Web3, IoT/Edge, AI, HPC, Blockchain, Cryptocurrencies, GPUs and Quantum, Cyber Risk, 5G, and BioTech point to opportunities and threats. Why are there so many big technology trends right now? Doug and Shahin discuss a framework to help make sense of why these trends point to important changes, how these trends are related, and what they mean individually and together.
In this era of “Too Much Data”, it is imperative for all organizations to understand and have a plan for Edge Computing. We discuss it all with Bill Walker, another old colleague from Sun Microsystems who is Chief Technology Officer for Edge Computing pioneer Tensor Networks .
Edge Computing is important for many reasons, not least because pretty much all the raw data will originate from outside the cloud, and the vast majority of it will be processed outside the cloud.
Edge Computing is also important because it combines all of the trends that we track at OrionX that enable Digital Transformation: IoT as the fountain of data, 5G as the way data gets transmitted, HPC and AI as the way we make sense of data, Blockchain as an important model for transactions, Cryptocurrencies as the way value is assigned and transferred, and accelerator technologies that make it all possible within an economic envelope.
Mark Himelstein, CTO of RISC-V joins us to discuss what we think will be a very important part of the information technology landscape. RISC-V has cracked the code on taking an open source community approach to chip design. We can expect to see it everywhere from embedded IoT, to communication fabrics, storage, AI, HPC, accelerators, and more. Initially created at the University of California at Berkeley, RISC-V has grown over the past 10 years to a position of serious prominence. It is clear that it will remain important in the coming decades.
OrionX is a team of industry analysts, marketing executives, and demand generation experts. With a stellar reputation in Silicon Valley, OrionX is known for its trusted counsel, command of market forces, technical depth, and original content.
Can you cover, in 30 minutes, the technical basics of IoT, 5G, HPC, AI, Blockchain (including cryptocurrencies and smart contracts), and Quantum Computing?
Yes you can! Well, I hope so, anyway, since that is what we did at the HPC-AI Advisory Council Conference – Stanford University edition. These technologies cannot be ignored. They drive the digital infrastructure of the future enterprise. We believe it is a must to know enough all of them, in one place, so they can inform your strategy, corporate and product roadmap, and your narrative.
OrionX works with clients on the impact of Digital Transformation on them, their customers, and their messages. Generally, they want to track, in one place, trends like IoT, 5G, AI, Blockchain, and Quantum Computing. And they want to know what these trends mean, how they affect each other, and when they demand action, and how to formulate and execute an effective plan. This talk is a somewhat technical summary of such discussions. Needless to say, if that describes you, please let us know.
Here’s the Slide Deck
Please take a look at the slides below and let us know what you think, or if you’d like us to take you through them.
Shahin is a technology analyst and an active CxO, board member, and advisor. He serves on the board of directors of Wizmo (SaaS) and Massively Parallel Technologies (code modernization) and is an advisor to CollabWorks (future of work). He is co-host of the @HPCpodcast, Mktg_Podcast, and OrionX Download podcast.
We are looking into the future of value, money, and payments. The result is a series of presentations, papers, and podcasts (here’s an example) that is released and updated over several months.
OrionX Research clients will automatically receive this material, and OrionX Advisory subscribers will see the essence of these in their quarterly briefings and reports. Here’s a quick preview.
What is the Big Deal?
Smart connected things, as in Internet of Things (IoT), are already participating in many transactions. But right now, people are very much in the process, directing the transaction and handling the devices.
The real revelation here is that soon, Things will be transacting with each other, and autonomously. This is like “Program Trading” that already exists on Wall Street. But what we are talking about is mainstream transactions.
You need new transaction systems to make it possible for smart connected Things to do their thing: deliver services efficiently, to people and to each other, verify delivery, and receive payment.
Core Technologies
Core technologies will include Blockchain on top of IoT platforms. Smart Contracts govern transactions.
Impact on Organizations
This post-credit-card post-cashier era is complex. Companies must track and understand lots of important details. Such systems may improve existing business processes, but their real impact is to enable new business models.
As you decide what to do when, you must
track the readiness of the technology stack,
identify use cases and build business cases,
set policies,
build processes for physical vs. digital service delivery,
evaluate options for payment processing, currencies, wallets, and exchanges,
etc.
The broad range of technologies and issues indicates the large impact that this trend will have. What is unmistakable is that it is happening and it is gaining momentum.
Shahin is a technology analyst and an active CxO, board member, and advisor. He serves on the board of directors of Wizmo (SaaS) and Massively Parallel Technologies (code modernization) and is an advisor to CollabWorks (future of work). He is co-host of the @HPCpodcast, Mktg_Podcast, and OrionX Download podcast.
Digital Transformation (DX) is coming and it will impact you and your customers. It is not just another IT upgrade, but a profound shift in what your clients and customers want, and a shift in what you offer them. It is a shift in your business model.
In a new report, OrionX goes deeper to look at the dimension of DX and to characterize this trend. OrionX clients automatically receive the full report. Here is an excerpt.
DX Started with Digitization
The Information Revolution is already changing all aspects of society, blending several technology shifts in the process and creating new ones. Digitization is at the root of it, and as it progresses, its transformative powers will change everything.
Whereas the Industrial Age helped humans to surpass their mechanical strengths, Information Age is helping humans exceed their cognitive strengths. Eventually, all organizations must transform to exploit new opportunities, repel new threats, or simply to operate in a new world.
DX is a Board Room Imperative
DX is a threat unless you turn it into an opportunity. It is the new world order, and a necessary topic for executive level attention, sponsorship, and decision.
DX is Multi-Disciplinary
What makes DX especially challenging is that it must blend several technology shifts at once; not all of them, but the right ones. Technology disruptions caused by IoT, 5G, Cloud, AI, HPC, Blockchain, Cryptocurrencies, Smart Contracts, Cybersecurity, Quantum Computing, Robotics, 3D printing, Virtual and Augmented Reality, and BioTech are all eligible to impact your and your customers.
[…]
The emerging Information Age and its global impact is the umbrella trend that drives the OrionX Research, Market Execution, and Customer Engagement work. Digital Transformation is the key market trend and vehicle towards it.
Shahin is a technology analyst and an active CxO, board member, and advisor. He serves on the board of directors of Wizmo (SaaS) and Massively Parallel Technologies (code modernization) and is an advisor to CollabWorks (future of work). He is co-host of the @HPCpodcast, Mktg_Podcast, and OrionX Download podcast.
Ethereum is described in Wikipedia as an “an open-source, public, blockchain-based distributed computing platform featuring smart contract functionality“.
How does it differ from Bitcoin? Well Bitcoin is open-source, public, distributed, and block-chain based. The difference is principally found in the terms “computing platform” and “smart contract functionality”. And there are other differences as well.
Ethereum is only two years old. It was the brainchild of wunderkind Vitalik Buterin, a Bitcoin developer, and while initial funds for the project were raised in mid-2014, the network went live in mid-2015. A foundation under Swiss law manages Ethereum.
