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Oracle has announced the release of Java 10 version on March 20. While this is not an LTS version, there are few changes in this version which are worth noting. In this article we’ll look at  5 of the most important things you’ll need to watch out for, especially if you’re a Java developer. Java releases long term support versions in every 3 year. As per this scheduling, their future long term support version, Java 11 will be releasing in Fall 2018. Java 10 is a precursor to that and contains some important changes which will take a clearer shape in the next version. Java 10 is trying to emulate some of the popular features of Scala and Kotlin. One of the primary reasons can be the growing popularity of Kotlin in both web and mobile development domain and also the dynamic typing capability in Scala and Kotlin both.  The introduction of local variable type is one of them. This feature implies that variables can now be declared as “var” and when you assign a certain integer or a string to it then the compiler will automatically know what type of variable it is. Although this doesn’t make Java a dynamically typed language like Python, still this allows a lot more flexibility for the programmers and lets them avoid boilerplates in their code. There are 2 JEPs in JDK 10 that focus on improving the current Garbage Collection (GC) elements. The first one, Garbage-Collector Interface (JEP 304) will introduce a clean garbage collector interface to help improve the source code isolation of different garbage collectors. In current Java versions there are bits and pieces of GC source files scattered all over the HotSpot sources. This becomes an issue when implementing a new garbage collector, since developers have to know where to look for those source files. One of the main goals of this JEP is to introduce better modularity for HotSpot internal GC code, have a cleaner GC interface and make it easier to implement new collectors. Java 10 promises to become much faster than its previous version by making the full garbage collector parallel. This is a welcome move and change from the version 9 since this allows the developers scope to better allocate memory and use the GC (Garbage Collector) in parallel. The GC  in the previous versions didn’t have the capability to load values in parallel and that made it heavy and difficult to operate for complex applications. The present parallel GC removes that factor and makes it much more lightweight and efficient. Java 10 enables programmers to allow heap allocation on alternative memory devices. This feature lets the Java VM decide on the most important tasks and then allocate maximum memory for those priority processes with other processes are allocated to alternative memory. This helps in fastening up the overall process. This change is important for the Java developers because this will help them in better and efficient memory management and hence will increase the performance of their applications. With these changes, Java 10 has opened up the doors for a more open and flexible language which is looking towards the future. With Kotlin breathing down its neck as a worthy alternative, the stage is set for Java to work towards a more dynamic and easy to use power packed version 11 in 2018 fall. We would be waiting for that along with the Java developers for sure. What can you expect from the upcoming Java 11 JDK? Oracle reveals issues in Object Serialization. Plans to drop it from core Java. Java Multithreading: How to synchronize threads to implement critical sections and avoid race conditions  

The new version of macOS called Mojave was announced at Apple’s ongoing annual developer conference, WWDC 2018. It includes a bunch of new features namely dark mode, revamped Mac app store, desktop stacks, security control, safari privacy in addition to other updates and features. The final release will be sometime in fall during September or October, with public beta releasing this summer. Let’s have a look at what’s new in the shiny new macOS version Mojave. Key macOS Mojave Features Dark mode Dark mode is added by Apple to macOS with the latest release. It has the ability to change the dock, taskbar, and chrome around apps into a dark gray color. It doesn’t come with a new functionality though, it’s mainly for aesthetics, just like all the other dark modes. There is also an API available for developers to implement Dark Mode in their apps. Mojave also presents a new Dynamic Desktop which is capable of automatically changing the desktop picture to match the time of day. Revamped Mac app store The Mac app store is finally revamped in Mojave. Taking inspiration from the iOS store that underwent a makeover last year, the redesigned Mac app store consists of new app collection along with a lot more editorial content. There are also going to be many apps from top developers coming to the Mac App Store namely Office from Microsoft and Lightroom CC from Adobe, among others. Apple News, Stocks, Home, Voice memos Apps such as News, Stocks, Voice Memos and Home are available on Mac for the first time. News app comes with articles, photos, and videos which will look great on the Mac display. The home app allows the Mac users to control their HomeKit-enabled accessories. It lets users perform tasks like turning lights off and on, adjusting thermostat settings. Voice Memos makes it easy for you to record personal notes, lectures, interviews, song ideas, etc. You can also access them from iPhone, iPad or Mac. Stocks provides curated market news along with a personalized watchlist which is complete with quotes and interactive charts. Desktop stacks A new feature called stacks cleans up a messy desktop by dedicating folders to specific file types. These folders automatically collect files that belong to them. This way there will be stacks of PDFs, images, movies, etc. Clicking the folders will bring the files to the desktop to make it easy for you to browse through them. Security controls With more pop-ups added by Apple in the new Mojave, you can now control what apps can access your information and hardware. With newly added security controls, you can now decide if you want an app to have access to your location, photos, contacts, microphone, etc. Safari