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Five Minute Briefing - Data Center
October 9, 2023

Five Minute Briefing - Data Center: October 9, 2023. Published in conjunction with SHARE Inc., a bi-weekly report geared to the needs of data center professionals.

News Flashes

IonQ, a leader in the quantum computing industry, is debuting two rack-mounted solutions that introduce quantum capabilities to existing infrastructures: IonQ Forte Enterprise and IonQ Tempo.

The National Association of State Chief Information Officers (NASCIO), the Center for Digital Government (CDG), and IBM released a report entitled, "Preparing for Future Shocks in State Government," aiming to help frame what future disruptions may look like for state chief information officers (CIOs) and how they can effectively tackle them.

New AI toolkits, machine learning (ML) frameworks, and AI-based private cloud tools are coming to IBM Z-series mainframe users, as the company looks to preserve its share of the fast-growing AI marketplace. The company announced that its newest offerings, meant to help organizations get to work on the latest and greatest in AI frameworks, will be available for IBM Z, LinuxOne, z/OS, and Cloud Pak architectures.

Vantage Data Centers has raised $1.35 billion in securitized notes, enabling the company to refinance the existing loans in place for three of Vantage's data centers in Northern Virginia and five data centers in Quebec, Canada. The remaining funds will be used for "general corporate needs."

News From SHARE

If you've ever found yourself in a house so old that it smells like the last century and emits random creaks, you may have wondered if it had non-corporeal inhabitants, like the spirits in "Poltergeist" or "Ghostbusters." You may not immediately envision such disembodied gremlins inhabiting our computers.

Think About It

Data models allow for the expression of a great deal of clarity and precision—when the data modeler chooses to allow for it. Many designers seem to work in "sloppy" or "imprecise" mode. Entities are defined containing many nulls allowed attributes. Certainly, if in the existing situation the source data is so dirty that every defined attribute "should" apply, but randomly things are not passed on, then yes, the data model is accurate. However, if the condition is such that the object has many very similar sub-objects, and various combinations of attributes must be populated based on which sub-type is being instantiated, then the data model is not expressing those rules very well.