Data lake analytics and public perception key | #StructureConf
by Gabriel Pesek | Nov 18, 2015
#theCUBE #StructureConf #SiliconANGLE
At the end of the first day of the Structure 2015 conference in San Francisco, CA, hosted by Structure, LLC, there was plenty to talk about, from the new software developments and fresh applications to the public offerings and strategies intended to broaden the accessibility of cloud-based utilities.
Jeff Frick and George Gilbert, cohosts of theCUBE, from the SiliconANGLE Media team, sat down together to discuss some of the highlights from this first day of the conference, what they were anticipating for following days and what seemed to be the key points made so far.
‘Many layers of technology’
“Data lakes” were one of the most-discussed topics from this discussion, with examination of how their infrastructure was being established for clear future accessibility and adjustments, as well as the layering of analytics over their stored data.
A point referenced by Gilbert was that “Intel was talking about how what used to be hard-wired infrastructure … is now much more fluid,” and the changes brought on by this move were driving a number of developers to meet it in a variety of ways.
However, the avenues opened by these developments were not without their drawbacks. As Frick noted, “Cloud’s all about elasticity, being able to expand and contract, but you can’t do all that automation without a lot of overhead.”
Finding ways to scale management and overhead to meet the needs for each business is presenting its own challenges, ones which call for human ingenuity more than machine learning, at least at this point.
Direction and perception
“Machine learning is sort of the new black, as in the new ‘it’ color, and you can use machine learning to get a sense for how … your software should behave,” Gilbert stated. As both hosts acknowledged and Gilbert vocalized, “You have to start with a question,” even when working with the resources of giants such as Google and Microsoft.
While machine learning allows these companies to “get a sense of the rhythms,” they still face a large gulf between public perception of these capabilities and those of cloud utilities, and what they can actually accomplish.
@theCUBE
#StructureConf
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Wrap Up Day 1 | Structure 2015
Data lake analytics and public perception key | #StructureConf
by Gabriel Pesek | Nov 18, 2015
#theCUBE #StructureConf #SiliconANGLE
At the end of the first day of the Structure 2015 conference in San Francisco, CA, hosted by Structure, LLC, there was plenty to talk about, from the new software developments and fresh applications to the public offerings and strategies intended to broaden the accessibility of cloud-based utilities.
Jeff Frick and George Gilbert, cohosts of theCUBE, from the SiliconANGLE Media team, sat down together to discuss some of the highlights from this first day of the conference, what they were anticipating for following days and what seemed to be the key points made so far.
‘Many layers of technology’
“Data lakes” were one of the most-discussed topics from this discussion, with examination of how their infrastructure was being established for clear future accessibility and adjustments, as well as the layering of analytics over their stored data.
A point referenced by Gilbert was that “Intel was talking about how what used to be hard-wired infrastructure … is now much more fluid,” and the changes brought on by this move were driving a number of developers to meet it in a variety of ways.
However, the avenues opened by these developments were not without their drawbacks. As Frick noted, “Cloud’s all about elasticity, being able to expand and contract, but you can’t do all that automation without a lot of overhead.”
Finding ways to scale management and overhead to meet the needs for each business is presenting its own challenges, ones which call for human ingenuity more than machine learning, at least at this point.
Direction and perception
“Machine learning is sort of the new black, as in the new ‘it’ color, and you can use machine learning to get a sense for how … your software should behave,” Gilbert stated. As both hosts acknowledged and Gilbert vocalized, “You have to start with a question,” even when working with the resources of giants such as Google and Microsoft.
While machine learning allows these companies to “get a sense of the rhythms,” they still face a large gulf between public perception of these capabilities and those of cloud utilities, and what they can actually accomplish.
@theCUBE
#StructureConf