privacy Apple started blocking websites that track you based on your system configuration in Safari. Now, it comes with an added ability which will help you block social networks like Facebook from tracking you across the web using “like” buttons. It also flags reused passwords so users can change them. Finder updates Finder has a new view called “gallery view” which helps scroll through small previews of files There is also going to be a way to view metadata inside a finder window. You can also perform quick actions on files such as rotating a photo or assembling multiple files into a PDF. Markup and screenshots Users can mark up documents and make changes inside of Quick Look which will help quickly deal with files. If you take a screenshot, you will be presented with a button to mark them up. To know more about macOS Mojave, check out the official blog post by Apple. Apple releases iOS 11.4 update with features including AirPlay 2, and HomePod among others WWDC 2018 Preview: 5 Things to expect from Apple’s Developer Conference Apple steals AI chief from Google

Apache Spark team has revealed a new venture during a keynote at Spark AI Summit called Project Hydrogen. This new project focuses on eliminating the obstacles faced by organizations from using Spark with various deep learning frameworks such as TensorFlow and MxNet. The rise of Apache Spark is quite evident from the fact it is one of the highly accepted platforms for big data processing even outperforming other big data frameworks like Hadoop. It has shown a significant growth in the big data field. Due to its excellent functionalities and services, Apache Spark is one of the most used big data unified framework for carrying out data processing, SQL querying, real-time streaming analytics, and machine learning. If you want to understand why Apache Spark is gaining popularity, you can check out our interview with Romeo Kienzler, Chief Data Scientist in the IBM Watson IoT worldwide team. What are the current limitations of Apache Spark? Apache Spark works fine when you want to work in the big data field. However, the power of Spark’s single framework breaks down when one tries to use other third-party distributed machine learning or deep learning frameworks. Apache Spark has its own machine learning library called Spark MLlib, which provides noteworthy machine learning functionalities. However looking at the rate of development and research in the machine learning and artificial intelligence domain, data scientists and machine learning practitioners want to explore the power of leading deep learning frameworks such as TensorFlow, Keras, MxNet, Caffe2, and more. The problem is, Apache Spark and deep learning frameworks don’t play well together. With growing requirement and advanced tasks, Spark users do want to combine Spark together with those frameworks in order to handle complex functionalities. However, the main problem is the incompatibility between the way how Spark scheduler works and other machine learning frameworks works. Do we have any in-house solutions? Basically, there are two possible options for combining Spark with other deep learning frameworks, Option 1 We will need to use two different clusters to carry out individual work. Source: Databricks - Spark AI Summit 2018 As you can see in the preceding image, we have two clusters. All the data processing work which includes data prep, data cleansing and more can be performed in the Spark cluster, the final result is shared to a storage repository (HDFS or S3). The second cluster which is running the distributed machine learning framework can read the data stored in the repository, This architecture no more follows a unified nature. One of the core challenges faced is handling these two disparate systems separately since you need to understand how each system work. Each cluster might follow different debugging schemes, different log files, thus making it very difficult to operate. Option 2 Some users have tried to tackle all the challenges faced in option 1 such as operational difficulties, debugging, testing challenges and more by implementing option 2. As you can see in the following image, here we have one cluster that runs both Spark and distributed machine learning frameworks. However, the result is not so convincing. The main problem with this approach is the inconsistency between how both systems work. There is a great difference between how Spark tasks are scheduled and how deep learning tasks are scheduled. In Spark environment, each job is divided into a number of subtasks that are independent of each other. However, deep learning frameworks use different scheduling schemes. Based on the job, they either use MPI or their own custom RPCs for doing communication. Here they assume complete coordination and dependency among their set of tasks. Source: Databricks - Spark AI Summit 2018 You can see clear signs of this approach when the tasks fail. For example, as shown in the following figure, in the Spark model, when any task fails, the Spark scheduler simply restarts the single task, and thus the entire job is fully recovered. However, in case of deep learning frameworks, because of complete dependency if any of the tasks fails all the tasks need to be launched again. Source: Databricks - Spark AI Summit 2018 The Solution: Project Hydrogen Project Hydrogen aims to solve all the challenges faced while using Spark and other deep learning frameworks together. It is positioned as a potential solution allowing all the data scientists to plug Spark with other deep learning frameworks. This project uses a new scheduling primitive called Gang scheduler. This primitive addresses the dependencies challenge introduced by the deep learning schedulers as shown in option 2. Source: Databricks - Spark AI Summit 2018 In gang scheduling, it has to schedule all or nothing which means it schedule all the tasks in one go or none of the tasks are scheduled at all. This measure will successfully handle the disparity between how both systems work. What’s next? Project Hydrogen API is not ready yet. We can expect them to be added to the core Apache Spark project later this year. The primary goal of this project is to embrace all distributed machine learning frameworks in the Spark ecosystem. Thus allowing every other framework to run as smoothly as Apache Spark’s machine learning library MLlib. Along with Spark support for deep learning frameworks, they are also working on speeding up the data exchanges, which often becomes a potential bottleneck while doing machine learning and deep learning tasks. In order to comfortably use FPGA or GPUs in your latest clusters, Spark is working closely with accelerators. Read more Apache Spark 2.3 now has native Kubernetes support! How to win Kaggle competition with Apache SparkML How to build a cold-start friendly content-based recommender using Apache Spark SQL

