Providing containers for the new flow of data | #DockerCon
by Gabriel Pesek
Jun 21, 2016
As data from users and systems analysis continues building in production rates, new approaches to managing and storing it are vital for keeping pace.
Sandeepan Banerjee, SVP of Engineering and Operations at ClusterHQ, Inc., spoke with John Furrier (@furrier) and Brian Gracely (@bgracely), cohosts of theCUBE, from the SiliconANGLE Media team, during DockerCon 2016 in Seattle, Washington, about his company’s services and the changing face of data.
Workloads and protection
“We are ClusterHQ, container data people,” Banerjee said to introduce his company, identifying its mission goal as making compute elastic, because, as he stated, “Real life is all about managing persistence.”
With that basis established, Banerjee continued by explicating his company’s place in the data world. “The container world and the cloud world have realized that it’s not only necessary to start up stateless compute services, but you need [dozens of services], and the entire stack is growing toward the real world,” he said.
“Normally when you elasticize a workload using containers, if a particular node dies, the workload dies with it. … So you have to take care to make sure that the data is protected, that it is persistent … as the workload hops around from machine to machine,” he continued. Finding ways of solving this problem and then continuing to refine those fixes is ClusterHQ’s main focus at this point, though each situation calls for its own treatment.
Optimization and learning
“To protect [data] requires a lot of operational optimization,” Banerjee noted. “The entire life-cycle of data is a long and complex one. … With containers, things are inherently moving around, the compute jumps from machine to machine, and the data has to follow.” But examining each step in those life-cycles is giving them new ideas for how to handle the whole.
“What we have learned is that the initial place where we started was to make the developer’s life simple. … We now see a lot of interest not only from a DevOps person trying to operationalize data, but from … all the people who are stewards and custodians of data. … In a nutshell, I think the constituency for interest in contextualized data has grown even more broadly than where it was two years ago.” Summing up how ClusterHQ is serving its customers’ interests, Banerjee stated, “We provide a safe conveyor for your data to move.”
Considering the growth possibilities and emerging opportunities, he felt that machine learning would be where many of the most exciting developments would be found. “For the container world, what we are looking increasingly at is for the stewardship and machine learning we were talking about … it is going to be possible to [predict and place data optimally for you]. … With machine learning, we are going to be able to place your data in the right place at the right time,” he said.
#dockercon
#theCUBE
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Sandeepan Banerjee, ClusterHQ | DockerCon 16
Providing containers for the new flow of data | #DockerCon
by Gabriel Pesek
Jun 21, 2016
As data from users and systems analysis continues building in production rates, new approaches to managing and storing it are vital for keeping pace.
Sandeepan Banerjee, SVP of Engineering and Operations at ClusterHQ, Inc., spoke with John Furrier (@furrier) and Brian Gracely (@bgracely), cohosts of theCUBE, from the SiliconANGLE Media team, during DockerCon 2016 in Seattle, Washington, about his company’s services and the changing face of data.
Workloads and protection
“We are ClusterHQ, container data people,” Banerjee said to introduce his company, identifying its mission goal as making compute elastic, because, as he stated, “Real life is all about managing persistence.”
With that basis established, Banerjee continued by explicating his company’s place in the data world. “The container world and the cloud world have realized that it’s not only necessary to start up stateless compute services, but you need [dozens of services], and the entire stack is growing toward the real world,” he said.
“Normally when you elasticize a workload using containers, if a particular node dies, the workload dies with it. … So you have to take care to make sure that the data is protected, that it is persistent … as the workload hops around from machine to machine,” he continued. Finding ways of solving this problem and then continuing to refine those fixes is ClusterHQ’s main focus at this point, though each situation calls for its own treatment.
Optimization and learning
“To protect [data] requires a lot of operational optimization,” Banerjee noted. “The entire life-cycle of data is a long and complex one. … With containers, things are inherently moving around, the compute jumps from machine to machine, and the data has to follow.” But examining each step in those life-cycles is giving them new ideas for how to handle the whole.
“What we have learned is that the initial place where we started was to make the developer’s life simple. … We now see a lot of interest not only from a DevOps person trying to operationalize data, but from … all the people who are stewards and custodians of data. … In a nutshell, I think the constituency for interest in contextualized data has grown even more broadly than where it was two years ago.” Summing up how ClusterHQ is serving its customers’ interests, Banerjee stated, “We provide a safe conveyor for your data to move.”
Considering the growth possibilities and emerging opportunities, he felt that machine learning would be where many of the most exciting developments would be found. “For the container world, what we are looking increasingly at is for the stewardship and machine learning we were talking about … it is going to be possible to [predict and place data optimally for you]. … With machine learning, we are going to be able to place your data in the right place at the right time,” he said.
#dockercon
#theCUBE