The motivation was to have better scaling than Bitcoin, both horizontally, in terms of transaction speed, and vertically, in terms of use cases supported (implemented via smart contracts). It also has a better specified development plan, with 0, 1, and 2 versions of the software having been implemented, and version 3 (Metropolis) currently in testing.
It has been a great success, and Ether, the coin of Ethereum, now has the number two market cap among all cryptocurrencies at around $29 billion. Its value has risen dramatically during 2017, rising from $8 to well over $300.
Ethereum logo CC-BY-3.0
Contracts
There are two types of accounts in Etherland. One can have a regular cryptocurrency account, or an account can represent a smart contract. There is a virtual machine (EVM) that is said to be “Turing complete” and that supports multiple scripting languages in which contract rules can be specified.
The idea of smart contracts has been around for over two decades; blockchain with broad programmability on the chain provides a very useful technology for their implementation.
Smart contracts allow value to be exchanged between agents without existing trusted relationships. Sort of like escrow, but much more streamlined. The basic idea is to cut out the expense and complications associated with middlemen.
Use cases being explored for such smart contracts include:
Real estate leases or purchases
Securities settlement
Supply chain management
Governance, including voting
Intellectual property protection
The number of currently existing use cases is few at present, however, and they tend to be simple and related to the Ether coin itself. Some have argued that smart contracts are much harder to implement in practice than many imagine. A recent interesting one is Prism Exchange, which allows you to hold a variety of altcoins across multiple exchanges from a single application.
Mining
Ether is much quicker to mine than Bitcoin, and can process 25 transactions per second. Transaction fees are also much lower than Bitcoin, around 8 times lower currently. Blocks are generated every 12 seconds, as opposed to the 10 minute target with Bitcoin.
Like Bitcoin, Ether is mined via Proof of Work, but the intent is to move to Proof of Stake (some measure of ownership) over time. A different cryptographic hash problem, Ethash, is solved, and with this hash Ether does not benefit greatly from mining with ASICs and is therefore accessible to CPU and especially to GPU mining. “Ethash PoW is memory hard, making it basically ASIC resistant.”
Basically the algorithm is designed to consume memory bandwidth and to be GPU-friendly. So it is good news for Nvidia and AMD and Intel.
Enterprise Ethereum Alliance
The Enterprise Ethereum Alliance has grown to over 150 organizations as members and includes some of the most important technology companies and largest banks. Its purpose is to address enterprise requirements for smart contracts and blockchains. The founding members are shown in the graphic below. Mastercard and Cisco are two major companies who have also joined recently.
Banks, in particular, have interest in permissioned blockchains, so that they can retain control of their customer relationships. There is a natural tension between open distributed trust of the blockchain and centralized trust that banks provide today.
It is an exciting time. How blockchain will be deployed by the financial industry, and how it will disrupt the industry are open questions. Smart contracts allow blockchain to be even more disruptive because they provide the tools for disintermediation. Jamie Dimon may not want his traders to trade Bitcoin, but he sure wants a seat at the Ethereum “smart contracts” table.
Stephen Perrenod has lived and worked in Asia, the US, and Europe and possesses business experience across all major geographies in the Asia-Pacific region. He specializes in corporate strategy for market expansion, and cryptocurrency/blockchain on a deep foundation of high performance computing (HPC), cloud computing and big data. He is a prolific blogger and author of a book on cosmology.
There are more Things than anything else! And they’re going online in droves, adding capability but also vulnerability, and making the Secure Internet of Things (IoT), the super set of the technology trends. Secure IoT is the place with the most interdisciplinary, and therefore, the most difficult challenges. If you believe, as you should, that no-compromise cybersecurity is table stakes, then IoT means “secure IoT” and IoT by itself becomes the ultimate hashtag soup. Here is why.
IoT and Security are End to End
IoT and security require an end-to-end mindset. Today’s solutions are fragmented. Integrating them is hard and requires blending of many technologies and algorithms. It requires a team with a very broad set of expertise who have managed to understand each other and employ each other’s strengths.
#IoT
Things come in many shapes and sizes. We have to make a distinction between Small-t things, sensors and micro devices, Big-T Things, which often form meta things or Systems, or All-caps THINGS, meta systems and the so-called Distributed Autonomous Organizations (DAOs). Managing them in a coherent manner requires an expertise rubber band that has to stretch across from the microscopic to the astronomic:
Size: Tiny Things to large Things
Data: Simple signals to structured data to context-sensitive streams
Mobility: stationary Things to Things that are carried by people to self-propelled Things
Autonomy: Things that are controlled by people to the so-called DAOs
#Cybersecurity
Security is not just a technology and practice but a non-negotiable requirement that cuts across all aspect of tech. Small-t things require a whole a new approach to device and data security all the way up the supply chain, a difficult technological and operational task an area of intense innovation, for example by blending AI, IoT, and Cybersecurity disciplines or pursuing entirely new approaches. Big-T Things remain hard to secure but have more built-in resources and processing capability, making them eligible to be handled like existing IT systems.
Physical #Security
Things, scattered around, are exposed to physical tampering so it is important to physically secure them and detect any unauthorized physical access or near-field or far-field monitoring. As is often said, “security is security” so physical security and cybersecurity must be viewed and implemented as one thing: all under the umbrella of risk and threat management and #sustainability.
#Mobility
Many Things move, either by themselves (like #drones or robots) or carried by other things (pick a form of transportation) or by people (personal device or #wearables). Mobility issues from initial on-boarding to authenticated secure transmission to data streaming and data semantics to over-the-air (OTA) updates all become critical in IoT.
#Blockchain
Distributed Things that generate lots of data, require secure authenticated communications, are possibly subject to compliance regulations, may need digital rights management (#DRM), or need verifiable delivery… are the kind of use case that Blockchain technology can address very well. IoT applications are often natural candidates. (see also the OrionX Download podcast series on Blockchain.)
#BigData
Connected devices generate data. Making sense of that data requires Big Data practices and technologies: data lakes, data science, storage, search, apps, etc.
#AI
When you have so much data, invariably you will find an opportunity for AI (see the OrionX/ai page for a repository of reports), either via #MachineLearning which uses the data, or #ExpertSystems for policy management which decides what to do about the insight that the data generates. AI framewroks, especially the #DeepLearning variety, are #HPC oriented and require knowledge of HPC systems and algorithms.
There will come a time when another emerging trend, #QuantumComputing, will be useful for AI and cybersecurity.
#Robotics
If Things are smart enough and can move, they become robots. Distributed Autonomous Objects (DAOs) use and generate data and require a host of security, processing, control, policy, etc.
#Cloud
All of this processing happens in the cloud somewhere, so all cloud technologies come into play. The required expertise will cover the gamut: app development, streaming data, dev-ops, elasticity, service level assurance, API management, microservices, data storage, multi-cloud reliability, etc.
