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The team behind Google’s Go language, aka Golang, has released the beta version of Go 1.11, the next version of the popular open source language. Go 1.11 is expected to be in production release in August 2018. The most recent beta adds preliminary support for modules.

Next version: What’s new in the Go 1.11 beta

Go 1.11 features an experimental port to the WebAssembly binary format. The port is called JS/Wasm. In addition to potential application performance benefits, WebAssembly provides a binary format that serves as a compilation target for other languages, enabling deployment on the web for both client and server applications.

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AI’s rapid evolution is producing an explosion in new types of hardware accelerators for machine learning and deep learning.

Some people refer to this as a “Cambrian explosion,” which is an apt metaphor for the current period of fervent innovation. It refers to the period about 500 million years ago when essentially every biological “body plan” among multicellular animals appeared for the first time. From that point onward, these creatures—ourselves included—fanned out to occupy, exploit, and thoroughly transform every ecological niche on the planet.

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A distributed file system, a MapReduce programming framework, and an extended family of tools for processing huge data sets on large clusters of commodity hardware, Hadoop has been synonymous with “big data” for more than a decade. But no technology can hold the spotlight forever.

While Hadoop remains an essential part of the big data platforms, and the major Hadoop vendors—namely Cloudera, Hortonworks, and MapR—have changed their platforms dramatically. Once-peripheral projects like Apache Spark and Apache Kafka have become the new stars, and the focus has turned to other ways to drill into data and extract insight. 

Let’s take a brief tour of the three leading big data platforms, what each adds to the mix of Hadoop technologies to set it apart, and how they are evolving to embrace a new era of containers, Kubernetes, machine learning, and deep learning.

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Azure’s service platform’s adoption of Kubernetes and containerschanges how you build, deploy, and manage cloud-native applications, treating containers and services as the targets of your builds, rather than the code that makes up those services.

Kubernetes itself automates much of what had been infrastructure tasks, orchestrating and managing containers. Azure’s AKS tools simplify configuring Kubernetes, but you need to deploy straight into an AKS instance—a hurdle for anyone developing new apps or handling a migration of an existing service. Although AKS itself isn’t expensive, setting up and tearing down orchestration models takes time—time that can better be spent writing and debugging code.

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ASP.Net Web API is a lightweight framework that can be used for building RESTful HTTP services. When working with controller methods in Web API, you will often need to pass parameters to those methods. A “parameter” here simply refers to the argument to a method, while “parameter binding” refers to the process of setting values to the parameters of the Web API methods.

Note that there are two ways in which Web API can bind parameters: model binding and formatters. Model binding is used to read from the query string, while formatters are used to read from the request body. You can also use type converters to enable Web API to treat a class as a simple type and then bind the parameter from the URI. To do this, you would need to create a custom TypeConverter. You can also create a custom model binder by implementing the IModelBinder interface in your class and then implementing the BindModel method. For more on type converters and model binders, take a look at this Microsoft documentation.

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Deep learning is an important part of the business of Google, Amazon, Microsoft, and Facebook, as well as countless smaller companies. It has been responsible for many of the recent advances in areas such as automatic language translation, image classification, and conversational interfaces.

We haven’t gotten to the point where there is a single dominant deep learning framework. TensorFlow (Google) is very good, but has been hard to learn and use. Also TensorFlow’s dataflow graphs have been difficult to debug, which is why the TensorFlow project has been working on eager execution and the TensorFlow debugger. TensorFlow used to lack a decent high-level API for creating models; now it has three of them, including a bespoke version of Keras.

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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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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.

The most prominent among these is Docker Hub, the company’s public exchange for container images. Many open source projects provide official versions of their Docker images there, making it a convenient starting point for creating new containers by building on existing ones, or just obtaining stock versions of containers to spin up a project quickly. And you get one private Docker Hub repository of your own for free. 

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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.

Until recently—even with Xamarin—building cross-platform applications wasn’t easy. For all that the core development tools handle working with iOS and Android from .Net, using it to build apps meant having significant amounts of device-specific code to handle both native UX and deep platform integration. Although you could keep your core code across device-specific projects, building and testing the full application required domain knowledge and specialized skills. The result was code that, although a little cheaper than using native tools for each platform, really wasn’t as cheap to build as it could have been.

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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.

Policy-based authorization is a new feature introduced in ASP.Net Core that allows you to implement a loosely coupled security model. In this article I will explain what policy-based authorization is all about and how we can implement it in ASP.Net Core.

Assuming that you have .Net Core installed in your system, follow the steps below to create a new ASP.Net Core project in Visual Studio 2017.