Parrot, a Debian-based platform, announced the release of its latest version, Parrot 4.0. This release puts an end to all the development and testing processes of many new features, which were experimented in the previous releases since the release of Parrot 3.9. It also consists of all the updated packages and bug fixes announced since its previous version, Parrot 3.11. So, What’s new in Parrot 4.0? Netinstall Images Introduced Netinstall images are a powerful tool, which enables one to install just the necessary software components. One can even use them to install other desktop environments and to build a system of choice. With the provision of netinstall images in Parrot 4.0,  one can use Parrot as a pentest distribution, and also as a framework to build their very own working environment with ease. Docker images This version includes a release of Parrot’s own Docker templates. Docker is a powerful container technology that allows Parrot users to quickly download a Parrot template and immediately spawn unlimited and completely isolated Parrot instances on top of any host OS. Linux Kernel 4.16 The introduction of the new Linux 4.16 kernel is a very important step forward for Linux distributions. The Linux Kernel 4.16 version includes important updates, such as AMDGPU multi-display fixes, optimized in-kernel filesystem operations and so on. Sandbox Parrot system is secure and sandboxed. This is because of its custom firejail profiles with the underlying AppArmor support. This 4.0 version includes sandboxed applications that are stable and reliable. MATE 1.20 The MATE Desktop Environment is updated to its 1.20 release. This includes many graphic bug fixes and new features, such as HiDPI support, and the ability to auto-resize windows by simply dragging them to the screen corner and can also divide them into new layouts. Nginx This version introduces Nginx as Parrot’s new default web server daemon replacing Apache 2. Apache2 is the most famous web server out there, but it is heavy and complex to configure and maintain. On the other hand, Nginx is very lightweight and easy to use. It is not only a fast and secure web server but also a powerful proxy, cache, load-balancer and general purpose forwarder. And its configuration syntax is very easy to use. Apache2 will be available in the repository or pre-installed as a dependency of some security tools that rely on it. LibreOffice 6 LibreOffice 6 is now included as default in Parrot 4.0, with better documents support, memory efficiency and stability. MD Raid Support The Parrot 4.0 now includes a default MD raid support, which was absent in the previous versions. This is because parrot is also used for forensic analysis, and to open software, raids can be crucial while reading disks in a server environment. Mdadm is also introduced, which can be used as a pre-installed tool. This means that parrot can be installed in a software raid for better reliability. To know more about the new changes in detail, read the release notes. Pentest tool in focus: Metasploit 5 pen testing rules of engagement: What to consider while performing Penetration testing Top 5 penetration testing tools for ethical hackers

In the keynote, at the ongoing WWDC 2018, Apple has shared their latest version of Augmented reality toolkit, ARKit 2.0. The primary focus of Apple this year is primarily on improving the user experience and making the Apple devices perform better with improved functionalities. ARKit 2 features realistic rendering, multiplayer experiences, a new file format, and more. Shared Augmented reality With ARKit 2.0, you can now collaborate with multiple other users in a virtual environment. Apple says “Shared experiences with ARKit 2 make AR, even more, engaging on iPhone and iPad, allowing multiple users to play a game or collaborate on projects like home renovations.” There is also a new spectator mode, if you are keen on watching the game, instead of playing it. With this mode, you will see and experience what the players see and observe. AR that stays the same Persistent AR, as Apple likes to call it, is also another fabulous feature in ARKit 2.0. You can now leave virtual objects in the living world and then return back to them later in time. Interacting with AR becomes more life-like as you can now start a puzzle on a table and come back to it later in the same state. Image detection and tracking also get an update with ARKit 2.0. It can now detect 3D objects like toys or sculptures, and can also apply reflections of the real world onto AR objects. A new file format Apple has introduced a new open file format, usdz, in collaboration with Pixar. This file format is optimized for sharing in apps like Messages, Safari, Mail, Files, and News while retaining powerful graphics and animation features. The format enables the new Quicklook for AR feature, which allows users to place 3D objects into the real world. usdz is a part of the developer preview of iOS 12. It will be available this fall as part of a free software update for iPhone and iPad 2018 models. The Measure app Apple also unveiled its very own AR measuring app. The new iOS 12 app automatically provides the dimensions of objects like picture frames, posters, and signs, and can also show diagonal measurements, and compute area. Users can either take a photo or share these dimensions from their iPhone or iPad. You can tune into Apple’s WWDC event website to watch the keynote and read about other exciting releases. WWDC 2018 Preview: 5 Things to expect from Apple’s Developer Conference. Apple releases iOS 11.4 update with features including AirPlay 2, and HomePod among others. Apple steals AI chief from Google.

