Category Archives: Artificial Intelligence

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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Disaggregation and Composability vital for AI/DL models to scale

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New generations of applications and workloads like AI/DL (Artificial Intelligence/Deep Learning), and HPC (High Performance Computing) are breaking the seams of entrenched storage infrastructure models and frameworks. We cannot continue to scale-up or scale-out the storage infrastructure to meet these inundating fluctuating I/O demands. It is time to look at another storage architecture type of infrastructure technology – Composable Infrastructure Architecture.

Infrastructure is changing. The previous staid infrastructure architecture parts of compute, network and storage have long been thrown of the window, precipitated by the rise of x86 server virtualization almost 20 years now. It triggered a tsunami of virtualizing everything, including storage virtualization, which eventually found a more current nomenclature – Software Defined Storage. Both storage virtualization and software defined storage (SDS) are similar and yet different and should be revered through different contexts and similar goals. This Tech Target article laid out both nicely.

As virtualization raged on, converged infrastructure (CI) which evolved into hyperconverged infrastructure (HCI) went fever pitch for a while. Companies like Maxta, Pivot3, Atlantis, are pretty much gone, with HPE® Simplivity and Cisco® Hyperflex occasionally blipped in my radar. In a market that matured very fast, HCI is now dominated by Nutanix™ and VMware®, with smaller Microsoft®, Dell EMC® following them.

From HCI, the attention of virtualization has shifted something more granular, more scalable in containerization. Despite a degree of complexity, containerization is taking agility and scalability to the next level. Kubernetes, Dockers are now mainstay nomenclature of infrastructure engineers and DevOps. So what is driving composable infrastructure? Have we reached the end of virtualization? Not really.

Evolution of infrastructure. Source: IDC

It is just that one part of the infrastructure landscape is changing. This new generation of AI/ML workloads are flipping the coin to the other side of virtualization. As we see the diagram above, IDC brought this mindset change to get us to Think Composability, the next phase of Infrastructure.

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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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Truthful information under attack. The call for Data Preservation

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The slogan of The Washington Post is “Democracy Dies in Darkness“. Although not everyone agrees with the US brand of democracy, the altruism of WaPo‘s (the publication’s informal name) slogan is a powerful one. The venerable newspaper remains the beacon in the US as one of the most trustworthy sources of truthful, honest information.

4 Horsemen of Apocalypse with the 5th joining

Misinformation

Misinformation has become a clear and present danger to humanity. Fake news, misleading information, lies are fueling and propelling the propaganda and agenda of the powerful (and the deranged). Facts are blurred, obfuscated, and even removed and replaced with misinformation to push for the undesirable effects that will affect the present and future generations.

The work of SNIA®

Data preservation is part of Data Management. More than a decade ago, SNIA® has already set up a technical work group (TWG) on Long Term Retention and proposed a format for long-term storage of digital format. It was called SIRF (Self-contained Information Retention Format). In the words of SNIA®, “The SIRF format enables long-term physical storage, cloud storage and tape-based containers effective and efficient ways to preserve and secure digital information for many decades, even with the ever-changing technology landscape.”

I don’t think battling misinformation was SNIA®’s original intent, but the requirements for a vendor-neutral organization as such to present and promote long term data preservation is more needed than ever. The need to protect the truth is paramount.

SNIA® continues to work with many organizations to create and grow the ecosystem for long term information retention and data preservation.

NFTs can save data

Despite the hullabaloo of NFTs (non-fungible tokens), which is very much soiled and discredited by the present day cryptocurrency speculations, I view data (and metadata) preservation as a strong use case for NFTs. The action is to digitalize data into an NFT asset.

Here are a few arguments:

  1. NFTs are unique. Once they are verified and inserted into the blockchain, they are immutable. They cannot be modified, and each blockchain transaction is created with one never to be replicated hashed value.
  2. NFTs are decentralized. Most of the NFTs we know of today are minted via a decentralized process. This means that the powerful cannot (most of the time), effect the NFTs state according to its whims and fancies. Unless the perpetrators know how to manipulate a Sybil attack on the blockchain.
  3. NFTs are secure. I have to set the knowledge that NFTs in itself is mostly very secure. Most of the high profiled incidents related to NFTs are more of internal authentication vulnerabilities and phishing related to poor security housekeeping and hygiene of the participants.
  4. NFTs represent authenticity. The digital certification of the NFTs as a data asset also define the ownership and the originality as well. The record of provenance is present and accounted for.

