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The concepts of trust and security are different, but often confounded. They are similarly nuanced as the difference between threats and vulnerabilities, but I’ll save that for another day. The difference between trust and security was highlighted recently because it was discovered that there had been more than 5 million downloads of a cryptocurrency miner virus from DockerHub. A few years back, the XCodeGhost malware infected 39 iOS apps, including WeChat and something I use personally, CamScanner. This impacted hundreds of millions of users. These two incidents highlight a serious security problem—to feel confident in your software supply chain, you have to do both of the following:

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According to a recent report from IDC, "worldwide revenues for big data and business analytics will grow from nearly $122 billion in 2015 to more than $187 billion in 2019, an increase of more than 50 percent over the five-year forecast period."

Anyone in enterprise IT already knows that big data is a big deal. If you can manage and analyze massive amounts of data—I’m talking petabytes—you’ll have access to all sorts of information that will help you run your business better. 

Right? Sadly, for most enterprises, no. 

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text BrandPost: OpenCL Is for GPUs Too
Thu, 12 Jul 2018 16:27:00 -0700

OpenCL started as a solution for GPUs, but it has recently received a lot of attention as a solution for FPGAs. So much so, in fact, that I’ve heard people wonder if OpenCL was for GPUs still. Yes, it is. More than ever! OpenCL is an important heterogeneous language, and the support for GPUs—as well as for FPGAs—continues to grow.

Why OpenCL?

OpenCL offers a clever, portable, and effective way to see which devices are present (e.g., a GPU), and to perform certain computations with less power and possibly higher performance than a CPU. It’s incredibly easy to use, and with GPUs included on virtually all systems, it’s definitely something to experience (see the link below for a "Hello World" example as well as download links).

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Over the past few years, extending agile development teams into a devops delivery train has become the de facto next-gen process discussed at tech conferences across the globe. To be fair, the definitions and recipes have been well documented, and there’s zero doubt that "agile ops" will become the de facto replacement for innovation centers as cloud-based apps become ubiquitous.

In fact, the FAAMG (previously FANG) companies relish in describing their ultra-efficient delivery mechanisms with an implication that devops is easy-peasy. But, beyond the textbooks and unicorns, what does it really take to make the transformation? In the following paragraphs, I discuss some real-world experiences in evolving from a highly successful agile engineering team to an agile + IT operations organization.

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Distributed computing infrastructure has experienced evolutionary changes over the past two decades. Large, clunky server and storage systems have evolved into streamlined, highly efficient systems. Administrators have shifted how they consume resources, too. Historically, resource utilization was inefficient at best. Today, software automates the process to effectively manage resources — even the relationship between applications and the underlying hardware has changed. Today, abstractions between hardware and applications provide the ability for IT organizations to shift resources, as needed, to ever-changing application requirements.

In addition to the way resources were used, the physical number of things to manage skyrocketed. The number of servers and storage subsystems, network devices and amount of data have all seen a significant increase. And in the coming years, we can expect to see this trend continue to grow exponentially. In sum, a lot has changed… and continues to do so at a dizzying pace.

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One key devops best practice is instrumenting a continuous integration/continuousdelivery (CI/CD) pipeline that automates the process of building software, packaging applications, deploying them to target environments, and instrumenting service calls to enable the application. This automation requires scripting individual procedures and orchestrating the steps from code checkin to running application. Once matured, devops teams use the automation to drive process change and strive to do smaller, more frequent deployments that deliver new functionality to users and improve quality.

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(Insider Story)
text The key to proactive multicloud cost management
Wed, 11 Jul 2018 03:00:00 -0700

Enterprises are moving to multicloud in droves. Why? The key drivers most often cited by cloud adopters are speed, agility, platform flexibility, and reduced costs—or at least more predictable costs. It’s ironic then that more than half of these companies say that runaway cloud costs are their biggest postmigration pain point. 

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text Docker tutorial: Get started with Docker Hub
Wed, 11 Jul 2018 03:00:00 -0700

The power of Docker images is that they’re lightweight and portable—they can be moved freely between systems. You can easily create a set of standard images, store them in a repository on your network, and share them throughout your organization. Or you could turn to Docker Inc., which has created various mechanisms for sharing Docker container images in public and private.

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(Insider Story)

Companies everywhere are on the hunt for new ways to compete in their rapidly evolving markets, and nowhere is this truer than in traditional industries like energy, manufacturing, and transportation. From new opportunities to lower costs by increasing efficiency to more fundamental changes like evolving beyond selling capital goods to provide services, effectively turning expertise into a revenue generator, these companies are all working mightily to leverage technology as a change maker.

In almost every instance, data is the key asset. Not just data from internal systems, but a flood of data from sensors and related equipment throughout the supply, production and logistical chain—even those monitoring in-field customer deployments. Often referred to under the general umbrella of the industrial internet of things (IIoT), this connection of equipment, suppliers, distribution networks, and more works to generate a constant stream of data.