Legal Framework
IoT provides new kinds of information extraction, manipulation, and control. Just the AI and Robotics components are enough to pose a challenge for existing legal systems. Ultimately, it requires an ethics framework that can be used to create a proper legal system all the while ensuring widespread communication so organizational structures and culture can keep up with it.
Summary
Not every IoT deployment will need all of the above, but as you try to stitch together a strategy for your IoT project, it is imperative to do so with a big picture in mind. IoT is the essence of #DigitalTransformation and touches all aspects of your organization. It’s quite a challenge to make it seamless, and you’ll need specialists, generalists, and not just analysts but also “synthesists”.
In the OrionX 2016 Technology Issues and Predictions blog, we said “If you missed the boat on cloud, you can’t miss it on IoT too”, and “IoT is where Big Data Analytics, Cognitive Computing, and Machine Learning come together for entirely new ways of managing business processes.” Later, in February of 2016, my colleague Stephen Perrenod’s blog IoT: The Ultimate Convergence, further set the scene. Today, we see those predictions have become reality while IoT is poised for even more convergence.
We will cover these topics in future episodes of The OrionX Download podcast, identifying the salient points. As usual, we will go one or two levels below the surface to understand not just what these technologies do, but how they do it and how they came about. That will in turn, help put new developments in perspective and explain why they may or may not address a pain point.
Shahin is a technology analyst and an active CxO, board member, and advisor. He serves on the board of directors of Wizmo (SaaS) and Massively Parallel Technologies (code modernization) and is an advisor to CollabWorks (future of work). He is co-host of the @HPCpodcast, Mktg_Podcast, and OrionX Download podcast.
Here at OrionX.net, our research agenda is driven by the latest developments in technology. We are also fortunate to work with many tech leaders in nearly every part of the “stack”, from chips to apps, who are driving the development of such technologies.
In recent years, we have had projects in Cryptocurrencies, IoT, AI, Cybersecurity, HPC, Cloud, Data Center, … and often a combination of them. Doing this for several years has given us a unique perspective. Our clients see value in our work since it helps them connect the dots better, impacts their investment decisions and the options they consider, and assists them in communicating their vision and strategies more effectively.
We wanted to bring that unique perspective and insight directly to you. “Simplifying the big ideas in technology” is how we’d like to think of it.
The OrionX Download is both a video slidecast (visuals but no talking heads) and an audio podcast in case you’d like to listen to it.
Every two weeks, co-hosts Dan Olds and Shahin Khan, and other OrionX analysts, discuss some of the latest and most important advances in technology. If our research has a specially interesting finding, we’ll invite guests to probe the subject and add their take.
Please give it a try and let us know what we can do better or if you have a specific question or topic that you’d like us to cover.
Shahin is a technology analyst and an active CxO, board member, and advisor. He serves on the board of directors of Wizmo (SaaS) and Massively Parallel Technologies (code modernization) and is an advisor to CollabWorks (future of work). He is co-host of the @HPCpodcast, Mktg_Podcast, and OrionX Download podcast.
The data center market is hot, especially now that we are getting a raft of new stuff, from promising non-Intel chips and system architectures to power and cooling optimizations to new applications in Analytics, IoT, and Artificial Intelligence.
Here is my Top-10 Data Center Predictions for 2017:
1) Data center optimization is here
Data centers are information factories with lots of components and moving parts. There was a time when companies started becoming much more complex, which fueled the massive enterprise resource planning market. Managing everything in the data center is in a similar place. To automate, monitor, troubleshoot, plan, optimize, cost-contain, report, etc, is a giant task, and it is good to see new apps in this area.
Data center infrastructure management provides visibility into and helps control IT. Once it is deployed, you’ll wonder how you did without it. Some day, it will be one cohesive thing, but for now, because it’s such a big task, there will be several companies addressing different parts of it.
2) Azure will grow faster than AWS
Cloud is the big wave, of course, and almost anything that touches it is on the right side of history. So, private and hybrid clouds will grow nicely and they will even temper the growth of public clouds. But the growth of public clouds will continue to impress, despite increasing recognition that they are not the cheapest option, especially for the mid-size users.
AWS will lead again, capturing most new apps. However, Azure will grow faster, on the strength of both landing new apps and also bringing along existing apps where Microsoft maintains a significant footprint.
Moving Exchange, Office, and other apps to the cloud, in addition to operating lots of regional data centers and having lots of local feet on the ground will help.
Some of the same dynamics will help Oracle show a strong hand and get close to Google and IBM. And large telcos will stay very much in the game. Smaller players will persevere and even grow, but they will also start to realize that public clouds are their supplier or partner, not their competition! It will be cheaper for them to OEM services from bigger players, or offer joint services, than to build and maintain their own public cloud.
3) Great Wall of persistent memory will become a thing
Just as the hottest thing in enterprise becomes Big Data, we find that the most expensive part of computing is moving all that data around. Naturally, we start seeing what OrionX calls “In-Situ Processing” (see page 4 of the report in the link): instead of data going to compute, compute would go to data, processing it locally wherever data happens to be.
But as the gap between CPU speed and storage speed separates apps and data, memory becomes the bottleneck. In comes storage class memory (mostly flash, with a nod to other promising technologies), getting larger, faster and cheaper. So, we will see examples of apps using a Great Wall of persistent memory, built by hardware and software solutions that bridge the size/speed/cost gap between traditional storage and DRAM. Eventually, we expect programming languages to naturally support byte-addressable persistent memory.
4) System vendors will announce racks, not servers
Vendors already configure and sell racks, but they often populate them with servers that are designed as if they’d be used stand-alone. Vendors with rack-level thinking have been doing better because designing the rack vs the single node lets them add value to the rack while removing unneeded value from server nodes.
So server vendors will start thinking of a rack, not a single-node, as the system they design and sell. Intel’s Rack Scale Architecture has been on the right track, a real competitive advantage, and an indication of how traditional server vendors must adapt. The server rack will become the next level of integration and what a “typical system” will look like. Going forward, multi-rack systems are where server vendors have a shot at adding real value. HPC vendors have long been there.
5) Server revenue growth will be lower than GDP growth
Traditional enterprise apps – the bulk of what runs on servers – will show that they have access to enough compute capacity already. Most of that work is transactional, so their growth is correlated with the growth in GDP, minus efficiencies in processing.
New apps, on the other hand, are hungry, but they are much more distributed, more focused on mobile clients, and more amenable to what OrionX calls “High-Density Processing”: algorithms that have a high ops/bytes ratio running on hardware that provides similarly high ops/byte capability – ie, compute accelerators like GPUs, FPGAs, vector processors, manycore CPUs, ASICs, and new chips on the horizon.
On top of that, there will be more In-Situ Processing: processing the data wherever it happens to be, say locally on the client vs sending it around to the backend. This will be made easier by the significant rise in client-side computing power and more capable data center switches and storage nodes that can do a lot of local processing.