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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…

The code repository named Git is everyone’s favorite tool for curating software, but even this neat open source software isn’t enough. Most programmers and the teams to which they belong are now wedded to online versions of Git that add many extra layers of analysis and presentation to make it possible to wade through the vast swamp that is our code. 

There are three big contenders now for the best place to stash your regular expressions, anonymous functions, and intense recursive tree-walking flashes of genius: GitHub, Bitbucket, and GitLab. All of them are competing to be the best place for you to store your source.

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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.

Microsoft regularly updates Visual Studio Code. Keep track of the updates’ key features in this changelog.

Where to download Visual Studio Code

To download the editor for Windows, MacOS, and Linux, go to Microsoft’s Visual Code Studio website. It’s also available in Anaconda Python as an option in Distribution 51.

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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.

What it doesn’t explain, however, is why comparatively few companies are investing heavily enough in Kubernetes. Given the central role Kubernetes seems destined to play in shaping enterprise infrastructure, we should see more competition to contribute, given that more code tends to equal more influence in open source. Oddly, only Google and Red Hat seem to be playing to win in Kubernetes, based on contribution counts.

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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.

The latest major revision, MySQL 8, fixes a number of long-standing issues with MySQL—changes major enough to demand a revision to the left of the decimal point. 

In this article we’ll walk through the basic steps needed to bring up a standard MySQL 8 server installation. Along the way we’ll pay attention to the settings you need to keep in mind both when installing fresh or upgrading from a previous MySQL installation.

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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.

There’s a lot of data stored in Microsoft Graph, both at a personal and at a corporate level. It’s an information source that’s quickly growing, with the addition of new applications like Teams, and a rapid rollout of new APIs and queries. Microsoft is doing a lot to encourage developers to use Microsoft Graph in their applications, with APIs and with tools to help users construct and test graph queries.

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The latest version of the container orchestration system Kubernetes, 1.11, adds a new load-balancing method and provides custom resource definitions. 

Where to download Kubernetes

You can download the Kubernetes source from the releases page of its official GitHub repository. Kubernetes is also available by way of the upgrade process provided by the various vendors that supply Kubernetes distributions.

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During my decades as a database application developer, I never imagined in my wildest dreams that I would ever have access to a transactional, planet-scale, distributed database, much less that I would be comparing many of them. But with Google Cloud Spanner, CockroachDB, Azure Cosmos DB, Neo4j Enterprise, and most recently YugaByte DB all available in production, that one-time pipe dream is now quite real.

In broad terms, Google Cloud Spanner offers a scalable, distributed, strongly consistent SQL database as a service that can handle around 2,000 writes per second and 10,000 reads per second, per node, with a median latency of about five milliseconds. To speed up reads that don’t need absolutely up-to-date data, you can ask Spanner for stale reads, since it supports time-travel queries. Spanner uses a Google dialect of SQL and runs only on the Google Cloud Platform.

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Azure WebJobs give you an easy way to schedule and execute background tasks in Microsoft Azure. The Azure WebJobs SDK simplifies the work of creating background tasks with declarative binding and trigger management features. Your Azure WebJobs then can be executed in three different ways: They can run continuously, or on a pre-defined schedule, or even on demand. In this article we will examine how we can build and deploy Azure WebJobs in .Net.

First off, you should make sure that you have Visual Studio 2017 and all of the latest updates installed. You will also need the Azure SDKs including the Azure Functions and Web Jobs Tools. You can install the Azure development tools in Visual Studio by choosing Tools -> Get Tools and Features. And you can ensure you have the latest updates to the Azure Functions and Web Jobs Tools by choosing Tools -> Extensions and Updates. 

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Now that Oracle has released Java Development Kit 10, the next version, JDK 11, is just around the corner. With a release candidate due in August 2018 and the production version in September 2018 as part of Oracle’s new six-month release cadence for the standard edition of Java, Version 11 will have 15 major feature changes.

Java 11 is also set to lose some capabilities through the removal of CORBA and Java EE (recently renamed Jakarta EE) modules, as well as the removal of JavaFX.

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Python 3.7, the latest version of the language aimed at making complex tasks simple, is now in production release. The most significant additions and improvements to Python 3.7 include:

• Data classes that reduce boilerplate when working with data in classes.
• A potentially backward-incompatible change involving the handling of exceptions in generators.
• A “development mode” for the interpreter.
• Nanosecond-resolution time objects.
• UTF-8 mode that uses UTF-8 encoding by default in the environment.
• A new built-in for triggering the debugger.

Where to download Python 3.7

You can download Python 3.7 from the Python Software Foundation.

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