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If you missed the Azure announcements made at Microsoft Ignite 2018, don’t worry, we’ve got you covered. Here are some of the biggest changes and improvements the Microsoft Azure team have made to their cloud offering. Infrastructure Improvements Azure’s new capabilities to deliver the best infrastructure for every workload include: 1. GPU enable and High-Performance VM To deliver the best infrastructure for every workload, Azure has announced the Preview of GPU-enabled and High-Performance Computing Virtual Machines. The two new N-series Virtual Machines have NVIDIA GPU capabilities. The first one is the NVv2 VMs and the second virtual machine is the NDv2 VMs. The two new H-series VMs are optimized for performance and cost and are aimed at HPC workloads like fluid dynamics, structural mechanics, energy exploration, weather forecasting, risk analysis, and more. The first VM is the HB VMs and the second VM is the HC VMs. 2. Networking Azure has announced the general availability of Azure Firewall and Virtual WAN. They have also announced the preview of Azure Front Door Service, ExpressRoute Global Reach, and ExpressRoute Direct. Azure Firewall has a built-in high availability and cloud scalability. The Virtual WAN will provide a simple, unified, global connectivity, and security platform to deploy large-scale branch connectivity. 3. Improved Disk storage Microsoft has expanded the portfolio of Azure Disk offerings to deploy any app in Azure, including those that are the most IO intensive. The new previews include the Ultra SSDs, Standard SSDs, Larger managed disk sizes - to help deal with data-intensive workloads. This will also ensure better availability, reliability, and latency as compared to standard SSDs 4. Hybrid Microsoft has announced new hybrid capabilities to manage data, create even more consistency, and secure hybrid environment. They have introduced the Azure Data Box edge, Windows Server 2019 and Azure stack. With AI enable edge computing capabilities, and OS that supports hybrid management and flexibility for deploying applications, Azure is causing waves in the developer community Built-in security & management For improved Security, Azure has announced new services for preview, like Confidential Computing DC VM series, Secure score, improved threat protection, and network map (preview). These will expand Azure security controls and services to protect network, applications, data, and identities. These services are enhanced by the unique intelligence that comes from the trillions of signals we collect in running first party services like Office 365 and Xbox. For better Management, Azure has announced the preview of Azure Blueprints. These blueprints make it easy to deploy and update Azure environments in a repeatable manner using composable artifacts such as policies, role-based access controls, and resource templates. Azure cost management in the Azure portal (preview) will help to access cost management from PowerBI or directly from your own custom applications. Migration To make the migration to the cloud less challenging, Azure has announced the support for Hyper-V assessments in Azure Migrate, Azure SQL Database Managed Instance, which enables users to migrate SQL Servers to a fully managed Azure service. To help improve your migration experience, we are announcing that if you migrate Windows Server or SQL Server 2008/R2 to Azure, you will get three years of free extended security updates on those systems. This could save you some money when Windows Server and SQL Server 2008/ R2 end of support (EOS). Automated ML capability in Azure Machine Learning The problem of finding the best machine learning pipeline for a given dataset scales faster than the time available for data science projects.  Azure’s Automated machine learning enables developers to access an automated service that identifies the best machine learning pipelines for their labelled data. Data scientists are empowered with a powerful productivity tool that also takes uncertainty into account, incorporating a probabilistic model to determine the best pipeline to try next. To follow more of the Azure buzz, head to  Microsoft’s official Blog   Microsoft’s Immutable storage for Azure Storage Blobs, now generally available Azure Functions 2.0 launches with better workload support for serverless Microsoft announces Azure DevOps, makes Azure pipelines available on GitHub Marketplace  

Microsoft  has announced the general availability of Azure Functions 2.0. The new release aims to handle demanding workloads, which should make managing the scale of serverless applications easier than ever before. With an improved user experience, and new developer capabilities, the release is evidence of Microsoft looking to take full advantage of interest in serverless computing. New features in Azure Functions 2.0 Azure Functions can now run on more platforms Azure Functions are now supported on more environments, including local Mac or Linux machines. An integration with its VS Code will help developers have a best-in-class serverless development experience on any platform. Code optimizations Functions 2.0 has added general host improvements, support for more modern language runtimes, and the ability to run code from a package file. .NET developers can now author functions using .NET Core 2.1.  This provides a significant performance gain and helps to develop and run .NET functions in more places. Assembly resolution functions have been improved to reduce the number of conflicts. Functions 2.0 now supports both Node 8 and Node 10, with improved performance in general. A powerful new programming model Bindings and integrations of Functions 1.0 have been improvised in functions 2.0. All bindings are brought in as extensions. The change to decoupled extension packages allows bindings (and their dependencies) to be versioned without depending on the core runtime. The recent launch of Azure SignalR Service, a fully managed service, enables focus on building real-time web experiences without worrying about setting up, hosting, scaling, or load balancing the SignalR server. Find an extension for this service, in this GitHub repo. Check out the SignalR Service binding reference to start building real-time serverless applications. Easier development To improve productivity, Microsoft has introduced a powerful native tooling inside of Visual Studio, VS Code, VS for Mac, and a CLI that can be run alongside any code editing experience. In Functions 2.0, more visibility is given to distributed tracing. Dependencies are automatically tracked, and cross-resource connections are automatically correlated across a variety of services To know more about the updates in Azure Functions 2.0  head to Microsoft’s official Blog Microsoft’s Immutable storage for Azure Storage Blobs, now generally available Why did last week’s Azure cloud outage happen? Here’s Microsoft’s Root Cause Analysis Summary.

