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Why Run Analytics in the Cloud? Talking Cloud with Azure, Part 6

Why Run Analytics in the cloud


Giovanni Tropeano:  Yeah. One of the more important parts about being able to make smart business decisions is to have the right data.  And a lot of businesses are looking at doing analytics in the cloud. Why would they want to do analytics in the cloud versus having it in the house?


Karl Rautenstrauch:  Yeah, great question.  I had a really, really good conversation last week with a mutual channel partner that we both work with about this use-case, because they just couldn’t believe and they were like, “Why would anybody ever want to do this in the public cloud?” It’s possible now to get these [Inaudible] [0:28:22] based infrastructures and all these super fast, I think well – yeah but think about what you are saying.  Unless you are in the business of analytics, unless that’s the basis of your business and you are running analytics routines and jobs 24/7, 365, if you’re looking at spending a lot of money and bringing a lot of expertise in house to deploy something that’s already build for you in a public cloud platform like Azure. Do you want to go out and acquire all that hardware and maintain all that hardware to deploy your own cloud error instance or data stacks instance, or do you want to just go through in a couple of clicks and get what you need and be able to scale it up and down to meet your needs?  That’s what we are finding companies doing.

And even customers like one that I’m really familiar with both from my time here at Microsoft and from my time at NetApp, here 12 miles from my house in North Florida, whose business is based around analytics.  They are in the business of providing critical reporting to their customers; they’ve moved their analytics instances, their analytics workloads to Azure, for all of the reasons that we’ve discussed on this call.  The ability to get the horsepower they need when they need it, but only pay for that horsepower when they need it. So this is a perfect case in point where everybody realizes they need valuable analytics, they need valuable insights about their customers and their customers’ activities, and time to that information is critical, there’s really better alternative than running those workloads in the public cloud, running those workloads in Azure.


Jay Livens:  I agree. I think the other piece that people may not think about or maybe concerned about is, so you got your large amount of data that you are doing critical analysis on top of.  But the problem comes on how do you maintain that data, right? Because you maybe typical generating a lot of data on premises or elsewhere or in super systems. And so thinking about how you keep that up-to-date and current, now some data sources could be maybe internet public lines, maybe there’s like weather trends or temperature, you can get that easily.  But what about, your corporate tools, your corporate data? Whatever data you have in your system and how do you get that? So another piece to consider is, how do I sort of make sure that you know large scale data analytics warehouse is in sync or have the same data that is from production, but it’s not so onetime thing, it needs to be updated periodically, so that you can make sure you have the latest trend analysis, the latest data in there, so can get those insights.

So the challenges of sort of maintaining the data is something that we’ve talked a lot about at different scenarios, whether it’s for backup or DR or DevOps, but it’s very relevant in here too.  I’m making sure that we can keep pushing that data into our large scale environment, so that we can do that analytics on information that’s current and its timely and can allow us to get those insights you know fast in a scalable way as Karl mentioned, but also with very fresh and new data, because what’s the point of doing analytics on data that’s six old, like you’ve been missing big trends.  So you know speaking about that as a piece of Vista as well.


Karl Rautenstrauch:  Yeah, no doubt, efficient, rapid, repeatable transfer is really, really key.  Something that you don’t have to babysit, the days of the bulk import are dead, right?


Giovanni Tropeano:  Right, exactly.


Karl Rautenstrauch:  And we are now on the continuous imports, the continuous upload, that’s where the world is going.


Jay Livens:  No doubt.


Giovanni Tropeano:  That’s awesome. That’s all the questions I really had gentlemen.  For anybody that’s listening don’t forget to follow Jay Livens and Karl Rautenstrauch on LinkedIn and Twitter.  And feel free to send us an email or I should say send us a note on LinkedIn or Twitter if you have questions directly to what we talked about today.

Follow Karl – @KloudKarl

Follow Jay – @JLivens

Follow Giovanni – @GioTropeano

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