This paper is designed to help key stakeholders mitigate the risk of Meltdown and Spectre, which will be especially difficult in hybrid or mixed systems.
There are billions of PCs and mobile devices affected by Meltdown and Spectre. That’s a big problem for OS vendors. For enterprise IT, there is also the need to deal with hundreds of millions of host servers and the virtual machines running on them. Meltdown and Spectre highlight just how difficult it is to update and patch hybrid systems with hosts, virtual machines, containers, and cloud servers in the mix. Don’t despair! There are solutions.
API management is a necessary but boring practice. As developers make use of a mix of public cloud, purchased or open source libraries, and homegrown services, the number of APIs used by developers quickly renders pouring through documentation impractical.
Microservices, usually accessed via RESTFul APIs, cause API calls to rapidly proliferate. Even modest-sized microservices-based systems experience API overload quickly. Agile development can exacerbate the problem of understanding and using APIs. The rapid pace of Agile, especially Scrum, leaves little time for proper documentation of APIs. Documentation often takes a back seat to continuous deployment. Continue reading “As API Management Problem Grows, Informatica Jumps into the Market”
As you know, I joined Amalgam Insights in September. Amalgam Insights, or AI, is a full-service market analyst firm. I’d welcome the opportunity to learn more about what 2018 holds for you. Perhaps we can schedule a quick call in the next couple of weeks. Let me know what works best for you. As always, if I can provide any additional information about AI, I’d be happy to do so!
As the fall season of tech conferences starts to wind down, something is quite clear – machine learning (ML) is going to be everywhere. Box is putting ML in content management, Microsoft in office and CRM, and Oracle is infusing it into, well, everything. While not a great leap forward on the order of the Internet, smartphones, or PCs, the inclusion of ML technology into so many applications will make a lot of mundane tasks easier. This trend promises to be a boon for developers. The strength of machining learning is finding and exploiting patterns and anomalies. What could be more useful to developers?
On the week of September 25th, 2017, Microsoft made a huge announcement at its annual Ignite and Envision conference. Microsoft has become one of a small number of companies that is demonstrating quantum computing. IBM is another company that is also pursuing this rather futuristic computing model.
For those who are not up-to-date on quantum computing, it uses quantum properties such as superposition and entanglement to develop a new way of computing. Current computers are built around tiny electron switches called transistors that allow for two states, which represent the binary system we have today. Quantum computers leverage quantum states that give us ones, zeros, and combinations of one and zero. This means a single qubit, the quantum equivalent of a bit, can represent many more states than the bit can. This is, of course, a gross oversimplification but quantum computing promises to deliver more dense and exponentially faster computing.
There are a number of problems with practical quantum computing. The hardware is still in a nascent stage and must be cooled to a temperature that is quite a bit colder than deep space. This makes it much more likely that quantum computing will be purchased via a cloud model than on-premises. The other inhibitor is that there is no standard programming model for quantum computing. IBM has demonstrated a visual programming model that shows how quantum computing works but is clearly not going to be a serious way to write real programs. Microsoft, on the other hand, showed a more standard looking curly bracket programming language. This application layer makes quantum computing more accessible to existing programmers who are more used to the current model of computing.
When quantum computing becomes practical – I would predict that is at least 5 years away, perhaps longer – it won’t be for everyday computing tasks. The current model is already more than adequate for those tasks. It’s also unlikely that the capabilities of quantum computers, especially the information dense qubit, and costs will have much a place in transactional computing. Instead, quantum computing will be used for analyzing very large and complex data sets for simulation and AI. That’s fine because the AI and analytics market is still new and the future needs are not yet completely known. That future computing needs is what quantum computing is meant to address. Even today’s big data applications can stretch computing capabilities and force batch analytics instead of real-time for some use cases.
Microsoft’s entry into what has been an otherwise esoteric corner of the computing world signals that quantum computing is on the path to being real. It has a long way to go and many obstacles to overcome but it’s no longer just science fiction or academic. It will be years but it is on the way to becoming mainstream.
Note: This post was originally posted on Tom’s Take
This past week (September 25 – 27, 2017) Microsoft held its Ignite and Envision Conferences. The co-conferences encompass both technology (Ignite) and the business of technology (Envision). Microsoft’s announcements reflected that duality with esoteric technology subjects such as mixed reality and quantum computing on equal footing with digital transformation, a mainstay of modern business transformation projects. There were two announcements that, in my opinion, will have the most impact in the short-term because they were more foundational.
The first announcement was that machine learning was being integrated into every Microsoft productivity and business product. Most large software companies are adding machine learning to their platforms but no company has Microsoft’s reach into modern businesses. Like IBM, SAP and Oracle, Microsoft can embed machine learning in business applications such as CRM. Microsoft can also integrate machine learning into productivity applications as can Google. IBM can do both but IBM’s office applications aren’t close to having the market penetration of Microsoft Office 365. Microsoft has the opportunity to embed machine learning everywhere in a business, a capability that none of their competitors have. Continue reading “Microsoft Infuses Products with Machine Learning and the Social Graph”