The Python team came out with a new simple and powerful library called Pypeline, last week for creating concurrent data pipelines. Pypeline has been designed for solving simple to medium data tasks that require concurrency and parallelism. It can be used in places where using frameworks such as Spark or Dask feel unnatural. Pypeline comprises an easy to use familiar and functional API. It enables building data pipelines using Processes, Threads, and asyncio.Tasks via the exact same API. With Pypeline, you also have control over memory and CPU resources which are used at each stage of your pipeline. Pypeline Basic Usage Using Pypeline, you can easily create multi-stage data pipelines with the help of functions such as map, flat_map, filter, etc. To do so, you need to define a computational graph specifying the operations which are to be performed at each stage, the number of resources, and the type of workers you want to use. Pypeline comes with 3 main modules, and each of them uses a different type of worker. To build multi-stage data pipelines, you can use 3 type of workers, namely, processes, threads, and tasks. Processes You can create a pipeline based on multiprocessing. Process workers with the help of process module. After this, you can specify the numbers of workers at each stage. The maxsize parameter limits the maximum amount of elements that the stage can hold simultaneously. Threads and Tasks Create a pipeline using threading.Thread workers by using the thread module. Additionally, in order to create a pipeline based on asyncio.Task workers, use an asyncio_task module. Apart from being used to create multi-stage data pipelines, it can also help you create pipelines with the help of the pipe | operator. For more information, check out the official documentation. How to build a real-time data pipeline for web developers – Part 1 [Tutorial] How to build a real-time data pipeline for web developers – Part 2 [Tutorial] Create machine learning pipelines using unsupervised AutoML [Tutorial]

Google announced a new Chief Privacy Officer as Keith Enright, yesterday, who has served a decade at leading Google’s Privacy Legal team. Enright has always been heavily involved in speaking out regarding Google’s Privacy and Security regulations. As a Chief Privacy Officer, Enright will be responsible for setting the privacy program at Google. This includes updating the security tools, policies, and practices as user-focused. “My team’s goal is to help you enjoy the benefits of technology while remaining in control of your privacy” mentions Enright in the Google outreach page. Google has already been taking measures when it comes to security as it launched a “Protect your Election program”, last month, which included security policies to defend against the state-sponsored phishing attacks. “This is an important time to take on this new role. There is real momentum to develop baseline rules of the road for data protection. Google welcomes this and supports comprehensive, baseline privacy regulation. People deserve to feel comfortable that all entities that use personal information will be held accountable for protecting it” as per the Google blog. Lately, there’s been a lot of companies raising voice against security-related issues. For instance, YouTube’s CBO and German OpenStreetMap, spoke out against the Article 13 of EU’s controversial copyright law. In light of organizations citing EU’s privacy laws as strict, Enright proposed a new Privacy framework which includes Google’s view on the requirements, scope, and enforcement expectations in data protection laws. This framework has been established using privacy frameworks, and the services that depend on personal data. Also, it will be complying with the evolving data protection laws around the world. “These principles help us evaluate new legislative proposals and advocate for responsible, interoperable and adaptable data protection regulations. How these principles are put into practice will shape the nature and direction of innovation”, says Enright. Principles in the new framework are based on established privacy regimes. These are applicable to organizations which are responsible for making decisions about the collection and use of personal information. Enright will be discussing the principles in the framework and Google’s work on privacy and security with the U.S. Senate later this week. The new framework states the requirements, scope, and accountability in data protection laws as follows: Requirements Collecting and using personal information responsibly. Maintaining Transparency is mandatory for helping individuals be informed. Placing reasonable limitations on the means of collecting, using, and disclosing personal information. Maintaining the quality of personal information. Make it practical for individuals so that they can control the use of personal information. Giving individuals an ability to access, correct, delete and download personal information if it's about them. Requirements needed to secure personal information should be included. Scope and Accountability Holding organizations accountable for compliance. More focus on the risk of harm to individuals and communities. Direct consumer services should be distinguished from enterprise services. Personal information should be defined flexibly to ensure the proper incentives and handling. Rules should be applied to all organizations that process personal information. Regulations should be designed for improving the ecosystem and accommodating the changes in technology and norms. A geographic scope that accords with international norms should be applied. Encouraging global interoperability. “Sound practices combined with strong and balanced regulations can help provide individuals with confidence that they’re in control of their personal information,” says Enright. For more information, check out the official framework.  EU slaps Google with $5 billion fine for the Android antitrust case Ex-Google CEO, Eric Schmidt, predicts an internet schism by 2028 Google plans to let the AMP Project have an open governance model, soon!

