Tag Archives: AI

Nurturing Data Governance for Cybersecurity and AI

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Towards the middle of the 2000s, I started getting my exposure in Data Governance. This began as I was studying and practising to be certified as an Oracle Certified Professional (OCP) circa 2002-2003. My understanding of the value of data and databases in the storage world, now better known as data infrastructure, grew and expanded quickly. I never gotten my OCP certification because I ran out of money investing in the 5 required classes that included PL/SQL, DBA Admin I and II, and Performance Tuning. My son, Jeffrey was born in 2002, and money was tight.

The sentiment of data governance of most organizations I have engaged with at that time, and over the next course of almost 18 years or so, pre-Covid, the practice of data governance was to comply to some regulatory requirements. 

All that is changing. Early 2024, NIST released the second version of their Cybersecurity Framework (CSF). CSF 2.0 placed Data Governance in the center of the previous 5 pillars of CSF 1.1. The diagram below shows the difference between the versions.

High level change of Cybersecurity Framework 1.1 to 2.0.

Ripples like this in my data management radar are significant, noticeable and important to me. I blogged about it in my April 2024 blog “NIST CSF 2.0 brings Data Governance into the Light“.

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The All-Important Storage Appliance Mindset for HPC and AI projects

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I am strong believer of using the right tool to do the job right. I have said this before 2 years ago, in my blog “Stating the case for a Storage Appliance approach“. It was written when I was previously working for an open source storage company. And I am an advocate of the crafter versus assembler mindset, especially in the enterprise and high- performance storage technology segments.

I have joined DDN. Even with DDN that same mindset does not change a bit. I have been saying all along that the storage appliance model should always be the mindset for the businesses’ peace-of-mind.

My view of the storage appliance model began almost 25 years. I came into NAS systems world via Sun Microsystems®. Sun was famous for running NFS servers on general Sun Solaris servers. NFS services on Unix systems. Back then, I remember arguing with one of the Sun distributors about the tenets of running NFS over 100Mbit/sec Ethernet on Sun servers. I was drinking Sun’s Kool-Aid big time.

When I joined Network Appliance® (now NetApp®) in 2000, my worldview of putting software on general purpose servers changed. Network Appliance®, had one product family, the FAS700 (720, 740, 760) family. All NetApp® did was to serve NFS services in the beginning. They were the NAS filers and nothing else.

I was completed sold on the appliance way with NetApp®. Firstly, it was my very first time knowing such network storage services could be provisioned with an appliance concept. This was different from Sun. I was used to managing NFS exports on a Sun SPARCstation 20 to Unix clients in the network.

Secondly, my mindset began to shape that “you have to have the right tool to the job correctly and extremely well“. Well, the toaster toasts bread very well and nothing else. And the fridge (an analogy used by Dave Hitz, I think) does what it does very well too. That is what the appliance does. You definitely cannot grill a steak with a bread toaster, just like you can’t run an excellent, ultra-high performance storage services to serve the demanding AI and HPC applications on a general server platform. You have to have a storage appliance solution for High-Speed Storage.

That little Network Appliance® toaster award given out to exemplary employees stood vividly in my mind. The NetApp® tagline back then was “Fast, Simple, Reliable”. That solidifies my mindset for the high-speed storage in AI and HPC projects in present times.

DDN AI400X2 Turbo Appliance

Costs Benefits and Risks

I like to think about what the end users are thinking about. There are investments costs involved, and along with it, risks to the investments as well as their benefits. Let’s just simplify and lump them into Cost-Benefits-Risk analysis triangle. These variables come into play in the decision making of AI and HPC projects.

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And great AI starts with good Data Management

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Processing data has become more expensive.

Somewhere, there is a misconception that data processing is cheap. That stems from the well-known pricings of the capacities of public cloud storage that are a fraction of cents per month. But data in storage has to be worked upon, and has to be built up and protected to increase its value. Data has to be processed, moved, shared, and used by applications. Data induce workloads. Nobody keeps data stored forever and never be used again. Nobody buys storage just for capacity alone.