Machine Learning has energised software applications with highly accurate predictions thereby upsurging the product demand of tech driven companies. However, while developing such smart applications, numerous machine learning challenges and software development issues are been faced by data scientist and machine learning professionals. Today, Databricks open sources their newly developed framework MLflow, with an aim to simplify their complex machine learning experiments with smart automation and numerous accessibility in deploying your machine learning models across any platform. With MLflow, Machine Learning users can simply standardize their complex processes while building and deploying their machine learning and predictive models. With this framework, data scientists are fueled with lots of automation accessibility through which they can track experiments, package their machine learning codes and manage their models on any of the popular machine learning frameworks. The current platform offers following three components: MLflow Tracking: This component allows you to log codes, data files, config and results. It also allows to query your experiments through which you visualize and compare your experiments and parameters swiftly without much hassle. MLflow Projects: It provides structured format for packaging machine learning codes along with useful API and CLI tools.This allows data scientists to reuse and reproduce their codes and easily chain their projects and workflows together. MLflow Models: It is a standard format for packaging and distributing machine learning models across different downstream tools. Azure ML compatible models, Deploying with Amazon Sagemaker or deploying on a local REST API are some of the examples of distributing models. The current version is just an Alpha release and more features would be added to its full release. To get more details on its core offerings, APIs and command-line interfaces, read the official documentation at mlflow.org. MachineLabs, the browser based machine learning platform, goes open source Microsoft Open Sources ML.NET, a cross-platform machine learning framework Google announces Cloud TPUs on the Cloud Machine Learning Engine (ML Engine)

Microsoft has acquired GitHub in a major deal worth $7.5 billion. Not only has this put the open source community in a frenzy, but has also opened up different options for the developers and programmers who don’t want to share their project and code details with Microsoft. There is a history to this particular behavior of the open source community towards Microsoft. Firstly let’s reframe the question - what is the fear that’s causing the migration? Microsoft has this well known habit of acquiring promising open source projects and then slowly letting them die. They even had a name for the strategy: ‘Embrace, Extend, Extinguish’. That’s a key reason, open source developers dread Microsoft. The other factor for the fear is Microsoft’s history of using their patents to sue open source projects. These are some reasons the open source developers have traditionally avoided Microsoft and their products for a long time.   The other side of the argument is that Microsoft is not the same company as it used to be in terms of their approach to open source mainly due to the change in their leadership team. Their present focus has also shifted from operating systems to the cloud, building Azure solutions, and promoting office 365. They have recently open sourced their scripting language powershell in an attempt to lure the open source developers under the organizational umbrella. In lesser words, Microsoft is trying for an image makeover and their GitHub deal might be yet another attempt to give the open source developers a bigger umbrella and more resources to develop production ready applications.   Whatever’s the actual reason, it’s pretty clear what’s on Open Source developers’ minds. As per the latest tweet from GitLab, the rate of new repositories being added to GitLab has increased significantly since Monday - the 4th of June. The snapshot below shows the spike in posting new repositories in Gitlab.   The trends of both Github and Gitlab have also spiked since the buying out news broke and that clearly shows that there is a huge spike in chatter regarding this. GitLab itself had started pushing a trend called #movingtogitlab  and because of the incoming traffic reaching exceptionally high volume, their servers also crashed for a brief period of time. Gitlab had posted the video tutorial called “Migrating from GitHub to GitLab” on the 3rd of June which has already reached 22.5k views which clearly shows that there have been 20k people at least who have tried to export their GitHub project to GitLab. Having said that let’s take a look at the number of active users for both of these platforms. While GitHub has around 24 million active users, GitLab is at a mediocre 100k. So the exodus of a few thousand might not make a significant dent on GitHub’s user base.   On one hand, the markets have rejoiced over the news of the Microsoft acquisition of Github boosting Microsoft’s stock prices well above 101 USD. On the other hand, the overall feeling towards this acquisition has been quite pessimistic among the developer community to say the least. This deal still has to go through regular auditing to check whether the norms for standard acquisition were followed and other details. The completion of this deal will happen only around December 2018 and the question remains whether Microsoft will be getting the same GitHub that they bought and what will this deal mean for Gitlab. The question on everyone’s mind right now is will Microsoft act as Github’s owner or steward? Will GitHub become the de facto leader for code sharing and pioneer in open source development? Or will other tools like GitLab, Sourceforge, Bitbucket take advantage of the situation and come to the forefront? The most interesting and positive thing to emerge from this scenario would be if Microsoft itself comes across as a leader in open source projects which would mean more funds and resources for useful and viable tech research and development and probably a brighter future for the tech world. Microsoft is going to acquire GitHub 10 years of GitHub Is Comet the new Github for Artificial Intelligence?