Since NFTs started as a technology to prove the assets and artifacts of the creative industry, there are already a few organizations that playing the role. Orygin Art is one that I found intriguing. Museums are also beginning to explore the potential of NFTs including validating and verifying the origins of many historical artifacts, and digitizing these physical assets to preserve its value forever.

The technology behind NFTs are not without its weaknesses as well but knowing what we know today, the potential is evident and power of the technology has yet to be explored fully. It does present a strong case in preserving the integrity of truthful data, and the data as historical artifacts.

Protect data safety and data integrity

Misinformation is damaging. Regardless if we believe the Butterfly Effect or not, misinformation can cause a ripple effect that could turn into a tidal wave. We need to uphold the sanctity of Truth, and continue to protect data safety and data integrity. The world is already damaged, and it will be damaged even more if we allow misinformation to permeate into the fabric of the global societies. We may welcome to a dystopian future, unfortunately.

This blog hopes to shake up the nonchalant state that we view “information” and “misinformation” today. There is a famous quote that said “Repeat a lie often enough and it becomes the truth“. We must lead the call to combat misinformation. What we do now will shape the generations of our present and future. Preserve Truth.

WaPo “Democracy Dies in Darkness”

[ Condolence: Japan Prime Minister, Shinzo Abe, was assassinated last week. News sources mentioned that the man who killed him had information that the slain PM has ties to a religious group that bankrupted his mother. Misinformation may played a role in the killing of the Japanese leader. ]

Is there no end to the threat of ransomware?

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I find it blasphemous that with all the rhetoric of data protection and cybersecurity technologies and solutions in the market today, the ransomware threats and damages have grown proportionately larger each year. In a recent report by Kaspersky on Anti-Ransomware Day May 12th, 9 out of 10 of organizations previously attacked by ransomware are willing to pay again if attacked again. A day before my scheduled talk in Surabaya East Java 2 weeks’ back, the chatter through the grapevine was one bank in Indonesia was attacked by ransomware on that day. These news proved how virulent and dangerous the ransomware scourge is and has become.

And the question that everyone wants an answer to is … why are ransomware threats getting bigger and more harmful and there are no solutions to it? 

Digital transformation and its data are very attractive targets

Today, all we hear from the data protection and storage vendors are recovery, restore that data blah, blah, blah and more blah, blah, blahs. The end point EDR (endpoint detection and response) solutions say they can stop it; the cybersecurity experts preach depth in defense; and the network security guys say use perimeter fencing. And the anti-phishing chaps say more awareness and education required. One or all have not worked effectively these few years. Ransomware’s threats and damages are getting worse. Why?

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Stating the case for a Storage Appliance approach

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I was in Indonesia last week to meet with iXsystems™‘ partner PT Maha Data Solusi. I had the wonderful opportunity to meet with many people there and one interesting and often-replayed question arose. Why aren’t iX doing software-defined-storage (SDS)? It was a very obvious and deliberate question.

After all, iX is already providing the free use of the open source TrueNAS® CORE software that runs on many x86 systems as an SDS solution and yet commercially, iX sell the TrueNAS® storage appliances.

This argument between a storage appliance model and a storage storage only model has been debated for more than a decade, and it does come into my conversations on and off. I finally want to address this here, with my own views and opinions. And I want to inform that I am open to both models, because as a storage consultant, both have their pros and cons, advantages and disadvantages. Up front I gravitate to the storage appliance model, and here’s why.

My story of the storage appliance begins …

Back in the 90s, most of my work was on Fibre Channel and NFS. iSCSI has not existed yet (iSCSI was ratified in 2003). It was almost exclusively on the Sun Microsystems® enterprise storage with Sun’s software resell of the Veritas® software suite that included the Sun Volume Manager (VxVM), Veritas® Filesystem (VxFS), Veritas® Replication (VxVR) and Veritas® Cluster Server (VCS). I didn’t do much Veritas® NetBackup (NBU) although I was trained at Veritas® in Boston in July 1997 (I remembered that 2 weeks’ trip fondly). It was just over 2 months after Veritas® acquired OpenVision. Backup Plus was the NetBackup.