We will also continue to see cloud computing and virtualization eliminate idle servers and increase the utilization rates of existing systems.
Finally, commoditization of servers and racks, driven by fewer-but-larger buyers and standardization efforts like the Open Compute Project, put pressure on server costs and limit the areas in which server vendors can add value. The old adage in servers: “I know how to build it so it costs $1m, but don’t know how to build it so it’s worth $1m” will be true more than ever.
These will all combine to keep server revenues in check. We will see 5G’s wow-speeds but modest roll-out, and though it can drive a jump in video and some server-heavy apps, we’ll have to wait a bit longer.
6) OpenPOWER will emerge as the viable alternative to x86
The real battle in server architecture will be between Intel’s in-house coalition and what my colleague Dan Olds has called the Rebel Alliance: IBM’s OpenPower industry coalition. Intel brings its all-star team: Xeon Phi, Altera, Omni-Path (plus Nervana/Movidius) and Lustre, while OpenPower counters with a dream team if its own: POWER, Nvidia, Xilinx, and Mellanox (plus TrueNorth) and GPFS (Spectrum Scale). Then there is Watson which will become more visible as a differentiator, and a series of acquisitions by both camps as they fill various gaps.
The all-in-house model promises seamless integration and consistent design, while the extended team offers a best-of-breed approach. Both have merits. Both camps are pretty formidable. And there is real differentiation in strategy, design, and implementation. Competition is good.
7) Beware IoT Security
One day in the not too distant future, your fancy car will break down on the highway for no apparent reason. It will turn out that the auto entertainment system launched a denial of service attack on the rest of the car, in a bold attempt to gain control. It even convinced the nav system to throw in with it! This is a joke, of course, but could become all too real if/when human and AI hackers get involved.
IoT is coming, and with it will come all sorts of security issues. Do you know where your connected devices are? Can you really trust them?
8) More chips than Vegas; riskier too
For the first time in decades, there is a real market opening for new chips. What drove this includes:
The emergence of new apps, led by AI and IoT. The part of AI that is computationally interesting and different is High-Performance AI, since it intersects with HPC. On the IoT side, backend apps are typically Analytics to make sense of sensor data. These new apps will run where they can run better/faster/cheaper. They will be too new to be burdened by any allegiance or bonds to a particular chip.
The fact that many existing apps have no clue what hardware they run on, and operate on the upper layers of a tall stack.
The possibility to build a complete software stack from open source software components.
The presence of very large customers like cloud providers or top supercomputing sites. They buy in seriously large volumes and have the wherewithal to build the necessary software stack, so they can afford to pick a new chip and bolster, if not guarantee, its viability.
This will be a year when many new chips became available and tested, and there is a pretty long list of them, showing just how big the opportunity is, how eager investors must have been to not miss out, and how many different angles there are.
In addition to AI, there are a few important general-purpose chips being built. The coolest one is by startup Rex Computing, which is working on a chip for exascale, focused on very high compute-power/electric-power ratios. Qualcomm and Caviuum have manycore ARM processors, Oracle is advancing SPARC quite well, IBM’s POWER continues to look very interesting, and Intel and AMD push the X86 envelope nicely.
With AI chips, Intel already has acquired Nervana and Movidius. Google has its TPU, and IBM its neuromorphic chip, TrueNorth. Other AI chip efforts include Mobileye (the company is being bought by Intel since this article was written), Graphcore, BrainChip, TeraDeep, KnuEdge, Wave Computing, and Horizon Robotics. In addition, there are several well-publicized and respected projects like NeuRAM3, P-Neuro, SpiNNaker, Eyeriss, and krtkl going after different parts of the market. That’s a lot of chips, but most of these bets, of course, won’t pay off.
9) ARM server market share will stay below 3 per cent
Speaking of chips, ARM servers will remain important but elusive. They will continue to make a lot of noise and point to significant wins and systems, but fail to move the needle when it comes to revenue market share in 2017.
As a long-term play, ARM is an important phenomenon in the server market – more so now with the backing of SoftBank, a much larger company apparently intent on investing and building, and various supercomputing and cloud projects that are great proving grounds.
But at the end, you need differentiation and ARM has the same problem against X86 as Intel’s Atom had against ARM. Atom did not differentiate vs ARM any more than ARM is differentiating vs Xeon.
Most systems end up being really good at something, however, and there are new apps and an open-source stack to support existing apps, which will help find specific workloads where ARM implementations could shine. That will help the differentiation become more than “it’s not X86”.
10) Is it an app, or is it a fabric? More cloud fabrics introduced
What is going on with big new apps? They keep getting more modular (microservices), more elastic (scale out), and more real-time (streaming). They’ve become their own large graph, in the computer science sense, and even more so with IoT (sensors everywhere plus In-Situ Processing).
As apps became services, they began resembling a network of interacting modules, depending on each other and evolving together. When the number of interacting pieces keeps increasing, you’ve got yourself a fabric. But as an app, it’s the kind of fabric that evolves and has an overall purpose (semantic web).
Among engineering disciplines, software engineering already doesn’t have a great reputation for predictability and maintainability. More modularity is not going to help with that.
But efforts to manage interdependence and application evolution have already created standards for structured modularity like the OSGi Alliance for Java. Smart organizations have had ways to reduce future problems (technical debt) from the get-go. So, it will be nice to see that type of methodology get better recognized and better generalized.
OK, we stop here now, leaving several interesting topics for later.
Shahin is a technology analyst and an active CxO, board member, and advisor. He serves on the board of directors of Wizmo (SaaS) and Massively Parallel Technologies (code modernization) and is an advisor to CollabWorks (future of work). He is co-host of the @HPCpodcast, Mktg_Podcast, and OrionX Download podcast.
Today, we are announcing the OrionX Constellation™ research framework as part of our strategy services.
Paraphrasing what I wrote in my last blog when we launched 8 packaged solutions:
One way to see OrionX is as a partner that can help you with content, context, and dissemination. OrionX Strategy can create the right content because it conducts deep analysis and provides a solid perspective in growth segments like Cloud, HPC, IoT, Artifical Intelligence, etc. OrionX Marketing can put that content in the right context by aligning it with your business objectives and customer needs. And OrionX PR can build on that with new content and packaging to pursue the right dissemination, whether by doing the PR work itself or by working with your existing PR resources.
Even if you only use one of the services, the results are more effective because strategy is aware of market positioning and communication requirements; marketing benefits from deep industry knowledge and technical depth; and PR can set a reliable trajectory armed with a coherent content strategy.
Constellation™: Research from OrionX
The Constellation research is all about high quality content. That requires the ability to go deep on technologies, market trends, and customer sentiment. Over the past two years, we have built such a capability.