We have a great saying in the industry. No matter, where the data moves, it will land in a storage. So, it is clear that data does not exist in ether. And yet, I often see how little attention and prudence and care, when it comes to data infrastructure and data management technologies, the very components that are foundational to great data.

Great data management for Great AI

AI is driving up costs in data processing

A few recent articles drew my focus into the cost of data processing.

Here is one posted by a friend on Facebook. It is titled “The world is running out of data to feed AI, experts warn.”

My first reaction was, “How can we run out of data“? We have so much data in the world today that the 175 zettabytes predicted by IDC when we reach 2025 might be grossly inaccurate. According to Exploding Topics, it is estimated that we create 328.77TB of data per day, 120 zettabytes per year. While I cannot vouch for the accuracy of the numbers, the numbers are humongous.

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Societies in crisis. Data at Fault

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The deluge of data is astounding. We get bombarded and attacked by data every single waking minute of our day. And it will get even worse. Our senses will be numbed into submission. In the end, I ask in the sense of it all. Do we need this much information force fed to us at every second of our lives?

We have heard about the societies a decade ago living in the Information Age and now, we have touted the Social Age. TikTok, Youtube, Twitter, Spotify, Facebook, Metaverse(s) and so many more are creating societies that are defined by data, controlled by data and governed by data. Data can be gathered so easily now that it is hard to make sense of what is relevant or what is useful. Even worse, private data, information about the individual is out there either roaming without any security guarding it, or sold like a gutted fish in the market. The bigger “whales” are peddled to the highest bidder. So, to the prudent human being, what will it be?

Whatever the ages we are in, Information or Social, does not matter anymore. Data is used to feed the masses; Data is used to influence the population; Data is the universal tool to shape the societies, droning into submission and ruling them to oblivion.

Societies burn

GIGO the TikTok edition

GIGO is Garbage In Garbage Out. It is an age old adage to folks who have worked with data and storage for a long time. You put in garbage data, you get garbage output results. And if you repeat the garbage in enough times, you would have created a long lasting garbage world. So, imagine now that the data is the garbage that is fed into the targeted society. What will happen next is very obvious. A garbage society.

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Storage IO straight to GPU

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The parallel processing power of the GPU (Graphics Processing Unit) cannot be denied. One year ago, nVidia® overtook Intel® in market capitalization. And today, they have doubled their market cap lead over Intel®,  [as of July 2, 2021] USD$510.53 billion vs USD$229.19 billion.

Thus it is not surprising that storage architectures are changing from the CPU-centric paradigm to take advantage of the burgeoning prowess of the GPU. And 2 announcements in the storage news in recent weeks have caught my attention – Windows 11 DirectStorage API and nVidia® Magnum IO GPUDirect® Storage.

nVidia GPU

Exciting the gamers

The Windows DirectStorage API feature is only available in Windows 11. It was announced as part of the Xbox® Velocity Architecture last year to take advantage of the high I/O capability of modern day NVMe SSDs. DirectStorage-enabled applications and games have several technologies such as D3D Direct3D decompression/compression algorithm designed for the GPU, and SFS Sampler Feedback Streaming that uses the previous rendered frame results to decide which higher resolution texture frames to be loaded into memory of the GPU and rendered for the real-time gaming experience.

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The Edge is coming! The Edge is coming!

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Actually, Edge Computing is already here. It has been here on everyone’s lips for quite some time, but for me and for many others, Edge Computing is still a hodgepodge of many things. The proliferation of devices, IoT, sensor, end points being pulled into the ubiquitous term of Edge Computing has made the scope ever changing, and difficult to pin down. And it is this proliferation of edge devices that will generate voluminous amount of data. Obvious questions emerge:

  • How to do you store all the data?
  • How do you process all the data?
  • How do you derive competitive value from the data from these edge devices?
  • How do you securely transfer and share the data?

From the storage technology perspective, it might be easier to observe what are the traits of the data generated on the edge device. In this blog, we also observe what could some new storage technologies out there that could be part of the Edge Computing present and future.