Between 1998-1999, I spent a lot of time working Sun NFS servers. The prevalent networking speed at that time was 100Mbits/sec. And I remember having this argument with a Sun partner engineer by the name of Wong Teck Seng. Teck Seng was an inquisitive fella (still is) and he was raving about this purpose-built NFS server he knew about and he shared his experience with me. I detracted him, brushing aside his always-on tech orgasm, and did not find great things about a NAS storage appliance. Auspex™ was big then, and I knew of them.

I joined NetApp® as Malaysia’s employee #2. It was an odd few months working with a storage appliance but after a couple of months, I started to understand and appreciate the philosophy. The storage Appliance Model made sense to me, even through these days.

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Object Storage becoming storage lingua franca of Edge-Core-Cloud

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Data Fabric was a big buzzword going back several years. I wrote a piece talking about Data Fabric, mostly NetApp®’s,  almost 7 years ago, which I titled “The Transcendence of Data Fabric“. Regardless of storage brands and technology platforms, and each has its own version and interpretations, one thing holds true. There must be a one layer of Data Singularity. But this is easier said than done.

Fast forward to present. The latest buzzword is Edge-to-Core-Cloud or Cloud-to-Core-Edge. The proliferation of Cloud Computing services, has spawned beyond to multiclouds, superclouds and of course, to Edge Computing. Data is reaching to so many premises everywhere, and like water, data has found its way.

Edge-to-Core-to-Cloud (Gratitude thanks to https://www.techtalkthai.com/dell-technologies-opens-iot-solutions-division-and-introduces-distributed-core-architecture/)

The question on my mind is can we have a single storage platform to serve the Edge-to-Core-to-Cloud paradigm? Is there a storage technology which can be the seamless singularity of data? 7+ years onwards since my Data Fabric blog, The answer is obvious. Object Storage.

The ubiquitous object storage and the S3 access protocol

For a storage technology that was initially labeled “cheap and deep”, object storage has become immensely popular with developers, cloud storage providers and is fast becoming storage repositories for data connectors. I wrote a piece called “All the Sources and Sinks going to Object Storage” over a month back, which aptly articulate how far this technology has come.

But unknown to many (Google NASD and little is found), object storage started its presence in SNIA (it was developed in Carnegie-Mellon University prior to that) in the early 90s, then known as NASD (network attached secure disk). As it is made its way into the ANSI T10 INCITS standards development, it became known as Object-based Storage Device or OSD.

The introduction of object storage services 16+ years ago by Amazon Web Services (AWS) via their Simple Storage Services (S3) further strengthened the march of object storage, solidified its status as a top tier storage platform. It was to AWS’ genius to put the REST API over HTTP/HTTPS with its game changing approach to use CRUD (create, retrieve, update, delete) operations to work with object storage. Hence the S3 protocol, which has become the de facto access protocol to object storage.

Yes, I wrote those 2 blogs 11 and 9 years ago respectively because I saw that object storage technology was a natural fit to the burgeoning new world of storage computing. It has since come true many times over.

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Unstructured Data Observability with Datadobi StorageMAP

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Let’s face it. Data is bursting through its storage seams. And every organization now is storing too much data that they don’t know they have.

By 2025, IDC predicts that 80% the world’s data will be unstructured. IDC‘s report Global Datasphere Forecast 2021-2025 will see the global data creation and replication capacity expand to 181 zettabytes, an unfathomable figure. Organizations are inundated. They struggle with data growth, with little understanding of what data they have, where the data is residing, what to do with the data, and how to manage the voluminous data deluge.