A new version of Jest, the popular framework for testing React applications is now available. Jest is developed by Facebook and can be used for testing JavaScript functions, but is specifically aimed at React. Jest is a zero configuration testing platform with features such as snapshot testing, parallelized test runs, built-in code coverage reports, and instant feedback. Jest 23 features major updates. Here are the top ones. Babel and Webpack join the Jest community Webpack saw their total test suite time reduced 6x from over 13 minutes to 2 minutes 20 seconds, after converting from Mocha to Jest 23 Beta. Interactive Snapshot Mode The newly incorporated Interactive snapshot mode, is added as a default watch menu option. With this new mode, testers can browse through each failing snapshot in each failing suite, and review, update or skip each failed snapshots individually. Snapshot Property Matchers Jest now has Snapshot property matchers through which testers can pass properties to the snapshot matcher which specify the structure of the data instead of the specific values. These property matchers are then verified before serializing the matcher type to provide consistent snapshot results across multiple test runs. Jest Each Jest 23 features a new jest-each library inspired by mocha-each and Spock Data Tables. This library defines a table of test cases, and then runs a test for each row with the specified column values. Support is provided for both array types and template literals for all flavors of describe and test. Watch Mode Plugins The watch mode system now allows adding of custom plugins to watch mode. These watch mode plugins can hook into Jest events and provide custom menu options in the watch mode menu. Other changes include: Test descriptions and functions are a mandate. Jest 23 will fail tests that do not include both a function and a description. Undefined props from React snapshots are now removed. MapCoverage, jest.genMockFunction and jest.genMockFn are deprecated. Snapshot name (if provided) is now added to the snapshot failure message so it's easier to find the snapshot that's failing. Mock timestamps are replaced with invocationCallOrder since two or more mocks may often have the same timestamp, making it impossible to test the call order. Mock function call results are added to snapshots so that both the calls and the results of the invocation are tracked. For the complete list of changes and updates, see the changelog. Testing Single Page Applications (SPAs) using Vue.js developer tools What is React.js and how does it work? How to test node applications using Mocha framework

Angular started as a simple frontend library. Today it has transformed in a complete framework as simply ‘Angular’ with continuous version progression from 2 to the recent 6. This progression added some amazing features to Angular, making the overall development process easier. Angular 6, is the latest version, is packed with exciting new features for all of the Angular community. In this article we are going to cover some amazing features which are out with Angular 6. So let’s get started! Angular Elements Consider a search component that we would like to have for a specific Angular application. It can be visualized as follows. In above application the search component uses the input ‘bat’ to fetch the results on the basis of its text similarity. A class named `SearchComponent` must be working beneath the app. With the advent of Angular 6, we can wrap such Angular components into custom elements. Such elements are nothing but DOM elements; in our case a combination of textbox and divs with a composition of javascript function. These elements once segregated can be used independently irrespective of any other frontend libraries like react.js, view or simple jquery. The custom elements are a new way to set the component individually out of the ng framework and use it independently. Ivy: Support for new Angular engine version 6 onwards Angular 6 will introduce us (in the near future) to a new Ivy engine that contributes to great performance and the decrease in load time of an application. Here are some important features of Ivy you need to know. Shaking Tree It is an optimization step that makes sure that unused code is not present in your build bundle. The tree shaking compilation is often used while executing `ng build` command to generate the build. New to what is a build or a bundle? A build or a bundle is a ready-to-go-live set of files that needs to be deployed on the production environment. Let’s  say a frontend project will be needing the following files in a bundle : In your Angular project there might be a component included but is not required. Assume, it falls under a specific if-condition and is not at all executed. The normal dead code elimination tools using static analysis work by retaining the symbols/characters of the reference already present in the unbundled code. Hence the component that was conditionally not at all used, unfortunately remains inside the bundle. The new rendering mechanism Render 2 is built to solve such issues. Now we can specify configuration through instruction based rendering technique. This may include only things that are required which in turn minimizes the size of builds bundles to the great extent. The new Ivy engine seems cool! New cli commands With upgradation to Angular 6, the ng cli package provides two new commands. ng add As its name suggests, the ‘ng add’ command provides you the capability to add a new module/package to your current application. This may be rxjs, material UI libraries etc. Don’t get confused, it doesn’t install the package but simply adds one to your project whenever required. So if you are planning to add a third party library to your Angular app make sure you install it using npm, and then add it using ng add. The automatic addition of such modules helps reduce development time by avoiding errors while adding up a module. ng update The new Angular version 6 cli has the most awaited ‘ng