Edge Computing overview - Cloud to Edge to Endpoint

Edge Computing overview – Cloud to Edge to Endpoint

Storage at the Edge

The mantra of putting compute as close to the data and processing it where it is stored is the main crux right now, at least where storage of the data is concerned. The latency to the computing resources on the cloud and back to the edge devices will not be conducive, and in many older settings, these edge devices in factory may not be even network enabled. In my last encounter several years ago, there were more than 40 interfaces, specifications and protocols, most of them proprietary, for the edge devices. And there is no industry wide standard for these edge devices too.

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Is General Purpose Object Storage disenfranchised?

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[Disclosure: I am 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 will be covered by GestaltIT, the organizer and I am not obligated to blog or promote the vendors’ technologies to be presented at this event. The content of this blog is of my own opinions and views]

This is NOT an advertisement for coloured balls.

This is the license to brag for the vendors in the next 2 weeks or so, as we approach the 2020 new year. This, of course, is the latest 2019 IDC Marketscape for Object-based Storage, released last week.

My object storage mentions

I have written extensively about Object Storage since 2011. With different angles and perspectives, here are some of them:

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Whither HPC, HPE?

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HPE is acquiring Cray Inc. Almost 3 years ago, HPE acquired SGI. Back in 2017, HPE partnered WekaIO, and invested big in the latest Series C funding of WekaIO just weeks ago.

Cray, SGI and WekaIO are all strong HPC technology companies. Given the strong uptick in the HPC market, especially commercial HPC, we cannot deny HPE’s ambition to become the top SuperComputing and HPC vendor in the industry. Continue reading →

Own the Data Pipeline

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[Preamble: I was a delegate of Storage Field Day 15 from Mar 7-9, 2018. My expenses, travel and accommodation were paid for by GestaltIT, the organizer and I was not obligated to blog or promote the technologies presented at this event. The content of this blog is of my own opinions and views]

I am a big proponent of Go-to-Market (GTM) solutions. Technology does not stand alone. It must be in an ecosystem, and in each industry, in each segment of each respective industry, every ecosystem is unique. And when we amalgamate data, the storage infrastructure technologies and the data management into the ecosystem, we reap the benefits in that ecosystem.

Data moves in the ecosystem, from system to system, north to south, east to west and vice versa, random, sequential, ad-hoc. Data acquires different statuses, different roles, different relevances in its lifecycle through the ecosystem. From it, we derive the flow, a workflow of data creating a data pipeline. The Data Pipeline concept has been around since the inception of data.

To illustrate my point, I created one for the Oil & Gas – Exploration & Production (EP) upstream some years ago.

 

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NetApp and IBM gotta take risks

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[Preamble: I was a delegate of Storage Field Day 15 from Mar 7-9, 2018. My expenses, travel and accommodation were paid for by GestaltIT, the organizer and I was not obligated to blog or promote the technologies presented at this event. The content of this blog is of my own opinions and views]

Storage Field Day 15 was full of technology. There were a few avant garde companies in the line-up which I liked but unfortunately NetApp and IBM were the 2 companies that came in at the least interesting end of the spectrum.

IBM presented their SpectrumProtect Plus. The data protection space, especially backup isn’t exactly my forte when it comes to solution architecture but I know enough to get by. However, as IBM presented, there were some many questions racing through my mind. I was interrupting myself so much because almost everything presented wasn’t new to me. “Wait a minute … didn’t Company X already had this?” or “Company Y had this years ago” or “Isn’t this…??”

I was questioning myself to validate my understanding of the backup tech shared by the IBM SpectrumProtect Plus team. And they presented with such passion and gusto which made me wonder if I was wrong in the first place. Maybe my experience and knowledge in the backup software space weren’t good enough. But then the chatter in the SFD15 Slack channel started pouring in. Comments, unfortunately were mostly negative, and jibes became jokes. One comment, in particular, nailed it. “This is Veeam 0.2“, and then someone else downgraded to version 0.1.

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