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Intel Parallel Studio XE is an expansive suite of software development tools made specifically for building and analyzing software written in C, C++, Fortran, and Python. In my opinion, if you develop C, C++, Fortran, or Python code that runs on x86 or x86-64 processors, you should have at least part of Intel Parallel Studio XE on your development systems.

The promise of Intel tools is reliability and higher performance. If you want the best performance on x86 or x86-64 on Linux, Windows, or macOS, I’ve found Intel tools to be the best bet. After using the 2019 beta tools all summer, I’m happy to report that the tools are in a full product release as of this week. You can learn more in this article, as well as from Intel’s website, where you can download the software.

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By this point, you know what microservices are and how they work. Now it’s time to get down to brass tacks: namely, the very critical topic of how to approach the transition to microservices.

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JavaFX 11, the first standalone release of the Java-based rich client technology, is now available. Oracle is removing JavaFX from the Java Development Kit (JDK) 11, given an overall desire to pull out noncore modules from the JDK and retire them or stand them up as independent modules.

The open source JavaFX 11 provides a client application platform for desktop, mobile, and embedded systems. JavaFX is a runtime available as a platform-specific SDK, as jmod files, and as a set of Maven central artifacts. With the JDK no longer including JavaFX, developers must explicitly include JavaFX modules in applications.

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text LLVM 7 improves performance analysis, linking
Wed, 19 Sep 2018 14:08:00 -0700

The developers behind LLVM, the open-source framework for building cross-platform compilers, have unveiled LLVM 7. The new release arrives right on schedule as part of the project’s cadence of major releases every six months.

LLVM underpins several modern language compilers including Apple’s Swift, the Rust language, and the Clang C/C++ compiler. LLVM 7 introduces revisions to both its native features and to companion tools that make it easier to build, debug, and analyze LLVM-generated software.

Among the changes to the LLVM core:

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text Python virtualenv and venv do’s and don’ts
Wed, 19 Sep 2018 03:00:00 -0700

One of the biggest draws of Python is its expansive ecosystem of third-party packages. If there is a task you want to pull off—file format conversion, scraping and restructuring web pages, linear regression, you name it—odds are that one or more packages in the Python Package Index will fill your need.

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text How to secure your PostgreSQL database
Wed, 19 Sep 2018 03:00:00 -0700

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Version 2.4.0 of the ASP.Net SignalR real-time communications library will support Azure SignalR Service, a managed service for adding real-time web functionality to applications.

ASP.Net SignalR 2.4.0 is due later this year; Azure SignalR Service currently is in beta. It is an Azure cloud-managed service that can be used to develop capabilities such as chat rooms, instant broadcasting, and IoT dashboards. Developers using the service do not have to deal with hosting, authentication, scaling, or load-balancing.

Working with the Visual Studio and Visual Studio Code development tools, Azure SignalR Service is tuned to apps with the following needs:

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text How to work with Azure Pipelines for devops
Tue, 18 Sep 2018 03:00:00 -0700

Microsoft’s recent renaming of Visual Studio Team Services as Azure DevOps came as a surprise, rebranding a familiar service and adding significant new features. One of those new features, Azure Pipelines, builds on Microsoft’s previous cloud-hosted build service to deliver a more powerful tool for building and delivering on-premises and cloud-hosted applications for Windows, MacOS, and Linux.

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The economy is doing well. Even the Fed is looking to figure out ways to slow things down, so the economic boom does not become overheated and then collapse.

How will the good economy affect the adoption of cloud computing? Most people answer quickly that it’s good for cloud. But that’s not always the case. Some technologies are more valuable the worse the economic climate.

Cloud computing rose in the tough economic times of a decade ago. Enterprises were seeking ways to cut costs, and cloud computing was a major weapon in doing that. Indeed, my cloud computing consulting dance card was full while the banks where failing, because I was the "save money" guy.

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The only difference between technical debt and financial debt is that costs are more often known in advance when taking on financial debt. Both types of debt are a tool when used intelligently with purpose and a plan to manage it, and both can take a devastating toll when used recklessly or imposed through misdirection or miscommunication.

Acceptable vs. unnecessary debt

The original heading here was "necessary vs. unnecessary debt." On further reflection, though, I realized that the only good reasons for incurring debt are time drive. If time is removed as a factor, there is no reasonable need for debt. So then it becomes a question of when time is important enough of a factor to make debt acceptable. The only context I can think of where time is universally an acceptable driver for debt is in an emergency.

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Microsoft has refreshed its ML.Net open source machine learning framework, fitting its beta Version 0.5 with TensorFlow model scoring as a transform to ML.Net. This capability enables use of an existing model from Google’s TensorFlow deep learning and machine learning toolkit in an ML.Net experiment.