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Processing engines are everywhere. CPUs, GPUs, and even purpose-built FPGAs all offer compute capability that for decades was limited to specific functions. Today, thanks to the Open Computing Language (OpenCL), that has all changed, and savvy developers and IT teams are now taking advantage of OpenCL’s C-like language to create heterogeneous execution environments for custom code.

Getting started with OpenCL is easy if you know where to start, and for teams who rely on Intel technology, the best place to begin is with the Intel® SDK for OpenCL™ Applications.

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I hear it every day now: "We’re moving beyond cloud computing to edge computing." Pretty hypey, and not at all logical.

Edge computing is a handy trick. It’s the ability to place processing and data retention at a system that’s closer to the target system it’s collecting data for as well as to provide autonomous processing.

The architectural advantages are plenty, including not having to transmit all the data to the back-end systems—typical in the cloud—for processing. This reduces latency and can provide better security and reliability as well.

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The Xamarin acquisition was one of Microsoft’s smartest deals. It quickly gave it access to tools that let developers use familiar tools and technologies to build cross-platform applications. Now built into every version of Visual Studio, and providing the basis for its MacOS Visual Studio release, Xamarin has become a key element of Microsoft’s development tools.

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(Insider Story)

If you spend any time in Silicon Valley these days, it’s easy to believe every business in America is becoming a data analytics powerhouse. We hear so many stories about companies reinventing themselves with data—opening new revenue streams, better targeting customers, slashing costs. It’s enough to make a statistician weep with joy.

The reality on the ground is much different. From the enterprises I speak with, I know that many large companies are only just beginning to use data to really transform their businesses. There are pockets of innovation in every company, but few are truly using data across their organization to uncover game-changing opportunities.

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Product managers need devops, and devops people need product managers. This might seem a stretch at first but considering devops ties together all parts of the customer value stream and product managers want to create the best possible product as quickly as possible and keep applications up and running, it suddenly makes sense.

The two groups of people are specialists, and actually are very closely connected to each other in a traditional devops kind of perspective. It starts to make a lot of sense that that a lot of value can be created if they are they are copacetic, especially as the underlying technology makes this possible through the power of innovation and virtualization and vice versa.

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If you have experience building ASP.Net applications, you are undoubtedly familiar with role-based authorization. In ASP.Net Core – Microsoft’s lean and modular framework that can be used to build modern-day web applications on Windows, Linux, or MacOS – we have an additional option.

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(Insider Story)

It’s the kind of meta notion that makes undergraduate philosophers say, "Whoa!" Software today is so complicated that we need to write software to help us understand and construct the software we need to write. Code begets code begets more code…

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(Insider Story)

What does it mean to truly understand fog computing? Looking at fog computing as a framework that supports a vast ecosystem of resources is probably the best place to start. Understanding fog requires an appreciation for how all elements, from the billions of connected devices at the very edge to the datacenters in the far cloud can be leveraged cohesively to create an infrastructure capable of powering the tech of the future.

There’s a misconception that fog, edge, and cloud computing are engaged in a constant battle, when in reality they can complement each other. In order to support today’s rapid increase in connected devices and demand for resources from emerging tech, we’ll see a continued shift toward decentralized architectures that incorporate elements of each. Fog computing is how we create that dynamic infrastructure, enabling, for example, IoT to access flexible and affordable nearline processing, and trained AI to harness low-latency compute right at the edge.

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As companies today are re-architecting to become fully digital, they are putting events and data at the center of the business. In this journey, it's critical to start off on the right foot. Here’s a guide on what makes a good first project and how to use change data capture (CDC) to set yourself, and the entire company, up for success.

The first step

Sometimes the first project just shows up: there is a business problem to be solved, and the architects who lead the project agree that Apache Kafka is part of the solution and the best way to solve the problem. One project leads to another and next thing you know, everything that happens in the company is streamed in real time to a central platform, where it is available for anything else to tap into, process and react to: a central exchange of events and data that connects the entire organization.

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In 1999, the world was a very different place. Less than 4% of the world population was using the internet—largely over dial-up modems. Google was just coming out of beta, sites like Ask Jeeves, Alta Vista, Lycos, and AOL dominated the search engine landscape, people still purchased VHS tapes, and everyone was worried that the world might come to an end on January 1, 2000. Web sites looked like this and state of the art internet could get you up to 1.25 mbps. Slow performance was as ubiquitous as the modem beep—but if someone wanted to load test, they could do so using a protocol-level load testing tool like the newly-released JMeter or LoadRunner.

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Cognitive dissonance occurs when beliefs or assumptions are contradicted by new information. People address that tension with one of several defensive maneuvers: Often, they reject or avoid the new information, or they resort to other defensive means of preserving order in their conceptions of reality.

What does this have to do with cloud computing? A lot, these days.

For many IT professionals a decade ago, cloud computing was not secure, overpriced, unreliable, and other otherwise evil. In the early days, they loudly agitated against the cloud notion. Today, those people largely keep their opinions to themselves, but more of them are out there than you know.

