ArchiTECHt Daily: HPE's billion-dollar storage acquisition

The benefit of getting a super-late start on finalizing today's newsletter: actual news from today th
ArchiTECHt Daily: HPE's billion-dollar storage acquisition
By ARCHITECHT • Issue #31
The benefit of getting a super-late start on finalizing today’s newsletter: actual news from today that I couldn’t pass up. While every other day this week will likely be dominated by Google, today it’s HPE buying Nimble Storage for $1 billion.
I don’t have a lot of time to dig into this, but the big draws for HPE appear to be that: 
  1. Nimble has a hybrid flash-disk system, which is good.
  2. Nimble allows for hybrid cloud storage options across data centers and the public cloud.
  3. HPE’s storage revenue has been falling, while Nimble (which just announced earnings today) is still growing.
Also, the price tag seems pretty small, considering that Nimble’s 2016 revenue was $403 million.  
I suggested last week that HPE should buy a container company (and I still think it should), but the Nimble acquisition was probably cheaper and offers more revenue right off the bat, so it looks like HPE got itself a good deal.It’s also more progressive than HPE’s legacy-centric $65o million acquisition of Simplivity in January.

Sponsor: Datos IO
Sponsor: Datos IO
Around the web: Cloud and infrastructure
Actually, Google got a headstart on the week’s news with the GA release of Container Builder—a project I’m not sure most people even knew was in beta. Docker probably isn’t too happy about this.
Details from the companies about how they’ll integrate are sparse, but it looks like Thinkbox will keep selling cloud-powered rendering services and more under the AWS banner. If AWS has more strategic plans than reaching a growing vertical with targeted products, they’re not immediately clear.
The timing on the AWS Health Tools repository, on GitHub no less, is either planned or coincidental. It’s a tool for automatically receiving alerts and remediating certain issues when stuff goes awry.
I think everyone accepts some version of this as true. The real question is about who will own the edge and how edge-y it will actually be.
This guy’s series on deploying and managing “serverless” computing has been quite good. Last, but not least, is how to do all this securely.
The important lesson here is that there’s a big open source movement in China that’s often unserved by U.S.-based projects and companies. So they build their own.
Around the web: Artificial intelligence
Cue easy joke about Watson teaming up with Einstein. The plan is to help businesses do predictive targeting. The AI-washing police are having a field day.
Deloitte claims it will actually increase jobs overall, while eliminating certain tasks. This seems reasonable in white-collar settings.
Whenever I read research out of Disney, I assume the plan is to apply it in short order. I’m guessing more realistic video games or something to do with ESPN (better predictions/simulations?) here …
So, this is interesting … Google is applying evolutionary models to self-generate computer vision neural nets. That means less human effort and, the authors claim, the ability to integrate new techniques without restarting an experiment.  •  Share
The evolution of the data lake, according to Hortonworks.
The evolution of the data lake, according to Hortonworks.
Around the web: All things data
VR, water conservation, digital signage. This is marketing 101 in terms of content, but sharing interesting examples of how to think about utilizing data is still a helpful practice.
First it was Gartner, now it’s Forrester’s turn. They’re probably right to a degree, but enterprises are coming to the cloud with a watchful eye against getting locked in at the infrastructure layer.
I like where Hortonworks is headed here, particularly the emphasis on containers. Whether this is ultimately a YARN thing, rather than a Kubernetes or even Mesos thing, remains to be seen. 
Good advice for aspiring data scientists. In Silicon Valley, especially, I would pay attention to the networking part. It often is who you know.
I point this out because many attempts to do something like this today would likely take an AI approach. But for companies like Twitter with huge Hadoop clusters, you want to work with what you have.  •  Share
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