Given the stellar connotations of the name Orion (not to mention our partner Stephen Perrenod’s book and blog about quantum and astro- physics), it is natural that we would see the players in each market segment as stars in a constellation. Constellations interact and evolve, and stars are of varying size and brightness, their respective positions dependent on cosmic (market) forces and your (customers’) point of view. Capturing that reality in a relatively simple framework has been an exciting achievement that we are proud to unveil.
OrionX Constellation Research Model
Types of Data
The OrionX strategy process collects and organizes data in three categories: market, customer, and product. The Constellation process further divides that into two parameters in each category:
Market presence and trends: a vendor’s presence in the segment and its ability to shape or embrace market trends
Customer needs and readiness: a typical customer’s current needs and readiness to adopt a particular product
Product capabilities and roadmap: a product’s existing capabilities and competitive standing as well as its expected enhancements and replacements in the future
This data is then analyzed to identify salient points which are in turn synthesized into the coherent insights that are necessary for a clear understanding of the dynamics at play and effective market conversations and change management.
Types of Report
The resulting content leads to five types of reports that capture the OrionX perspective on a segment. We call these the five Es:
Event: industry milestones and periodic or seasonal topics)
Evolution: historical perspective on a technology or market as a way of defining the segment and understanding the parameters that impact customer sentiment
Environment: the players (stars) in the segment that is in focus
Evaluation: how customer needs have evolved and how they evaluate (or should evaluate) the products in a segment
Excellence: how OrionX analysts see the segment and score each vendor according to six parameters in the three categories of data, two parameters in each category.
OrionX Constellation Decision Tool
Visualizing six distinct parameters, each with its own axis, in three categories cannot be done with the traditional 2×2 or 3×3 diagrams. Indeed, existing models in the industry do quite a good job of providing simple diagrams that rank vendors in a segment.
To simplify this visualization, we have developed the OrionX Constellation Decision Tool. This diagram effectively visualizes the six parameters that capture the core of technology customers’ selection process. (A future blog will drill down into the diagram.)
Sample Reports
While OrionX is not a typical industry analyst firm, we will offer samples of our reports from time to time. We hope they are useful to you and provide a glimpse of our work. They will complement our blog.
Shahin is a technology analyst and an active CxO, board member, and advisor. He serves on the board of directors of Wizmo (SaaS) and Massively Parallel Technologies (code modernization) and is an advisor to CollabWorks (future of work). He is co-host of the @HPCpodcast, Mktg_Podcast, and OrionX Download podcast.
An important part of the OrionX magic is the ability to understand technologies, the competitive landscape, and customer needs. We put a lot of effort into that. That is what clients need to connect products to customers, and to meet real customer needs with genuine product capabilities.
Today we raise the bar in that effort as we announce Dan’s addition to the OrionX team and celebrate a new milestone. In addition to tracking key technologies and emerging market segments, Dan has built a well-established process to measure customer sentiment in enterprise IT, something every technology vendor needs.
Thanks to the support of all of you: our clients, colleagues, and community, OrionX is poised to grow its reach and expand its services in Strategy, Marketing, and PR. We’ve been lucky to know you and to work on leading edge technologies in important areas such as IoT, Cloud, Big Data, HPC, & Security, etc.
Here is the press release we issued today:
ORIONX APPOINTS BIG DATA & HPC EXPERT DAN OLDS AS PARTNER
March 2nd, 2016, Menlo Park, CA – Pioneering a new consulting services model for strategy, marketing, and PR, OrionX today announced the appointment of Dan Olds as partner. Mr. Olds is principal analyst at Gabriel Consulting Group (GCG) a Portland, Oregon-based IT consulting firm, which he founded in 2002 and whose activities continue as part of OrionX.
“Our clients value OrionX’s ability to understand their technology, their competition, and their customers, as they pave the way for digital transformation,” said Shahin Khan, Founding Partner at OrionX. “We are raising the bar with Dan’s extensive knowledge of technology trends and insightful appreciation of customer adoption patterns.”
An authority on technology trends and customer requirements, Dan Olds is a frequently quoted expert in industry and business publications such as The Wall Street Journal, Bloomberg News, Computerworld, eWeek, CIO.com, and PCWorld.
“I am thrilled to join OrionX and a group of professionals with whom I have collaborated closely in the past,” said Dan Olds, Partner at OrionX. “My work on technology trends and customer sentiment is a perfect match with OrionX as we expand our services to deliver sound advice and quality content to our clients.”
In addition to server, storage, and network technologies, Dan closely follows the Big Data, Cloud, and HPC markets. He writes the “HPC Blog” on The Register, co-hosts the popular Radio Free HPC podcast, and is the acknowledged go-to person for the coverage of the supercomputing industry’s highly lauded Student Cluster Challenge.
Big Data and HPC Expert Dan Olds Joins OrionX
About OrionX
Mobile, Social, Cloud, Big Data and IoT are redefining B2B technology. OrionX.net is the pioneer of a new model for Strategy, Marketing, and PR. OrionX is known for its depth and agility, insightful advice, original content, and its stellar reputation in Silicon Valley. More than 40 technology leaders in virtually every B2B segment have trusted OrionX.net to help set new break-away strategies, ignite brands, and engage customers for increased market share. Visit us at OrionX.net
For more information: Cindee Mock, OrionX, cindee.mock@orionx.net 650-255-2975
Shahin is a technology analyst and an active CxO, board member, and advisor. He serves on the board of directors of Wizmo (SaaS) and Massively Parallel Technologies (code modernization) and is an advisor to CollabWorks (future of work). He is co-host of the @HPCpodcast, Mktg_Podcast, and OrionX Download podcast.
In our 2016 predictions blog, we said “If you missed the boat on cloud, you can’t miss it on IoT too”, and “IoT is where Big Data Analytics, Cognitive Computing, and Machine Learning come together for entirely new ways of managing business processes.”
IoT represents the ultimate convergence theme in the marketplace today. IoT includes fixed and mobile edge devices (fog computing), cloud computing, big data, analytics and machine learning, and in many instances can include social and mobile computing as well.
Composition VIII, Wassily Kandinsky, 1923, Shared via #WikiArtApp http://bit.ly/18GCSaj
Because of the very large number of devices being incorporated in IoT solutions and because of the high data rates that must be supported at the very edge of a network, IoT also requires embedded computing with low-power processors for preliminary data ingestion and filtering. Data processing at the edge accomplishes two things: firstly, it allows elimination of data that does not need to be transmitted to the central cloud-based data lake. Secondly, it supports preliminary real-time processing of acquired data that can be used for device monitoring and control with immediate feedback and very low latency. Additionally, computing at the edge should be a prerequisite for enabling security of devices and the data at the time of acquisition.
IOT and cloud computing fit together like a hand in a glove. This is only natural with the highly distributed and very dynamic nature of IoT devices and IoT data. Cloud infrastructure provides the most versatile, flexible and adaptable platform for an IoT repository and processing system. Cloud vendors including Amazon (AWS), Microsoft (Azure), IBM (SoftLayer) and Alphabet (Google Compute Engine) are racing to implement IoT capabilities, APIs, and advanced analytics and machine learning solutions in their public cloud environments. For reasons of control and to meet privacy and regulatory requirements, many companies will choose to implement private cloud services as well. Hybrid cloud services will play an important role in IoT.