update’ command. This command when run, yields a command line that provides a list of packages that need to be updated over time. In case they are already updated, the command just provides a confirmation that everything is in order. Upgrading to ng 6 A fresh Angular 6 installation is not a problem. You can always follow https://update.Angular.io/ for incorporating changes with respect to updates. Here are a few set of things to do if you are planning to upgrade in your current project. Node.js version 8.9+ Update your Angular configuration //Globally npm i -g @Angular/cli //locally npm i @Angular/cli Once the Angular cli has its latest code, the ng update command is available for use. So let us use it for updating the packages under Angular/cli as follows npm update @Angular/cli Update the Angular/core packages using ng update as follows ng update @Angular/core Angular has rxjs for handling asynchronousity in the application. This library also needs to be updated to rxjs 6. Here is the link for the detailed updation process Update Angular material library that provides beautiful UI components ng update @Angular/material Finally run `ng serve` and test the new setup Besides all the amazing features listed above, Angular 6 provides support to rxJS6, Typescript 2.7 with conditional type declarations and not to forget the service-workers package in Angular’s core. At the time of Angular 6 launch, there were small break points with respect to command line commands like ng updates which are fixed by now and stable. The Angular team is already working towards some more incredible features like new ng-compiler engine, @aiStore (an AI powered solutions store), @mine package for bitcoins and much more in Angular 7. Over the years, the Angular team has continued to provide dedicated support to evolve the project into one of the  best that technology has to offer. With such tenacity, looks like the whole Angular ecosystem is poised to scale even greater heights than before. I, for one, can’t wait to see what they do next in Angular! [author title="Author Bio"] Erina is an assistant professor in the computer science department of Thakur college, Mumbai. Her enthusiasm in web technologies inspires her to contribute to freelance JavaScript projects, especially on Node.js. Her research topics were SDN and IoT, which according to her create amazing solutions for various web technologies when used together. Nowadays, she focuses on blockchain and enjoys fiddling with its concepts in JavaScript.[/author] Why switch to Angular for web development – Interview with Minko Gechev ng-conf 2018 highlights, the popular angular conference Getting started with Angular CLI and build your first Angular Component

Google recently announced that their Google Kubernetes Engine 1.10 is now generally available and is also ready for enterprise use. For a prolonged time, enterprises have faced challenges such as security, networking, logging, and monitoring. With the availability of Kubernetes Engine 1.10, Google has introduced new and exciting features that have a built-in robust security for enterprise use, which are: Shared Virtual Private Cloud (VPC): This enables better control of network resources Regional Persistent Disks and Regional Clusters: These ensure higher-availability and stronger SLAs. Node Auto-Repair GA and Custom Horizontal Pod Autoscaler: These can be used for greater automation. New features in the Google Kubernetes Engine 1.10 Networking One can deploy workloads in Google’s global Virtual Private Cloud (VPC) in a Shared VPC model. This gives you the flexibility to manage access to shared network resources using IAM permissions while still isolating departments. Shared VPC lets organization administrators assign administrative responsibilities, such as creating and managing instances and clusters, to service project admins while maintaining centralized control over network resources like subnets, routers, and firewalls. Shared VPC network in the Kubernetes engine 1.10 Storage This will make it easy to build highly available solutions. The Kubernetes Engine will provide support for the new Regional Persistent Disk (Regional PD). Regional PD enables a persistent network-attached block storage with synchronous replication of data between two zones within a region. One does not have to worry about application-level replication and can take advantage of replication at the storage layer, with the help of Regional PDs. This kind of replication offers a convenient building block using which one can implement highly available solutions on Kubernetes Engine. Reliability Regional clusters, which would be made available in some time soon, allow one to create a Kubernetes Engine cluster with a multi-master, highly-available control plane. This cluster would spread the masters across three zones in a region, which is an important feature for clusters with higher uptime requirements. Regional clusters also offer a zero-downtime upgrade experience when upgrading Kubernetes Engine masters. The node auto-repair feature is now generally available. It monitors the health of the nodes in one’s cluster and repairs nodes that are unhealthy. Auto-scaling In Kubernetes Engine 1.10, Horizontal Pod Autoscaler supports three different custom metrics types in beta: External - For scaling based on Cloud Pub/Sub queue length Pods - For scaling based on the average number of open connections per pod Object - For scaling based on Kafka running in the cluster To know more about the features in detail, visit the Google Blog. Kublr 1.9.2 for Kubernetes cluster deployment in isolated environments released! Kubernetes Containerd 1.1 Integration is now generally available Rackspace now supports Kubernetes-as-a-Service