macOS Mojave was released yesterday with plenty of new features and a bypass flaw. The public beta was released in June at Apple’s annual developer conference, WWDC 2018. The final version has now been released with visual, utility and security improvements. The changes make macOS more clutter free. There are also certain changes made to the Mac store, iTunes, Safari and some other Apple applications. A new dark mode The new Apple macOS Mojave brings a new dark mode that is system-wide and covers all applications made by Apple. This includes the file browser, Mac App Store, and Xcode among others. The controls go into the background while focusing on what is on the screen for better focus. Third party applications using standard colors via the AppKit will also be adopting the new theme. Desktop changes in macOS Mojave The desktop picture changes throughout the day via Dynamic Desktop. It mimics sunlight and gives an effect of day or night in the desktop. For example, it is dark in the early morning, bright at noon and gets dark again at night. Similar file types can be stacked together to keep your desktop clutter-free.  For example, all PDFs in one stack, images in one, spreadsheets in one, and so on. The files can also be grouped by dates and tags. Finder and Quick Look Files can be found with ease using large previews in Gallery View. All the metadata for all file types is now displayed in the Preview pane. There are shortcuts like rotating an image, creating a PDF, and more all from the Finder using Quick Actions. You can mark up and sign PDFs, crop images, and trim audio and video files via Quick Look. macOS Mojave makes the Finder look for and do certain tasks with abilities to quickly locate a file by how it looks, instantly see metadata, and perform Quick Actions on files without even opening any application. These quick actions can be performed to multiple files at once. More screenshot options Taking screenshots is now easier and just a click away for various kinds of screenshots. Once a screenshot is captured, a small preview is shown on the corner of your screen. You can then click on it and edit the screenshot taken. There is also a new easy to use menu with options for screen recording tools and the option to choose where the screenshot is saved including saving to the clipboard. You can also drag the preview to insert the screenshot into a document. Continuity camera on iPhone macOS Mojave comes with a new feature called Continuity camera. If you click a picture of an object or scan a document with your iPhone, it will automatically appear on your Mac. This way you can quickly click a picture or scan a document and insert it into the document you’re working on. Continuity camera works with different applications like Finder, Mail, Messages, Notes, Pages, Keynote, and Numbers. New apps brought to macOS The Mac App Store now has handpicked apps in the new Discover, Create, Work, and Play tabs. Apps from iOS, Apple News, Stocks, Voice Memos and Home are now available in macOS Mojave redone to fit a computer screen. On iTunes, you can search lyrics with a few words you remember and listen to what your friends are listening to. More security controls There are better controls for accessing the camera or microphone on macOS Mojave. There are options to allow or deny when an application is trying to access them. This applies to messages history and mail database. There is also improved intelligent tracking prevention to keep embedded content like social media like buttons, share buttons and comments from tracking you without your permission. Safari now automatically creates, stores and autofills strong passwords for the user. macOS also flags existing passwords that have been reused in Safari for easily updating them. A Privacy flaw allowing unauthorized access Speaking of security and privacy, there is also a flaw in the new macOS Mojave. In a video, Patrick Wardle, an experienced macOS hacker demonstrates a privacy flaw in macOS Mojave. Wardle shows that the security in macOS Mojave can be bypassed to gain access to sensitive user data, like address book information. As told to the Bleeping Computer, "I found a trivial, albeit 100% reliable flaw in their implementation," He also added that it allows a malicious or untrusted app to bypass the new security mechanism and access the sensitive details without authorization. https://vimeo.com/291491984 Wardle tweeted that the improved privacy protections in macOS are not delivered. And this is not specific to the new dark mode, any mode is vulnerable. Source: Twitter Wardle plans to share the specifics and more details about the vulnerability at Objective By the Sea, a Mac security conference to be held in November. You can upgrade to macOS Mojave from the Apple website. macOS gets RPCS3 and Dolphin using Gfx-portability, the Vulkan portability implementation for non-Rust apps Google open sources Filament – a physically based rendering engine for Android, Windows, Linux and macOS macOS Mojave: Apple updates the Mac experience for 2018