When it comes to infrastructure within IoT clouds and at the edge, one can imagine every type of processor, network and protocol, servers and storage playing a role – both in distributed and centralized fashions – for IoT environments. Shared APIs and protocols are critical to promote interoperability. End-to-end security is as well an imperative; security is needed within every device and at each and every layer and sub-layer of the solution.
If anything deserves the term Big Data it is IoT. Billions and billions of devices are becoming Internet-enabled and the number of devices engaged in IoT applications will soon exceed the number of people on Earth. Because there are now many Big Data solutions available in public clouds this reinforces the cloud as a natural repository for IoT-generated Big Data lakes.
Analytics and machine learning will be heavily used to extract maximum value from the large amount of data acquired. A very wide range of business intelligence, analytics and machine learning techniques will be required. We foresee that IoT will be a big driver for machine learning advances. Analytics and machine learning will support everything from better operations of the connected devices to better information on how devices are utilized and new value-added services enabled by the device manufacturers.
There is a wide array of potential IoT applications covering every industry: Transportation, Retail, Manufacturing, Energy, Finance, Smart Cities, Healthcare, Agriculture, Government and other areas. Consumer apps are found in areas such as wearables, fitness, smart homes, electronics, and gaming.
Benefits from IoT applications will include improved operational efficiency and reliability, insight into usage patterns, and opportunities for increased revenue and better product design for manufacturers. Just as today we cannot imagine life without the Internet, we will not be able to imagine life without IoT by the beginning of the next decade.
It’s difficult to think of an area that requires a more holistic and converged view than IoT and that is not already a natural subset or example of IoT. Robotics? Drones? 3D printing? …. Every device of value should benefit from being plugged into the Internet, if only for maintenance and monitoring purposes, and these IoT-enabled devices will become available to participate in other IoT apps. At the other end of the data flow, big value comes from analytics services that are applied to a pool of devices and the streaming data they provide.
IT and IoT will become inseparable. IoT requires, incorporates, and benefits from all of the other major themes in IT today and thus we see it as representing the Ultimate Convergence at present.
Stephen Perrenod has lived and worked in Asia, the US, and Europe and possesses business experience across all major geographies in the Asia-Pacific region. He specializes in corporate strategy for market expansion, and cryptocurrency/blockchain on a deep foundation of high performance computing (HPC), cloud computing and big data. He is a prolific blogger and author of a book on cosmology.
Here at OrionX.net, we are fortunate to work with tech leaders across several industries and geographies, serving markets in Mobile, Social, Cloud, and Big Data (including Analytics, Cognitive Computing, IoT, Machine Learning, Semantic Web, etc.), and focused on pretty much every part of the “stack”, from chips to apps and everything in between. Doing this for several years has given us a privileged perspective. We spent some time to discuss what we are seeing and to capture some of the trends in this blog: our 2016 technology issues and predictions. We cut it at 17 but we hope it’s a quick read that you find worthwhile. Let us know if you’d like to discuss any of the items or the companies that are driving them.
1- Energy technology, risk management, and climate change refashion the world
Energy is arguably the most important industry on the planet. Advances in energy efficiency and sustainable energy sources, combined with the debate and observations of climate change, and new ways of managing capacity risk are coming together to have a profound impact on the social and political structure of the world, as indicated by the Paris Agreement and the recent collapse in energy prices. These trends will deepen into 2016.
2- Cryptocurrencies drive modernization of money (the original virtualization technology)
Money was the original virtualization technology! It decoupled value from goods, simplified commerce, and enabled the service economy. Free from the limitations of physical money, cryptocurrencies can take a fresh approach to simplifying how value (and ultimately trust, in a financial sense) is represented, modified, transferred, and guaranteed in a self-regulated manner. While none of the existing implementations accomplish that, they are getting better understood and the ecosystem built around them will point the way toward a true digital currency.
3- Autonomous tech remains a fantasy, technical complexity is in fleet networks, and all are subordinate to the legal framework
Whether flying, driving, walking, sailing, or swimming, drones and robots of all kinds are increasingly common. Full autonomy will remain a fantasy except for very well defined and constrained use cases. Commercial success favors technologies that aim to augment a human operator. The technology complexity is not in getting one of them to do an acceptable job, but in managing fleets of them as a single network. But everything will be subordinate to an evolving and complex legal framework.
4- Quantum computing moves beyond “is it QC?” to “What can it do?”
A whole new approach to computing (as in, not binary any more), quantum computing is as promising as it is unproven. Quantum computing goes beyond Moore’s law since every quantum bit (qubit) doubles the computational power, similar to the famous wheat and chessboard problem. So the payoff is huge, even though it is, for now, expensive, unproven, and difficult to use. But new players will become more visible, early use cases and gaps will become better defined, new use cases will be identified, and a short stack will emerge to ease programming. This is reminiscent of the early days of computing so a visit to the Computer History Museum would be a good recalibrating experience.
5- The “gig economy” continues to grow as work and labor are better matched
The changing nature of work and traditional jobs received substantial coverage in 2015. The prospect of artificial intelligence that could actually work is causing fears of wholesale elimination of jobs and management layers. On the other hand, employers routinely have difficulty finding talent, and employees routinely have difficulty staying engaged. There is a structural problem here. The “sharing economy” is one approach, albeit legally challenged in the short term. But the freelance and outsourcing approach is alive and well and thriving. In this model, everything is either an activity-sliced project, or a time-sliced process, to be performed by the most suitable internal or external resources. Already, in Silicon Valley, it is common to see people carrying 2-3 business cards as they match their skills and passions to their work and livelihood in a more flexible way than the elusive “permanent” full-time job.
6- Design thinking becomes the new driver of customer-centric business transformation
With the tectonic shifts in technology, demographic, and globalization, companies must transform or else. Design thinking is a good way to bring customer-centricity further into a company and ignite employees’ creativity, going beyond traditional “data driven needs analysis.” What is different this time is the intimate integration of arts and sciences. What remains the same is the sheer difficulty of translating complex user needs to products that are simple but not simplistic, and beautiful yet functional.
7- IoT: if you missed the boat on cloud, you can’t miss it on IoT too
Old guard IT vendors will have the upper hand over new Cloud leaders as they all rush to claim IoT leadership. IoT is where Big Data Analytics, Cognitive Computing, and Machine Learning come together for entirely new ways of managing business processes. In its current (emerging) condition, IoT requires a lot more vertical specialization, professional services, and “solution-selling” than cloud computing did when it was in its relative infancy. This gives traditional big (and even small) IT vendors a chance to drive and define the terms of competition, possibly controlling the choice of cloud/software-stack.
