With all of the drama associated with AI, it’s easy to miss the need to understand the foundations that deliver the data that is the raw element from which AI value is built. Databases and storage infrastructure are critical components that have to work in concert with AI plans and returning guests Henry Baltazar and James Curtis join host Eric Hanselman to discuss what’s been happening and what enterprises need to know about the future. Databases and storage management systems have been intertwined for a long time and AI pressures are tightening that connection. Storage systems perform analytics on the data they store to optimize its handling, tracking use and characteristics. The same insights that aid in compression and tiering are also useful in classifying data for AI. Data classification has always been a challenge for enterprises, as storage systems are typically disconnected from the data owners and applications that use them. Intelligent storage systems have been able to intuit the nature of content, including mapping databases and virtual machines. Databases have been able to leverage storage capabilities like snapshotting for resilience.

Into this mix a new set of AI focused storage and database offerings arrive that target AI uses. The question is whether the native database and storage systems can do enough of what’s needed. They already store key data and have valuable insights and classification capabilities. Some vendors are attaching GPU clusters to storage systems to provide high performance AI model training functionality. The major issue for most, is the matter of data placement. Shifting petabytes of data is no small task and concerns about data security and the costs involved now loom much larger.

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Credits:

• Host/Author: Eric Hanselman 
• Guests: Henry Baltazar, James Curtis
• Producer/Editor: Adam Kovalsky
• Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith