice-president of alliances at Apps Associates, an IT consultancy, said embedding AI in IT infrastructure will fundamentally change many of the tasks traditionally required to keep storage systems humming. One area is in tuning the physical data infrastructure, using AI in just-in-time maintenance, self-healing, failover and business continuity. For example, many storage systems use RAID to make multiple physical hard drives or solid-state drives appear as one storage system to improve performance and reduce the impact of a single failure. Better automation can help distribute this data to improve read and write speeds or improve comprehensiveness.
On the data management side, AI and automation will dramatically reduce the efforts of managing, scaling, transforming and tuning across various database management systems, said Bharath Terala, practice manager and solution architect for cloud services at Apps Associates.
Terala said AI and automation will also make it easier to tune the data management application for different kinds of databases, including structured SQL for transactions, graph databases for analytics, and other kinds of non-SQL databases for capturing fast-moving data. AI-assisted automation could affect a cultural shift away from DBAs focused on optimizing an enterprise's existing databases and toward data engineers focused on optimizing and scaling the infrastructure across different best-of-breed data management apps. For example, SQL might be used for transactions, graph databases for analytics and key-value stores for capturing IoT data.
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