8- Security: Cloud-native, Micro-zones, and brand new strategies
Cybercrime is big business and any organization with digital assets is vulnerable to attack. As Cloud and Mobile weaken IT’s control and IoT adds many more points of vulnerability, new strategies are needed. Cloud-native security technologies will include those that redirect traffic through SaaS-based filters, Micro-Zones to permeate security throughout an app, and brand new approaches to data security.
9- Cloud computing drives further consolidation in hardware
In any value chain, a vendor must decide what value it offers and to whom. With cloud computing, the IT value chain has been disrupted. What used to be a system is now a piece of some cloud somewhere. As the real growth moves to “as-a-service” procurements, there will be fewer but bigger buyers of raw technology who drive hardware design towards scale and commoditization.
10- Composable infrastructure matures, leading to “Data Center as a System”
The computing industry was down the path of hardware partitioning when virtualization took over, and dynamic reconfiguration of hardware resources took a backseat to manipulating software containers. Infrastructure-as-code, composable infrastructure, converged infrastructure, and rack-optimized designs expand that concept. But container reconfiguration is insufficient at scale, and what is needed is hardware reconfiguration across the data center. That is the next frontier and the technologies to enable it are coming.
11- Mobile devices move towards OS-as-a-Service
Mobile devices are now sufficiently capable that new features may or may not be needed by all users and new OS revs often slow down the device. Even with free upgrades and pre-downloaded OS revs, it is hard to make customers upgrade, while power users jailbreak and get the new features on an old OS. Over time, new capabilities will be provided via more modular dynamically loaded OS services, essentially a new class of apps that are deeply integrated into the OS, to be downloaded on demand.
12- Social Media drives the Analytics Frontier
Nowhere are the demands for scale, flexibility and effectiveness for analytics greater than in social media. This is far beyond Web Analytics. The seven largest “populations” in the world are Google, China, India, Facebook, WhatsApp, WeChat and Baidu, in approximately that order, not to mention Amazon, Apple, Samsung, and several others, plus many important commercial and government applications that rely on social media datasets. Searching through such large datasets with very large numbers of images, social commentary, and complex network relationships stresses the analytical algorithms far beyond anything ever seen before. The pace of algorithmic development for data analytics and for machine intelligence will accelerate, increasingly shaped by social media requirements.
13- Technical Debt continues to accumulate, raising the cost of eventual modernization
Legacy modernization will get more attention as micro-services, data-flow, and scale-out elasticity become established. But long-term, software engineering is in dire need of the predictability and maintainability that is associated with other engineering disciplines. That need is not going away and may very well require a wholesale new methodology for programming. In the meantime, technologies that help automate software modernization, or enable modular maintainability, will gain traction.
14- Tools emerge to relieve the DB-DevOps squeeze
The technical and operational burden on developers has been growing. It is not sustainable. NoSQL databases removed the time-delay and complexity of a data schema at the expense of more complex codes, pulling developers closer to data management and persistence issues. DevOps, on the other hand, has pulled developers closer to the actual deployment and operation of apps with the associated networking, resource allocation, and quality-of-service (QoS) issues. This is another “rubber band” that cannot stretch much more. As cloud adoption continues, development, deployment, and operations will become more synchronized enabling more automation.
The idea of a “memory-only architecture” dates back several decades. New super-large memory systems are finally making it possible to hold entire data sets in memory. Combine this with Flash (and other emerging storage-class memory technologies) and you have the recipe for entirely new ways of achieving near-real-time/straight-through processing.
16- Multi-cloud will be used as a single cloud
Small and mid-size public cloud providers will form coalitions around a large market leader to offer enterprise customers the flexibility of cloud without the lock-in and the risk of having a single supplier for a given app. This opens the door for transparently running a single app across multiple public clouds at the same time.
17- Binary compatibility cracks
It’s been years since most app developers needed to know what CPU their app runs on, since they work on the higher levels of a tall software stack. Porting code sill requires time and effort but for elastic/stateless cloud apps, the work is to make sure the software stack is there and works as expected. But the emergence of large cloud providers is changing the dynamics. They have the wherewithal to port any system software to any CPU thus assuring a rich software infrastructure. And they need to differentiate and cut costs. We are already seeing GPUs in cloud offerings and FPGAs embedded in CPUs. We will also see the first examples of special cloud regions based on one or more of ARM, OpenPower, MIPS, and SPARC. Large providers can now offer a usable cloud infrastructure using any hardware that is differentiated and economically viable, free from the requirement of binary compatibility.
OrionX is a team of industry analysts, marketing executives, and demand generation experts. With a stellar reputation in Silicon Valley, OrionX is known for its trusted counsel, command of market forces, technical depth, and original content.
Last week, in Part 1 of this two-part blog, we looked at trends in Big Data and analytics, and started to touch on the relationship with HPC (High Performance Computing). In this week’s blog we take a look at the usage of Big Data in HPC and what commercial and HPC Big Data environments have in common, as well as their differences.
High Performance Computing has been the breeding ground for many important mainstream computing and IT developments, including:
The Web
Cluster computing
Cloud computing
Hi-quality visualization and animation
Parallel computing
and arguably, Big Data itself
Big Data has indeed been a reality in many HPC disciplines for decades, including:
Particle physics
Genomics
Astronomy
Weather and climate modeling
Petroleum seismic processing
Horseshoe Falls (portion of Niagara Falls on Canadian side)
All of these fields and others generate massive amounts of data, which must be cleaned, calibrated, reduced and analyzed in great depth in order to extract knowledge. This might be a new genetic sequence, the identification of a new particle such as the Higgs Boson, the location and characteristics of an oil reservoir, or a more accurate weather forecast. And naturally the data volumes and velocity are growing continually as scientific and engineering instrumentation becomes more advanced.
A recent article, published in the July 2015 issue of the Communications of the ACM, is titled “Exascale computing and Big Data”. Authors Daniel A. Reed and Jack Dongarra note that “scientific discovery and engineering innovation requires unifying traditionally separated high-performance computing and big data analytics”.
(n.b. Exascale is 1000 x Petascale, which in turn is 1000 x Terascale. HPC and Big Data are already well into the Petascale era. Europe, Japan, China and the U.S. have all announced Exascale HPC initiatives spanning the next several years.)
What’s in common between Big Data environments and HPC environments? Both are characterized by racks and racks of commodity x86 systems configured as compute clusters. Both environments have compute system management challenges in terms of power, cooling, reliability and administration, scaling to as many as hundreds of thousands of cores and many Petabytes of data. Both are characterized by large amounts of local node storage, increasing use of flash memory for fast data access and high-bandwidth switches between compute nodes. And both are characterized by use of Linux OS operating systems or flavors of Unix. Open source software is generally favored up through the middleware level.
What’s different? Big Data and analytics uses VMs above the OS, SANs as well as local storage, the Hadoop (parallel) file system, key-value store methods, and a different middleware environment including Map-Reduce, Hive and the like. Higher-level languages (R, Python, Pig Latin) are preferred for development purposes.
