Tag Archives: devops

The AI Platformization of Storage – The Data Intelligence Platform

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The IT industry uses the word “platform” all the time. Often, I find myself shifting between the many jargons circling “platform”, loosely. I am pretty sure many others are doing so as well.

I finally found the word “platformization” giving right vibes in a meaningful way in February last year, when Palo Alto Networks pivoted to platformization. Their stock tumbled that day. Despite the ambiguous definition “platformization” when Palo Alto Networks (PANW) mentioned it, I understood their strategy.

Defence-in-Depth in cybersecurity wasn’t exactly working for many organizations. Cybersecurity point solutions peppered the landscape. There were so many leaks and gaps. Platformization, from the PANW‘s point-of-view, is the reverse C&C (command & control), if you know the cybersecurity speak. PANW wants to take charge all the way for all things cybersecurity, and it made sense to me from a data perspective.

Paradigm shift for Data. 

For the longest time, networked storage technology has been about data sharing, be it blocks, files or objects. The data from these protocols is delivered over the network, mostly over Fibre Channel and/or Ethernet (although I remembered implementing NFS over Asynchronous Transfer Mode at Sarawak Shell in East Malaysia), in a client-server fashion.

By late 2000s onwards, unified storage or multi-protocol storage (where the storage array is able to served all 3 SAN, NAS and S3 services) was all the rage. All the prominent enterprise storage vendors had a solution or two in their solutions portfolio. I started viewing networked storage as a Data Services Platform which I started explaining it in 2017. Within the data services platform, various features revolve around my A.P.P.A.R.M.S.C. framework (I crafted the initial framework in 2000, thanks to Jon Toigo‘s book – The Holy Grail of Data Management). This framework and the approach I used for my consulting and analyst work worked well and is still relevant, even after 25 years.

But AI is changing the data landscape. AI is changing the way data is consumed and processed through the networks between the compute layer and the storage layer. It is indeed, for me, a paradigm shift of data, and the storage layer, better known as AI Data Infrastructure now, is shifting as well. And this shift will accelerate the exponential growth in innovations, with AI and super-charged data leading the way.

DDN Infinia Data Intelligence Platform (screencapture from DDN Beyond Artificial webinar)

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The Starbucks model for Storage-as-a-Service

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Starbucks™ is not a coffee shop. It purveys beyond coffee and tea, and food and puts together the yuppie beverages experience. The intention is to get the customers to stay as long as they can, and keep purchasing the Starbucks’ smorgasbord of high margin provisions in volume. Wifi, ambience, status, coffee or tea with your name on it (plenty of jokes and meme there), energetic baristas and servers, fancy coffee roasts and beans et. al. All part of the Starbucks™-as-a-Service pleasurable affair that intends to lock the customer in and have them keep coming back.

The Starbucks experience

Data is heavy and they know it

Unlike compute and network infrastructures, storage infrastructures holds data persistently and permanently. Data has to land on a piece of storage medium. Coupled that with the fact that data is heavy, forever growing and data has gravity, you have a perfect recipe for lock-in. All storage purveyors, whether they are on-premises data center enterprise storage or public cloud storage, and in between, there are many, many methods to keep the data chained to a storage technology or a storage service for a long time. The storage-as-a-service is like tying the cow to the stake and keeps on milking it. This business model is very sticky. This stickiness is also a lock-in mechanism.

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Storageless shan’t be thy name

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Storageless??? What kind of a tech jargon is that???

This latest jargon irked me. Storage vendor NetApp® (through its acquisition of Spot) and Hammerspace, a metadata-driven storage agnostic orchestration technology company, have begun touting the “storageless” tech jargon in hope that it will become an industry buzzword. Once again, the hype cycle jargon junkies are hard at work.

Clear, empty storage containers

Clear, nondescript storage containers

It is obvious that the storageless jargon wants to ride on the hype of serverless computing, an abstraction method of computing resources where the allocation and the consumption of resources are defined by pieces of programmatic code of the running application. The “calling” of the underlying resources are based on the application’s code, and thus, rendering the computing resources invisible, insignificant and not sexy.

My stand

Among the 3 main infrastructure technology – compute, network, storage, storage technology is a bit of a science and a bit of dark magic. It is complex and that is what makes storage technology so beautiful. The constant innovation and technology advancement continue to make storage as a data services platform relentlessly interesting.

Cloud, Kubernetes and many data-as-a-service platforms require strong persistent storage. As defined by NIST Definition of Cloud Computing, the 4 of the 5 tenets – on-demand self-service, resource pooling, rapid elasticity, measured service – demand storage to be abstracted. Therefore, I am all for abstraction of storage resources from the data services platform.

But the storageless jargon is doing a great disservice. It is not helping. It does not lend its weight glorifying the innovations of storage. In fact, IMHO, it felt like a weighted anchor sinking storage into the deepest depth, invisible, insignificant and not sexy. I am here dutifully to promote and evangelize storage innovations, and I am duly unimpressed with such a jargon.

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Paradigm shift of Dev to Storage Ops

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[ Disclosure: I was invited by GestaltIT as a delegate to their Storage Field Day 19 event from Jan 22-24, 2020 in the Silicon Valley USA. My expenses, travel, accommodation and conference fees were covered by GestaltIT, the organizer and I was not obligated to blog or promote the vendors’ technologies presented at the event. The content of this blog is of my own opinions and views ]

A funny photo (below) came up on my Facebook feed a couple of weeks back. In an honest way, it depicted how a developer would think (or the lack of thinking) about the storage infrastructure designs and models for the applications and workloads. This also reminded me of how DBAs used to diss storage engineers. “I don’t care about storage, as long as it is RAID 10“. That was aeons ago 😉

The world of developers and the world of infrastructure people are vastly different. Since cloud computing birthed, both worlds have collided and programmable infrastructure-as-code (IAC) have become part and parcel of cloud native applications. Of course, there is no denying that there is friction.

Welcome to DevOps!

The Kubernetes factor

Containerized applications are quickly defining the cloud native applications landscape. The container orchestration machinery has one dominant engine – Kubernetes.

In the world of software development and delivery, DevOps has taken a liking to containers. Containers make it easier to host and manage life-cycle of web applications inside the portable environment. It packages up application code other dependencies into building blocks to deliver consistency, efficiency, and productivity. To scale to a multi-applications, multi-cloud with th0usands and even tens of thousands of microservices in containers, the Kubernetes factor comes into play. Kubernetes handles tasks like auto-scaling, rolling deployment, computer resource, volume storage and much, much more, and it is designed to run on bare metal, in the data center, public cloud or even a hybrid cloud.

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