What’s new in ML.Net 0.5

Version 0.5 begins adding support for deep learning, with the TensorFlow Transform class, which can take an existing TensorFlow model and get scores from that model into ML.Net. Users of this TensorFlow scoring capability do not need a working knowledge of TensorFlow internal details. The transform is based on code from the TensorFlowSharp .Net bindings.

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Money can’t buy you happiness, but developers just might. According to a new survey from Stripe, companies finally recognize that access to engineering talent is a bigger inhibitor to growth than access to capital. In fact, as fed up as enterprises may be with their outdated IT infrastructure, they’re convinced that if they can just find good developers, most other problems will prove secondary.

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text How to use Dapper in ASP.Net Core
Mon, 17 Sep 2018 03:00:00 -0700

Dapper is an open source, lightweight "micro ORM" that supports many databases including SQL Server, MySQL, SQLite, SQL CE, and Firebird. By using Dapper in your applications you can simplify data access while ensuring high performance. In previous articles here I provided an introduction to Dapper and examined the Dapper Extensions Library

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text Review: Keras sails through deep learning
Mon, 17 Sep 2018 03:00:00 -0700

As I discussed in my review of PyTorch, the foundational deep neural network (DNN) frameworks such as TensorFlow (Google) and CNTK (Microsoft) tend to be hard to use for model building. However, TensorFlow now contains three high-level APIs for creating models, one of which, tf.keras, is a bespoke version of Keras.

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Technical recruiter HackerRank has introduced Tech Talent Matrix, a subscription service that provides data to help with the hiring of software developers.

Tech Talent Matrix draws from an analysis of more than 150 million assessments and company data points. Recruiting performance is measured, with insights provided to help users hire talent. Enterprises are evaluated on their technical recruiting process, including the type of developers they can attract, how well candidates are being assessed, and the level of alignment between hiring managers and recruiters. Companies also can be benchmarked against peers based on industry and size. A company’s position on the matrix is a graphical representation of two factors:

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text What’s new in TypeScript 3.1
Fri, 14 Sep 2018 13:00:00 -0700

Microsoft has released the release candidate for TypeScript 3.1, which focuses on programming enhancements, some of which can break existing code.

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Microsoft has released the production version of Visual Studio 2017 15.8, which offers a unified Docker container experience. It also has released the second beta of Visual Studio 15.9.

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Despite their reputation as laggards, large corporations are as obsessed with innovation as any startup. Blockchain, serverless computing, artificial intelligence, IoT—they want it all. And unlike startups, they can afford to invest the money, time, and people to get it.

But even when you’re blessed with these kinds of corporate resources, hazards can lurk out of sight. It doesn’t just happen with devops (the example I’m using), but agile development and other forms of business transformation as well.

Here’s what to watch out for, and how to avoid the quicksand in the first place when you are introducing devops in the enterprise.

Enter the bean bags

The story often starts with the creation of a corporate innovation center (or incubator hub or disruption lab or similar). The business taps forward-thinking in-house talent and brings in new blood from the outside to experiment with whiz-bang technologies and cutting-edge methods. Sometimes there are bean bags.

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Where have you heard that enterprises using cloud are moving to more complexity as well? That’s right, from this guy.

The growing cloud computing complexity was recently documented by the Wall Street Journal that cites a survey of 46 CIOs by KeyBanc Capital Markets. It found that 32 percent said they plan to use multiple vendors to create internal private cloud systems, while 27 percent planned hybrid cloud arrangements.

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Over the past year, Kubernetes has become the de facto standard for managing and orchestrating Docker containers. To find out why, I asked its creators Craig McLuckie and Joe Beda—cofounders of the startup Heptio and former Google colleagues—to explain Kubernetes’s value to the enterprise.

As Joe Beda describes Kubernetes, it builds on container portability to enable orchestration of many services in many containers. "With Kubernetes, instead of saying I want to run this program on this particular machine, or particular server or VM, you can say, ‘Find me the right place to do that,’" says Beda. "Now you can go from deploying one of something to deploying ten or 100 instances across a whole set of machines. It starts enabling new workflows with how people develop, how they deploy, and how they operate software over the long term."

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Psychiatrists define an asymmetric relationship as one in which people do not feel like equals. In modern business, the CRM is not playing well with others.

The perils of customer relationship management (CRM) systems are long-documented. For 20 years, we have known that CRMs could be slow and buggy, and were often eagerly adopted by companies that weren’t really customer-focused, hadn’t taken the time to understand them, and ended up stalking—not wooing—them.

