Joey v2: IaaS – to – Data Platform

Hello Everyone,

It’s been a little while since I blogged, but I thought I would blog on a topic that I feel will be the next big “paradigm shift” (wow, did I really use a buzz phrase?) in cloud: the shift from modernizing IaaS to focus on the app and/or the data for a business. Joey, that sounds very catchy, but what do you mean? As we continue in our cloud journey, we’re all noticing that big changes in infrastructure services like virtualization and containerization are still improving but at incremental levels.

What I mean is that major ecosystem auin IaaS operating technologies aren’t really occurring; they’re just iterating. WHOA, Joey, what about Docker, Kubernetes, <insert cool buzzword bingo term here>? Those are all exciting new ways to do the same thing we’ve always been doing: virtualization. Improvements to these platforms are iterations on past versions, not holistic changes in deployment. Once a company embraces CI/CD – continuous integration/continuous deployment – and containers, what’s left? Well, cool new features for their CI/CD and containers platforms will be released but nothing earth-shattering. This lack of radical innovation in IaaS results in a renewed focus on the technologies that ARE rapidly evolving and disrupting the way we consume IT: data.

I would argue, it’s always been about the data. Think about this: when was the last time that you thought “Wow, that load-balanced VM Scale Set produced/saved a lot of money for my company!” No, it’s usually, “That tweak in Application X really helped me see our data different way and allowed me to achieve XYZ with ABC results.” So, it makes sense that large public providers like Microsoft, AWS, and Google have (re)focused on data platforms and their supporting application operating platforms.

My point being is that, personally, I feel that my skills need to become even sharper on the data platform elements of public clouds (my cloud of choice being Azure) with slightly less focus on the infrastructure bits. Does that mean I will neglect knowledge development on the infrastructure side? No, not at all. What I am saying is that I will double my efforts on an area that I’ve only passingly learned about in the past: data (and Big Data). That said, here are my focuses and why I’m taking the time to learn more about these areas:

  • Azure HDInsight
    • Why?
      • HDInsight is a platform that allows multiple open-source path to derive actionable insights from large amounts of data either already collected or data that can is real-time/streaming. Machine learning abilities and Microsoft-backed output sinks (PowerBI, Azure Data Lake, Azure Blob, etc.) are also reasons for looking into this 7-in-1 data platform.
      • Microsoft Customer Case Study – Rubikloud
    • Azure Cosmos DB
      • Why?
        • Cosmos DB allows for a singular NoSQL (wait, what is NoSQL?) datastore backend to be front-ended by APIs that are pretty well known (SQL, MongoDB, Cassandra, Gremlin, etc.) AND is geographically dispersed, scalable, and secure. Cosmos will allow for us to deploy a data platform regardless of how the application needs to talk to it without the normal capacity considerations. The only real configuration “gotcha” is identifying your partition keys. Here’s how you do that part.
        • Microsoft Customer Case Study – STRV


So, do you share my perspective? Please comment below!


Thanks for taking time out to read and I’ll see you soon with a new blog on IoT readings and automations…from my own garage!


Joey Brakefield, MCSE
Sr. Solutions Architect, Ensono

** cross-posted to LinkedIn here **

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