The simple knee-jerk action is to store it in cloud object storage where the price of storage is $0.0000xxx/GB/month. But many IT departments in these organizations often overlook the fact that that the data they have parked in the cloud require movement between the cloud and on-premises. I have been involved in numerous discussions where the customers realized that they moved the data in the cloud moved too frequently. Often it was an erred judgement or short term blindness (blinded by the cheap storage costs no doubt), further exacerbated by the pandemic. These oversights have resulted in expensive and painful monthly API calls and egress fees. Welcome to reality. Suddenly the cheap cloud storage doesn’t sound so cheap after all.

The same can said about storing non-active unstructured data on primary storage. Many organizations have not been disciplined to practise good data management. The primary Tier 1 storage becomes bloated over time, grinding sluggishly as the data capacity grows. I/O processing becomes painfully slow and backup takes longer and longer. Sounds familiar?

The A in ABC

I brought up the ABC mantra a few blogs ago. A is for Archive First. It is part of my data protection consulting practice conversation repertoire, and I use it often to advise IT organizations to be smart with their data management. Before archiving (some folks like to call it tiering, but I am not going down that argument today), we must know what to archive. We cannot blindly send all sorts of junk data to the secondary or tertiary storage premises. If we do that, it is akin to digging another hole to fill up the first hole.

We must know which unstructured data to move replicate or sync from the Tier 1 storage to a second (or third) less taxing storage premises. We must be able to see this data, observe its behaviour over time, and decide the best data management practice to apply to this data. Take note that I said best data management practice and not best storage location in the previous sentence. There has to be a clear distinction that a data management strategy is more prudent than to a “best” storage premises. The reason is many organizations are ignorantly thinking the best storage location (the thought of the “cheapest” always seems to creep up) is a good strategy while ignoring the fact that data is like water. It moves from premises to premises, from on-prem to cloud, cloud to other cloud. Data mobility is a variable in data management.

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All the Sources and Sinks going to Object Storage

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The vocabulary of sources and sinks are beginning to appear in the world of data storage as we witness the new addition of data processing frameworks and the applications in this space. I wrote about this in my blog “Rethinking data. processing frameworks systems in real time” a few months ago, introducing my take on this budding new set of I/O characteristics and data ecosystem. I also started learning about the Kappa Architecture (and Lambda as well), a framework designed to craft and develop a set of amalgamated technologies to handle stream processing of a series of data in relation to time.

In Computer Science, sources and sinks are considered external entities that often serve as connectors of input and output of disparate systems. They are often not in the purview of data storage architects. Also often, these sources and sinks are viewed as black boxes, and their inner workings are hidden from the views of the data storage architects.

Diagram from https://developer.here.com/documentation/get-started/dev_guide/shared_content/topics/olp/concepts/pipelines.html

The changing facade of data stream processing presents the constant motion of data, the continuous data being altered as it passes through the many integrated sources and sinks. We are also see much of the data processed in-memory as much as possible. Thus, the data services from a traditional storage model of SAN and NAS may straggle with the requirements demanded by this new generation of data stream processing.

As the world of traditional data storage processing is expanding into data streams processing and vice versa, and the chatter of sources and sinks can no longer be ignored.

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The young report card on Decentralized Storage

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I kept this blog in my queue for over 4 months. I was reluctant to publish it because I thought the outrageous frenzies of NFTs (non-fungible tokens), metaverses and web3 were convoluting the discussions on the decentralized storage topic. 3 weeks back, a Google Trends search for these 3 opaque terms over 90 days showed that the worldwide fads were waning. Here was the Google Trends output on April 2, 2022:

Google Trends on NFT, metaverse and web3

Decentralized storage intrigues me. I like to believe in its potential and I often try to talk to people to strengthen the narratives, and support its adoption where it fits. But often, the real objectives of decentralized storage are obfuscated by the polarized conversations about cryptocurrencies that are pegged to their offerings, NFTs (non-fungible tokens), DAOs (decentralized autonomous organizations) and plenty of hyperboles with bewildering facts as well.

But I continue to seek sustainable conversations about decentralized storage without the sway of the NFTs or the cryptos. After dipping in my toes and experiencing with HODLers, and looking at the return to sanity, I believe we can discuss decentralized storage with better clarity now. The context is to position decentralized storage to the mainstream, specifically to business organizations already immersed in centralized storage. Here is my fledgling report card on decentralized storage.

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