HPC uses C, C++, and Fortran traditional compiler development environments, numerical and parallel libraries, batch schedulers and the Lustre parallel file system. And in some cases HPC systems employ accelerator chips such as Nvidia GPUs or Intel Xeon Phi processors, to enhance floating point performance. (Prediction: we’ll start seeing more and more of these used in Big Data analytics as well – http://www.nvidia.com/object/data-science-analytics-database.html).
But in both cases the pipeline is essentially:
Data acquisition -> Data processing -> Model / Simulation -> Analytics -> Results
The analytics must be based on and informed by a model that is attempting to capture the essence of the phenomena being measured and analyzed. There is always a model — it may be simple or complex; it may be implicit or explicit.
Human behavior is highly complex, and every user, every customer, every patient, is unique. As applications become more complex in search of higher accuracy and greater insight, and as compute clusters and data management capabilities become more powerful, the models or assumptions behind the analytics will in turn become more complex and more capable. This will result in more predictive and prescriptive power.
Our general conclusion is that while there are some distinct differences between Big Data and HPC, there are significant commonalities. Big Data is more the province of social sciences and HPC more the province of physical sciences and engineering, but they overlap, and especially so when it comes to the life sciences. Is bioinformatics HPC or Big Data? Yes, both. How about the analysis of clinical trials for new pharmaceuticals? Arguably, both again.
So cross-fertilization and areas of convergence will continue, while each of Big Data and HPC continue to develop new methods appropriate to their specific disciplines. And many of these new methods will crossover to the other area when appropriate.
The National Science Foundation believes in the convergence of Big Data and HPC and is putting $2.4 million of their money into this at the University of Michigan, in support of various applications including climate science, cardiovascular disease and dark matter and dark energy. See:
Stephen Perrenod has lived and worked in Asia, the US, and Europe and possesses business experience across all major geographies in the Asia-Pacific region. He specializes in corporate strategy for market expansion, and cryptocurrency/blockchain on a deep foundation of high performance computing (HPC), cloud computing and big data. He is a prolific blogger and author of a book on cosmology.
Data volumes, velocity, and variety are increasing as consumer devices become more powerful. PCs, smart phones and tablets are the instrumentation, along with the business applications that continually capture user input, usage patterns and transactions. As devices become more powerful each year (each few months!) the generated volumes of data and the speed of data flow both increase concomitantly. And the variety of available applications and usage models for consumer devices is rapidly increasing as well.
Are the Big Data and HPC disciplines converging or diverging?
Holding more and more data in-memory, via in-memory databases and in-memory computing, is becoming increasingly important in Big Data and data management more broadly. HPC has always required very large memories due to both large data volumes and the complexity of the simulation models.
Igauzu Falls: By Mario Roberto Duran Ortiz Mariordo (Own work) CC BY 3.0, via Wikimedia Commons
Volume and Velocity and Variety
As is often pointed out in the Big Data field, it is the analytics that matters. Collecting, classifying and sorting data is a necessary prerequisite. But until a proper analysis is done, one has only expended time, energy and money. Analytics is where the value extraction happens, and that must justify the collection effort.
Applications for Big Data include customer retention, fraud detection, cross-selling, direct marketing, portfolio management, risk management, underwriting, decision support, and algorithmic trading. Industries deploying Big Data applications include telecommunications, retail, finance, insurance, health care, and the pharmaceutical industry.
There are a wide variety of statistical methods and techniques employed in the analytical phase. These can include higher-level AI or machine learning techniques e.g. neural networks, support vector machines, radial basis functions, and nearest neighbor methods. These imply a significant requirement for a large number of floating point operations, which is characteristic of most of HPC.
For one view on this, here is a recent report on InsideHPC.com and video on “Why HPC is so important to AI”
If one has the right back-end applications and systems then it is possible to keep up with the growth in data and perform the deep analytics necessary to extract new insights about customers, their wants and desires, and their behavior and buying patterns. These back-end systems increasingly need to be of the scale of HPC systems in order to stay on top of all of the ever more rapidly incoming data, and to meet the requirement to extract maximum value.
In Part 2 of this blog series, we’ll look at how Big Data and HPC environments differ, and at what they have in common.
Stephen Perrenod has lived and worked in Asia, the US, and Europe and possesses business experience across all major geographies in the Asia-Pacific region. He specializes in corporate strategy for market expansion, and cryptocurrency/blockchain on a deep foundation of high performance computing (HPC), cloud computing and big data. He is a prolific blogger and author of a book on cosmology.
The industry is awash with articles on big data. Big data news is not confined to the technical webpages anymore. You can read about big data on Forbes and The Economist for example.
Each week the technical media reports on break through, startups, funding and customer use cases.
No matter your source for information on big data one thing they all have in common is that the amount of information an organization will deal with only ever increase, this is driving the ‘big data’ concept. Organizations are relying on increasing amounts of information from a variety of sources, text, images, audio, video, etc., to analyze, improve and execute their operations.
Big data is happening with Industries such as financial services, healthcare, retail, communications, to name a few. These industries have been collecting data for years and with the advent of the Internet of Things data is growing exponentially. The challenge is how to make sense of this data and turn it into business value – analytics and predictive analytics.
An example of big data at work is Fraud Detection and Security requirements of the Banking and Financial Services industry. These organizations need to prevent fraud and leverage analytics, machine learning, and big data technology to gain a holistic view of their customers to identify patterns buried in data and distinguish fraudulent activity from normal activity.
Many new solutions are being developed to churn through and make sense of this data. Perhaps an old solution is getting its second or third wind – big memory, really big memory and the ability to troll through with high bandwidth and latency speeds.
Why will this be important?
First, memory is getting cheaper and cheaper. Second, by storing the database in-memory one avoids the traditional IO bottleneck, despite the growth in SSD storage, and third there are some really big memory systems available, up to 64TB of shared memory in some cases and getting bigger. These tera-scale memory systems have the ability to sift through masses of data enabling OLTP and OLAP in a single system creating a Hybrid/Transaction Analytics Processing (HTAP) solutions that do not require data duplication.
In-memory computing is not new. For several decades the supercomputing industry or High Performance Computing squeezed every bit of performance they could with in-memory computing solutions. Now we are seeing in-memory HPC techniques being applied to everyday big data. HPC to the rescue of the enterprise, one more time.
Steve brings a unique blend of expertise to the technology industry. He has held senior VP positions in sales & marketing for HPC and Enterprise vendors including Sun Microsystems and Hitachi. He’s also worked with early stage technology and Internet start-ups helping to raise over $300M for clients.
Horseshoe Falls (portion of Niagara Falls on Canadian side)
Igauzu Falls: By Mario Roberto Duran Ortiz Mariordo (Own work) CC BY 3.0, via Wikimedia Commons