Yesterday, Google announced the stable release of Android Studio 3.2 with a variety of new features and improvements. This version comes with 20+ features including Android App Bundle, Energy Profiler, and Android Emulator Snapshots. What’s new in Android Studio 3.2 Android App Bundle With Android App Bundle, the new app publishing format, you can deliver smaller APKs to your users and reduce the download size of your app. Once Google Play has your app bundle, it uses an app serving model named Dynamic Delivery. This serving model processes your app bundle to generate and serve optimized APKs for each user's device configuration. This eliminates your efforts of building, signing, and managing multiple APKs to support different devices, and users get smaller, more optimized downloads. Android Studio 3.2 provides you a CLI, using which you can easily build your code as an app bundle. Energy Profiler Battery life is one of the key concerns of users, so it is important to check the energy consumption done by your app. Energy Profiler helps you find where your app is using more energy than necessary. With the Energy Profiler, you can visualize the estimated energy usage of system components, plus inspect background events that may lead to battery drainage. To use it, ensure you are connected to an Android device or emulator running Android 8.0 Oreo (API 26) or higher. Slices support Slices are UI templates that can be used to display rich, dynamic, and interactive content from your app in the Google Search suggestions and Google Assistant. Built-in templates are available, to help you extend your app with the new Slice Provider APIs. You can also do lint checks to ensure that you're following best practices when constructing the Slices. To start constructing your Slices, right-click on your project folder, and navigate to New | Other | Slice Provider. Sample data You can now add sample data to a TextView, an ImageView, or a RecyclerView from within the Layout Editor. This will make it easier for you to visualize the look and feel of a layout while designing your app. ‘What's New’ assistant This newly added assistant panel automatically informs developers about the latest changes in the IDE. You can also open the panel by navigating to Help | What's New in Android Studio. AndroidX Refactoring Support You can now add the Android extension (AndroidX) libraries in a new project by adding the following line in your gradle.properties file: android.useAndroidX=true To help you migrate your project to the new namespace and dependencies, Android Studio 3.2 provides you a new built-in refactoring action. Also, the Android Studio build system will automatically convert any Maven dependencies that have not migrated to the AndroidX namespace. With Android Studio 3.2 and higher, you can quickly migrate an existing project to use AndroidX by selecting Refactor > Migrate to AndroidX from the menu bar. IntelliJ Platform and Kotlin update Android Studio 3.2 comes with IntelliJ 2018.1.6 platform release and  Kotlin 1.2.61. Many improvements to dataflow analysis, debugging, new inspections, have been added to this release of IntelliJ. Kotlin 1.2.61 provides support for the Kotlin-friendly Android 9 Pie SDK. Emulator Snapshots With the latest Android Emulator, you can create a snapshot of the current state of your emulator and boot up and switch into any snapshot in under 2 seconds. In Android Studio 3.2, Android Snapshots are even faster to save and load due to underlying speed enhancements. Using Android snapshots you can pre-configure an Android Virtual Device (AVD) with the presets, apps, data, and settings that you want in-place, and repeatedly go back to the same snapshot. Microsoft Hyper-V and AMD processor support Google has finally addressed this long-standing user request from the Android developer community. Now you can run the Android Emulator on Windows 10 computers that have Hyper-V enabled while Intel HAXM remains the default hypervisor. Support for AMD processor is added to provide developers hardware-accelerated performance. Screen Record in Android Emulator With the new screen recording feature in the Android Emulator, you can now perform screen and audio recording on any Android API and save this recording to a WebM or animated GIF file. You can enable this feature via the Android Emulator Extended Controls panel, command line, or from Android Studio. These were few of the features that they have added, and there are more to explore. You can find the full list at Android Developer Blog. Entry level phones to taste the Go edition of the Android 9.0 Pie version Did you know your idle Android device sends data to Google 10 times more often than an iOS device does to Apple? 6 common challenges faced by Android App developers