In one of the most interesting developments in tech for some time (and that's saying something), Bloomberg are reporting that Microsoft has acquired GitHub. Spokespeople from Microsoft and GitHub declined to comment when asked by Bloomberg, but the deal could be announced later today. With 24 million users on the platform, this move could well have an impact across the software world. However, while it may seem surprising, it isn't perhaps quite as shocking as it immediately appears. Microsoft has embraced open source in the last few years; the company is one of the top contributors to the site, according to The Verge. When were rumors of Microsoft's intention to buy GitHub first reported? Reports of Microsoft's intention to acquire GitHub were first made in Business Insider just a few days ago, at the beginning of June 2018. According to the website, sources 'close to both companies' said that serious talks have been happening for the past few months. Informal discussions on the issue have taken place over the last few years - it's only now that they have become more serious. With GitHub's CEO Chris Wanstrath set to leave in August, it makes sense for Microsoft to take the opportunity to make a move to acquire the company now. Why would Microsoft want to acquire GitHub? Microsoft has been playing catch up with the open source revolution. It's attitude towards open source has changed significantly in recent years. It has open sourced a growing number of its tools, including PowerShell, Visual Studio Code and .NET. Back in 2001, former Microsoft CEO Steve Ballmer called Linux a "cancer" (he did later retract his statement). Today, under Satya Nadella, it's a completely different story. For that reason, the acquisition of GitHub represents an important step in the evolution of Microsoft's relationship to the open source world. There are questions around how much Microsoft is really committed to open source. To cynics, embracing open source is as much about business than values. Billion dollar acquisitions don't exactly scream 'free and open software'. However, it is still early days. How the acquisition unfolds, how it will be received by the developer community will be interesting. Whatever you think of the Microsoft's move, GitHub isn't exactly thriving from a business perspective; GitHub lost $66 million in three quarters in 2016. Read next 10 years of GitHub Microsoft releases Windows 10 Insider build 17682! Epicor partners with Microsoft Azure to adopt Cloud ERP

Microsoft announced today that they are releasing Windows 10 Insider build 17682 from the RS5 branch today. The new release includes sets improvements, wireless projection experience, Microsoft Edge improvements, and RSAT along with other updates and fixes. Major improvements and updates Sets Improvements New tab page has been updated which makes it easy to launch apps. On clicking the plus button in a Sets window, apps are visible in the frequent destinations list. The all apps list have been integrated into the new tab page to make it easy to browse apps instead of using the search box. Apps supporting Sets when clicked will launch into a new tab. In case you select News Feed, just select the “Apps” link which is next to “News Feed”, this will help switch to the all apps list. Managing Wireless Projection Experience Earlier, there were disturbances during wireless projection for users when the session was started through file explorer or an app. This has been fixed now with Windows 10 Insider build 17682 as there’ll be a control banner at the top of a screen during a session. The control banner informs you about your connection state, lets you tune the connection as well as helps with quick disconnect or reconnect to the same sink. Tuning is done with the help of settings gear. Screen to screen latency is optimized based on the following scenarios: Game mode makes gaming over a wireless connection possible by minimizing screen to screen latency. Video mode ensures smooth playback of the videos without any glitches on the big screen by increasing screen to screen latency. Productivity mode helps to balance between game mode and video mode. Screen to screen latency is responsive enough so that typing feels natural while ensuring limited glitch in the videos. All connections start off in the productivity mode. Improvements in Microsoft Edge for developers With the latest Windows 10 insider build 17682, there is unprefixed support for the new Web Authentication API (WebAuthN). Web Authentication helps provide a scalable and interoperable solution. It helps with replacing passwords with stronger hardware-bound credentials. Microsoft Edge users can use Windows Hello (via PIN or biometrics). They can also use other external authenticators namely FIDO2 Security Keys or FIDO U2F Security Keys. This helps authenticate the websites securely. RSAT available on demand No need to manually download RSAT on every upgrade. Select the “Manage Optional features” in Settings. Then click on “Add a feature” option which will provide you with all the listed RSAT components. You can pick the components you want, and on next upgrade, Windows will ensure that all those components automatically persist the upgrade. More information about other known issues and improvements is on the Window’s Blog. Microsoft Cloud Services get GDPR Enhancements Microsoft releases Windows 10 SDK Preview Build 17115 with Machine Learning APIs Microsoft introduces SharePoint Spaces, adds virtual reality support to SharePoint

