Preliminary Data Taxonomy at ingestion. An opportunity for Computational Storage

Data governance has been on my mind a lot lately. With all the incessant talks and hype about Artificial Intelligence, the true value of AI comes from good data. Therefore, it is vital for any organization embarking on their AI journey to have good quality data. And the journey of the lifecycle of data in an organization starts at the point of ingestion, the data source of how data is either created, acquired to be presented up into the processing workflow and data pipelines for AI training and onwards to AI applications.

In biology, taxonomy is the scientific study and practice of naming, defining and classifying biological organisms based on shared characteristics.

And so, begins my argument of meshing these 3 topics together – data ingestion, data taxonomy and with Computational Storage. Here goes my storage punditry.

Data Taxonomy in post-injection 

I see that data, any data, has to arrive at a repository first before they are given meaning, context, specifications. These requirements are different from file permissions, ownerships, ctime and atime timestamps, the content of the ingested data stream are made to fit into the mould of the repository the data is written to. Metadata about the content of the data gives the data meaning, context and most importantly, value as it is used within the data lifecycle. However, the metadata tagging, and preparing the data in the ETL (extract load transform) or the ELT (extract load transform) process are only applied post-ingestion. This data preparation phase, in which data is enriched with content metadata, tagging, taxonomy and classification, is expensive, in term of resources, time and currency.

Elements of a modern event-driven architecture including data ingestion (Credit: Qlik)

Even in the burgeoning times of open table formats (Apache Iceberg, HUDI, Deltalake, et al), open big data file formats (Avro, Parquet) and open data formats (CSV, XML, JSON et.al), the format specifications with added context and meanings are added in and augmented post-injection.

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Making Immutability the key factor in a Resilient Data Protection strategy

We often hear “Cyber Resilience” word thrown around these days. Every backup vendor has a cybersecurity play nowadays. Many have morphed into cyber resilience warrior vendors, and there is a great amount of validation in terms of Cyber Resilience in a data protection world. Don’t believe me?

Check out this Tech Field Day podcast video from a month ago, where my friends, Tom Hollingsworth and Max Mortillaro discussed the topic meticulously with Krista Macomber, who has just become the Research Director for Cybersecurity at The Futurum Group (Congrats, Krista!).

Cyber Resilience, as well articulated in the video, is not old wine in a new bottle. The data protection landscape has changed significantly since the emergence of cyber threats and ransomware that it warrants the coining of the Cyber Resilience terminology.

But I want to talk about one very important cog in the data protection strategy, of which cyber resilience is part of. That is Immutability, because it is super important to always consider immutable backups as part of that strategy.

It is no longer 3-2-1 anymore, Toto. 

When it comes to backup, I always start with 3-2-1 backup rule. 3 copies of the data; 2 different media; 1 offsite. This rule has been ingrained in me since the day I entered the industry over 3 decades ago. It is still the most important opening line for a data protection specialist or a solution architect. 3-2-1 is the table stakes.

Yet, over the years, the cybersecurity threat landscape has moved closer and closer to the data protection, backup and recovery realm. This is now a merged super-segment pangea called cyber resilience. With it, the conversation from the 3-2-1 backup rule in these last few years is now evolving into something like 3-2-1-1-0 backup rule, a modern take of the 3-2-1 backup rule. Let’s take a look at the 3-2-1-1-0 rule (simplified by me).

The 3-2-1-1-0 Backup rule (Credit: https://www.dataprise.com/services/disaster-recovery/baas/)

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Data Trust and Data Responsibility. Where we should be at before responsible AI.

Last week, there was a press release by Qlik™, informing of a sponsored TechTarget®‘s Enterprise Strategy Group (ESG) about the state of responsible AI practices across industries. The study highlighted critical gaps in the approach to responsible AI, ethical AI practices and AI regulatory compliances. From the study, Qlik™ emphasizes on having a solid data foundation. To get to that bedrock foundation, we must trust the data and we must be responsible for the kinds of data that built that foundation. Hence, Data Trust and Data Responsibility.

There is an AI boom right now. Last year alone, the AI machine and its hype added in USD$2.4 trillion market cap to US tech companies. 5 months into 2024, AI is still supernova hot. And many are very much fixated to the infallible fables and tales of AI’s pompous splendour. It is this blind faith that I see many users and vendors alike sidestepping the realities of AI in the present state as it is.

AI is not always responsible. Then it begs the question, “Are we really working with a responsible set of AI applications and ecosystems“?

Responsible AI. Are we there yet?

AI still hallucinates, unfortunately. The lack of transparency of AI applications coming to a conclusion and a recommended decision is not always known. What if you had a conversation with ChatGPT and it says that you are dead. Well, that was exactly what happened when Tom’s Guide writer, Tony Polanco, found out from ChatGPT that he passed away in September 2021.

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NIST CSF 2.0 brings Data Governance into the light

In the past weekend, I watched a CNA Insider video delving into Data Theft in Malaysia. It is titled “Data Theft in Malaysia: How your personal information may be exploited | Cyber Scammed”.

You can watch the 45-minute video below.

Such dire news is nothing new. We Malaysians are numbed to those telemarketers calling and messaging to offer their credit card services, loans, health spa services. You name it; there is something to sell. Of course, these “services” are mostly innocuous, but in recent years, the forms of scams are risen up several notches and severity levels. The levels of sophistication, the impacts, and the damages (counting financial and human casualties) have rocketed exponentially. Along with the news, mainstream and others, the levels of awareness and interests in data, especially PII (personal identifiable information) in Malaysians, are at its highest yet.

Yet the data theft continues unabated. Cybersecurity Malaysia (CSM), just last week, reported a 1,192% jump of data theft cases in Malaysia in 2023. In an older news last year, cybersecurity firm Surf Shark ranked Malaysia as the 8th most breached country in Q3 of 2023.
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Disaggregation and Composability vital for AI/DL models to scale

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

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

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. ]