At the” Future of Search” event held at San Francisco yesterday, Google celebrated its 20th anniversary announcing a variety of new features to Google Search engine. Their proprietary search engine uses sophisticated machine learning, computer vision, and data science. The focus of this event was Artificial Intelligence and making new features available on a smartphone. Let’s look at what all was announced. Activity Cards on Google Discover Perhaps the most significant feature, is the Google Discover which is a completely revamped version of Google Feed. Google Feed is the content discovery news feed available in the dedicated Google App and on the Google’s homepage. Now, it has got a new look, brand, and feel. It features more granular controls over content that appears. A new feature called activity cards will show up in a user’s search results if they've done searches on one topic repetitively. This activity card will help users pick up from where they left off their Google Search. Users can retrace their steps to find useful information that they have found earlier and might not remember which sites had that. Google Discover starts with English and Spanish in the U.S. and will expand to more languages and countries soon. Collections in Google Search Collections in Google Search can help users keep track of content they have visited, such as a website, article, or an image, and quickly get back to it later. Users can now add content from an activity card directly to Collections. This makes it easy to keep track of and organize the content to be revisited. Dynamic organization with Knowledge graph Users will see more videos and fresh visual content, as well as evergreen content—articles and videos that aren’t new to the web, but are new to you. This feature uses the Topic Layer in the Knowledge graph to predict level of expertise on a topic for a user and help them further develop those interests. The Knowledge Graph can intelligently show relevant content, rather than prioritizing chronological order. The content will appear based on user engagement and browsing history. The Topic Layer is built by analyzing all the content that exists on the web for a given topic and develops hundreds and thousands of subtopics. It then looks for patterns to understand how these subtopics relate to each other, to explore the next content a user may want to view. AMP Stories Google will now use Artificial Intelligence to create AMP stories, which will appear in both Google Search and image search results. AMP Stories is Google’s open source library that enables publishers to build web-based flipbooks with smooth graphics, animations, videos, and streaming audio. Featured Videos The next enhancement is Featured Videos that will semantically link to subtopics of searches in addition to top-level content. Google will automatically generate preview clips for relevant videos, using AI to find the most relevant parts of the clip. Google Lens Google has also improved its image search algorithm due to which images will now be sorted by the relevance of the web results they correspond to. Image search results will also contain more information about the pages that they come from. They also announced Google Lens, its visual search tool for the web. Lens in Google Images will analyze and detect objects in snapshots and show relevant images. Better SOS Alerts Google is also updating their SOS Alerts on Google Search and Maps with AI. They will use AI and significant computational power to create better forecasting models that predict when and where floods will occur. This information is also intelligently incorporated into Google Public Alerts. Improve Job Search with Pathways They are also improving their job search with AI by introducing a new feature called Pathways. When someone searches for jobs on Google, they will be shown jobs available right now in their area, but also be provided with information about effective local training and education programs. To learn in detail about where Google is headed next, read their blog Google at 20. Policy changes in Google Chrome sign in, an unexpected surprise gift from Google The team also announced a policy change in Google's popular Chrome browser which was not taken well. Following this, the browser automatically logs users into Chrome using other Google services. This has got people worried about their privacy as this can lead to Google tracking their browsing history and collecting data to target them with ads. Prior to this unexpected change, it was possible to sign in to a Google service, such as Gmail, via the Chrome browser without actually logging in to the browser itself. Adrienne Porter Felt, engineer, and manager at Google Chrome has however clarified the issue. She said that the Chrome browser sign in does not mean that Chrome is automatically sending your browsing history to your Google account. She further added, “My teammates made this change to prevent surprises in a shared device scenario. In the past, people would sometimes sign out of the content area and think that meant they were no longer signed into Chrome, which could cause problems on a shared device.” Read the Google clarification report on the Reader app. The AMPed up web by Google. Google announces Flutter Release Preview 2 with extended support for Cupertino themed controls and more! Pay your respects to Inbox, Google’s email innovation is getting discontinued.  