After almost 5 months of hard work by the Flink community, the team is happy to roll out the newest release Apache Flink 1.5.0. This is a major release of the 1.x series featuring advanced capabilities along with over 750+ bugs and issues fixed. Apache Flink is an open-source big data processing framework used for real-time analytics, stream processing and batch processing applications.This framework is capable of delivering fast, efficient, accurate, and high fault tolerance in handling huge massive streams of events. With more than 330 active contributors, Apache Flink is one of the most active stream processing projects of Apache Software Foundation. Key new features and improvements: Rewritten Flink’s Deployment and Process Model Added dynamic support for allocation and release of resources on YARN and Mesos. Simplified deployment on Kubernetes. Requests for job submission, cancellation, job status to the JobManager happen through REST. Broadcast State Connects broadcasted stream such as context data, machine learning models with other streams. Broadcasted states can be checkpointed and restored. Unblocks implementation of “dynamic patterns” feature. Improvements to Flink’s Network Stack Added Credit-based flow control for high throughput. Improved performance by lowering latencies without reduction in throughput. Task-Local State Recovery Keeps copy of the application state on the local disk of each machine. Improved failure recovery. Extending Join Support for SQL and Table API Support for joining of tables on bounded time ranges in both event-time and processing-time. Supports full-history matching similar to standard SQL statements. SQL CLI Client Added SQL CLI client support for processing exploratory queries on data streams. Service added for streaming and batch SQL queries. Various other features and improvements Supports OpenStack’s S3-like file system Improved reading and writing of JSON messages from and to connectors Applications rescaling improved without manual triggers Improved watermarks and latency measures For the complete list of features and improvements, please review the release notes on the official Apache Flink page. Flink Complex Event Processing Top 5 programming languages for crunching Big Data effectively Working with Kafka Streams

AT&T partners with Google cloud to deliver cloud networking solutions for enterprise customers using Partner Interconnect solution. This new offering enables customers to use ATT NetBond and connect to Google Cloud Platform in a secure way. Businesses can also connect to Google Cloud via Cloud VPN. Chief product officer at ATT, Roman Pacewicz said “ We're committed to helping businesses transform through our edge-to-edge capabilities. This collaboration with Google Cloud gives businesses access to a full suite of productivity tools and a highly secure, private network connection to the Google Cloud Platform.” Paul Ferrand, President Global Customer Operations, Google Cloud said “ AT&T provides organizations globally with secure, smart solutions, and our work to bring Google Cloud's portfolio of products, services and tools to every layer of its customers' business helps serve this mission. Our alliance allows businesses to seamlessly communicate and collaborate from virtually anywhere and connect their networks to our highly-scalable and reliable infrastructure” ATT is also offering access to G-suite, Google’s cloud-based productivity suite which includes Gmail, Docs and Drive available via ATT Collaborate. Using Cloud Partner interconnect, it facilitates private connectivity to Google Cloud and helps them run multiple workloads across different cloud environment. It also allows centres that are located far away from a Google Cloud region or point of presence to connect at up to 10Gbps. Additionally, since G Suite is there with AT&T Collaborate, enterprises have access to a single source for chat, voice, video and desktop sharing. Businesses can also enable carrier-grade voice reliability and security from within the G Suite applications.It can also be used across practically any device from any location. Google Compute Engine Plugin makes it easy to use Jenkins on Google Cloud Platform How to Run Hadoop on Google Cloud – Part 1 Google announces Cloud TPUs on the Cloud Machine Learning Engine (ML Engine)

The Anaconda team has announced a new release of Anaconda Distribution 5.2. This new version has brought several new changes in terms of platform changes, user-facing challenges, and backend improvements. Anaconda is a free open-source distribution of Python which allows fast, easier and powerful way to perform data science and machine learning tasks. It is an efficient platform used for carrying out large-scale data processing, scientific computing and more. With over 6 million users, it includes more than 250 data science packages suitable for all major operating systems such as Windows, Linux, and macOS. Every package version is managed by the package management system conda. Some of the noteworthy changes available in Anaconda Distribution 5.2 are: Major highlights More than 100 packages have been updated or added to the new release of Anaconda Distribution 5.2 (Notable Updates includes - Qt v5.9.5, OpenSSL v1.0.2o, NumPy 1.14.3, SciPy v1.1.0, Matplotlib v2.2.2, and Pandas 0.23.0). Now Windows installers control their environment more carefully. Thus even if menu shortcuts fail to get created, it won't lead to a lot of installation issues. macOS pkg installers developer certificate is now updated to Anaconda, Inc. User-facing improvements All default channels now point to repo.anaconda.com instead of repo.continuum.io Now you have more dynamic shortcut working directory behavior thus improving Windows multi-user installations To prevent usability issues, Windows installers now disallow the characters (! % ^ =) in the installation path. Backend improvements Security fixes done for more than 20 packages based on in-depth Common Vulnerabilities and Exposures (CVE) vulnerabilities. Improved behavior of --prune because of history file being updated correctly in the conda-meta directory Windows Installer will now use a trimmed down value for PATH env var, to avoid DLL hell problems with existing software In addition to these, several new changes have been added to all x86 platforms,  Linux distributions, and windows distributions. For the complete list of new changes, you can refer the release notes. In case you want to download the new version of Anaconda Distribution 5.2, you can get the file from the official page. Alternatively, you can update the current Anaconda Distribution platform to version 5.2 by using conda update conda followed by conda install anaconda=5.2. 30 common data science terms explained Data science on Windows is a big no 10 Machine Learning Tools to watch in 2018