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text What’s new in Microsoft Visual Studio Code
Fri, 06 Jul 2018 03:00:00 -0700

Microsoft’s open source development tool is an important piece of the developer’s toolkit. Built using GitHub’s cross-platform Electron framework, Visual Studio Code is a full-featured development editor that supports a wide selection of languages and platforms, from the familiar C and C# to modern environments and languages like Go and Node.js, with parity between Windows, MacOS, and Linux releases.

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(Insider Story)

Digital transformation is ushering in a new era of hybrid IT– a combination of both public and private cloud – that allows businesses to innovate while meeting their own unique organizational needs. Yet, a hybrid IT environment can create complexity and operational friction that can slow a business down and hold them back.

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text MySQL tutorial: Get started with MySQL 8
Thu, 05 Jul 2018 03:00:00 -0700

MySQL remains one of the most common and consistent elements in the modern application programming stack. If you want a database for your app or service, and your needs are fairly generic, MySQL is one of the easy defaults. It’s widely used and well-understood, so you can draw on a wealth of community knowledge and experience when deploying MySQL for your particular application.

MySQL migration and installation tips

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(Insider Story)
text How Red Hat has come to dominate Kubernetes
Thu, 05 Jul 2018 03:00:00 -0700

In the old world, the operating system was the center of the computing universe. In today’s modern application age, container platforms fill the space once occupied by the OS. That fact helps to explain why so much cash and code is pouring into Kubernetes.

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(Insider Story)

Today’s low-code app development platforms can play an essential role in any business undergoing digital transformation. As needs ebb and flow and new market opportunities present themselves, agile organizations require a way to innovate faster and speed time to market. While traditional development cycles are complex and can take months, modern low-code development platforms simplify the complexity of enterprise software development and enable deployment in weeks or days.

Low-code development platforms are not new. Rapid app development tools, like Visual Basic, PowerBuilder, and, to a lesser extent, Microsoft Access, have been around for decades, giving developers simple, easy-to-use platforms for creating applications quickly and efficiently with minimal or no coding required.

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As his 90th birthday approaches, one wonders how Intel co-founder Gordon Moore considers that his famous ‘law’, first postulated in 1965, still pretty much holds true. As revised in the ‘70s, it forecast that the number of transistors in an integrated circuit (read CPU) would double every 18 months, while the cost halved over the same period. That’s why the processing power on a single Xeon CPU today is roughly equivalent to all the mainframes that existed in the world in the early 1960s, which would have occupied a space roughly equivalent to several floors of the Empire State building.

But, there’s a rub. Many experts – physicists and engineers – are now saying we’re about out of room for Moore’s law to continue as in years past. The problem is physics: It is increasingly difficult to continue to shrink IC dies to continue the growth in the number of transistors; the physical limitations of etching dies is now reaching atomic limits that just can’t be exceeded with our current knowledge of the universe. Sad.

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Where do I get my cloud news? It’s almost never CPA Journal. But, more and more, accounting is becoming a larger part of cloud computing—no matter what side of the cloud you’re sitting on. 

On the enterprise side, it’s a matter of taxes to be paid. While you can typically find 30 to 40 percent better operational cost utilization when using cloud computing, that savings may be diluted by the fact that you’re giving up depreciation on hardware in the datacenter.

So, while cloud computing can save you millions of dollars a year, it may actually cost you money, at least in the short term. That’s something that I’ve run into from time to time with clients over the years.

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One key platform feature of Office 365 that is not well understood is Microsoft Graph. Building on the information stored in Office 365 and in Microsoft’s systems management and identity tools, Microsoft Graph is a way of not only using that information in your code, but a way of using that information to infer additional details about the context of that information, details that could well be more useful than the underlying data.

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(Insider Story)

Intel recently renamed its Computer Vision SDK as the OpenVINO™ toolkit. Looking at all that’s been added, it’s not surprising Intel wanted a new name to embrace all the new functionality. Included in the toolkit are three new APIs: The Deep Learning Deployment toolkit, a common deep learning inference toolkit, and optimized functions for OpenCV and OpenVX, with support for the TensorFlow, MXNet, and Caffe frameworks.

The OpenVINO toolkit offers software developers a single toolkit for applications wanting human-like vision capabilities. It does this by supporting deep learning, computer vision, and hardware acceleration with heterogeneous support, all in a single toolkit.

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Human brains constantly change their function and structure, and the term for this is neuroplasticity. Modern enterprises faced with constant challenges exercise a similar form of "plasticity" to adapt to a continuum of new changes. It was not always so, of course.

Thirty years ago, companies relied on people to get business done. They were organized in hierarchies complemented by narrowly scoped, localized systems of record. Change management programs restructured org charts, companies reeducated people, and eventually people were hired and fired. There was some change enforced on the existing system of records, but lack of agility (or lack of plasticity) was never a mission-critical impediment to success in business.

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