Yesterday, the Torch community announced the launch of Torch AR, a 3D design platform connecting the world of web and mobile application design to spatial computing. Designing and creating in 3D is tricky at times and requires special knowledge and skills to use 3Dcomputer-aided design apps or code from scratch. With the help of Torch AR, the Torch 3D Inc attempts to offer a mobile augmented reality (AR) prototyping and design app that lets users build interactive 3D prototypes quickly and easily. With the new Torch AR app, users will be able to: build interactive prototypes collaborate on projects in real-time work with existing 3D and 2D files, tools or workflows All this, using the same mobile device where the finished apps will run on. The community has only launched the first piece of a platform and not the complete application. Torch AR will continue to evolve and improve resulting in an app that can not only prototype, but also build, distribute, and manage user’s spatial application. Features of the new Torch AR platform The power to design in 3D Users can easily design and build prototypes on their devices, in 3D, using augmented reality features. This takes away the task of constantly switching between the desktop and the device. Apart from a user’s mobile device (iOS, for now, Android soon), there’s no special hardware required. Also, users need not have game development or coding experience to build in Torch AR. Free to use with collaboration in real-time Torch AR is free of charge for all users. Even if more features and services are built, the design environment released today will remain free. Projects built, can be shared with anyone. They can also be viewed for free in Torch. Many users can simultaneously work together in real-time from anywhere and can even iterate on designs in minutes. Build interactive prototypes Torch's interactions system lets users go beyond simple AR sticker apps to build powerful multi-scene augmented reality experiences. Add Sketchfab model with a swipe Now users can add the perfect Sketchfab model to their project in a few seconds with a swipe. Sketchfab is the largest platform to publish and find 3D models online. Alban Denoyel, CEO and co-founder of Sketchfab, says, “With Torch, you can just search, then drag and drop Sketchfab models into a scene and start prototyping. Torch’s focus on making 3D prototyping easy is a perfect complement to Sketchfab’s focus on providing diverse and qualitative 3D assets.” To know more about Torch AR head over to the Torch3D blog. Deep Learning with Torch Facebook launched new multiplayer AR games in Messenger 7 AI tools mobile developers need to know

Microsoft made several key announcements at their Ignite 2018 event, which began yesterday in Orlando, Florida. The biggest announcement of them all was the public preview availability of SQL Server 2019. With this new release of SQL Server, businesses will be able to manage their relational and non-relational data workloads in a single database management system. What we can expect in SQL Server 2019 Microsoft SQL Server 2019 will run either on-premise, or on the Microsoft Azure stack Microsoft announced the Azure SQL Database Managed Instance, which will allow businesses to port their database to the cloud without any code changes Microsoft announced new database connectors that will allow organizations to integrate SQL Server with other databases such as Oracle, Cosmos DB, MongoDB and Teradata SQL Server 2019 will get built-in support for popular Open Source Big Data processing frameworks such as Apache Spark and Apache Hadoop SQL Server 2019 will have smart machine learning capabilities with support for SQL Server Machine Learning services and Spark Machine Learning Microsoft also announced support for Big Data clusters managed through Kubernetes - the Google-incubated container orchestration system With organizations slowly moving their operations to the cloud, Microsoft seems to have hit the jackpot with the integration of SQL Server and Azure services. Microsoft has claimed businesses can save upto 80% of their operational costs by moving their SQL database to Azure. Also, given the rising importance of handling Big Data workloads efficiently, SQL Server 2019 will now be able to ingest, process and analyze Big Data on its own with built-in capabilities of Apache Spark and Hadoop - the world’s leading Big Data processing frameworks. Although Microsoft hasn’t hinted at the official release date yet, it is expected that SQL Server 2019 will be generally available in the next 3-5 months. Of course, the duration can be extended or accelerated depending on the feedback received from the tool’s early adopters. You can try the public preview of SQL Server 2019 by downloading it from the official Microsoft website. Read more Microsoft announces the release of SSMS, SQL Server Management Studio 17.6 New updates to Microsoft Azure services for SQL Server, MySQL, and PostgreSQL Troubleshooting in SQL Server