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LinkedIn has open-sourced a project for scaling and managing deep learning jobs in TensorFlow, using the YARN (Yet Another Resource Negotiator) job scheduling system in Hadoop.

The Tony project came about after LinkedIn tried to use two existing open source solutions for running scheduled TensorFlow jobs on Hadoop and found them both wanting. A few projects to run TensorFlow on Hadoop already exist, but LinkedIn was unsatisfied with them. One, TensorFlow on Spark, runs TensorFlow via Apache Spark’s job engine, but it couples too tightly with Spark. Another, TensorFlowOnYARN, provided the same basic functionality as Tony, but is unmaintained and didn’t provide fault tolerance.

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With the staggering scale of data being used today in IT infrastructures, companies are transitioning to cloud computing services to meet their demands. As such, the demand for IT professionals with cloud experience is growing, and earning a cloud certification can be a lucrative and stable career choice. There are dozens of cloud computing services on the market, one such service being Microsoft Azure.

Microsoft Azure is one of the most widely used cloud computing services on the market. There are numerous Azure certifications that can be earned, such as certifications for designing and implementing solutions for cloud data platforms and big data analytics. This Microsoft Azure Mastery Bundle contains prep courses for 3 Azure certification exams, and it's on sale for over 90% off.

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text When to use a CRDT-based database
Thu, 13 Sep 2018 03:00:00 -0700

Bending the consistency and availability as described by the CAP theorem has been a great challenge for the architects of geo-distributed applications. Network partition is unavoidable. The high latency between data centers always results in some disconnect between the data centers for a short period of time. Thus traditional architectures for geo-distributed applications are designed to either give up data consistency or take a hit on availability.

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IBM was a bit late to the cloud party but has since caught up and is growing quickly. It trades third and fourth place with Google from one market research report to the next, but it is growing quickly.

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text Oracle forges a Java microservices framework
Wed, 12 Sep 2018 14:00:00 -0700

Oracle has introduced Project Helidon, an open source microservices framework for Java.

Helidon features a collection of Java libraries for writing microservices that will run on a web core powered by the Netty network application framework. The project also includes Helidon Reactive WebServer, which provides a functional programming model to run on Netty. Cloud application development is supported, along with health checks, metrics, tracing, and fault tolerance.

Oracle said that although it is already possible to build Java EE (Enterprise Edition) microservices, it is better to have a framework designed for this purpose. The intent has been to build lightweight libraries that do not require an application server and can be used in Java SE (Standard Edition).

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The internet of things has become a reality.  According to IDC, "By 2021, global IoT spending is expected to total nearly $1.4 trillion as organizations continue to invest in the hardware, software, services, and connectivity that enable the IoT."

Led by the manufacturing, transportation, and utilities industries, we might expect to see tens of billions of internet-connected things by 2020. Yet connecting stuff to the internet is really the easy part. The biggest hurdle businesses contend with is how to take advantage of this new technology and the data it can provide for customers, partners, employees and shareholders. To profit from the IoT, leaders must first define what they want from connected devices. They must then design and build the digital solutions required to serve those needs or use cases, and have the skill to deploy them successfully in their business operations.

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10 handy no-cost tools for API development
10 handy no-cost tools for API development

Image by PeteLinforth via Pixabay

The rise of RESTful APIs has been met by a rise in tools for creating, testing, and managing them. Whether you’re an API newbie or an expert on an intractable deadline, you have a gamut of services to help you get your API up and running quick, and many of them won’t cost you a dime.

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As I explained in "Scale out and conquer architectural decisions behind distributed in memory systems," choosing the right open source solution (such as a combination of Apache Ignite, Apache Kafka, Apache Spark, and Kubernetes) for an in-memory computing (IMC) infrastructure ensures a simpler and more cost-effective architecture for applications to support digital transformation and omnichannel customer engagement initiatives.


"Failing fast" lies at the heart of agile. Teams want continuous integration (CI) to provide feedback on their latest updates as soon as possible. CI test results are the primary barometer that developers use to determine whether it’s safe to move on to the next development task, or if they inadvertently broke functionality that users have grown to rely on.

With more extensive and more effective regression testing during CI, you’re much more likely to spot critical problems as soon as they’re introduced—which is when they’re fastest, easiest and cheapest to fix. However, given the frequency of builds in most agile processes, there’s simply not much time available for test execution. Developers expect feedback in a matter of minutes, but most regression test suites—especially in Global 2000 organizations—take hours (or days!) to execute. This seems to force a trade-off: Settle for sluggish CI or scale down testing.

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