Yesterday, HAProxy published an article discussing stick tables, an in-memory storage. Introduced in 2010, it allows you to track client activities across requests, enables server persistence, and collects real-time metrics. It is supported in both the HAProxy Community and Enterprise Edition. You can think of stick tables as a type of key-value store. The key here represents what you track across requests, such as a client IP, and the values are the counters that, for the most part, HAProxy takes care of calculating for you. What are the common use cases of stick tables? StackExchange realized that along with its core functionality, server persistence, stick tables can also be used for many other scenarios. They sponsored its developments and now stick tables have become an incredibly powerful subsystem within HAProxy. Stick tables can be used in many scenarios; however, its main uses include: Server persistence Stick tables were originally introduced to solve the problem of server persistence. HTTP requests are stateless by design because each request is executed independently, without any knowledge of the requests that were executed before it. These tables can be used to store a piece of information, such as an IP address, cookie, or range of bytes in the request body, and associate it with a server. Next time when HAProxy sees new connections using the same piece of information, it will forward the request on to the same server. This way it can help in tracking user activities between one request and add a mechanism for storing events and categorizing them by client IP or other keys. Bot detection We can use stick tables to defend against certain types of bot threats. It finds its application in preventing request floods, login brute force attacks, vulnerability scanners, web scrapers, slow loris attacks, and many more. Collecting metrics With stick tables, you can collect metrics to understand what is going on in HAProxy, without enabling logging and having to parse the logs. In this scenario Runtime API is used, which can read and analyze stick table data from the command line, a custom script or executable program. You can visualize this data using any dashboard of your choice. You can also use the fully-loaded dashboard, which comes with HAProxy Enterprise Edition for visualizing stick table data. These were a few of the use cases where stick tables can be used. To get a clear understanding of stick tables and how they are used, check out the post by HAProxy. Update: Earlier the article said, "Yesterday (September 2018), HAProxy announced that they are introducing stick tables." This was incorrect as pointed out by a reader, stick tables have been around since 2010. The article is now updated to reflect the same.    Use App Metrics to analyze HTTP traffic, errors & network performance of a .NET Core app [Tutorial] How to create a standard Java HTTP Client in ElasticSearch Why is everyone going crazy over WebAssembly?

GitLab 11.3 was released on Saturday with support for Maven repositories, Code Owners, Protected Environments and other changes. These new added features help in automation of controls around environments and code while also providing further efficiencies for Java developers. Maven repositories in GitLab 11.3 Maven repositories are now directly available in GitLab. This gives Java developers a secure, standardized way to share version control in Maven libraries. It also saves time by reusing these libraries across projects but it is available only on GitLab premium. Lower-level services can now have their packaged libraries published to their project’s Maven repository. They can share a simple XML snippet with other teams to utilize the library while Maven and GitLab do the rest. Code owners and protected environments GitLab Starter now supports assignment of Code Owners to files indicating the appropriate team members contributing to the code. This is a primer for future releases, which will enforce internal controls at the code level. Operators can also use Protected Environments for setting permissions to determine which users can deploy code to production environments. This significantly reduces the risk of an unintended commit. This feature is also available only on premium. Epic forecasting with integrated milestone dates The new Portfolio Management feature in GitLab Ultimate forecasts an epic's start and end dates automatically based on the milestone dates of its issues. Portfolio managers will be able to compare their planned start and end dates against the scheduled work enabling faster decisions on delivery and plan adjustments. In older versions, fixed values could be set for the planned start and end dates of an epic. This was useful for high-level planning of epics. However, as issues are attached to the epic and the issues are scheduled for work with actual milestones, it is useful to have epic dates reflecting those milestones. In this version, the static values for the dates can be changed to a dynamic value called ‘From milestones’. The dynamic version of epic planned end dates are analogous. This is a useful feature to have if you want seamless transition from high-level, top-down planning to micro-level, and bottom-up planning. For more information, visit the GitLab website. GitLab raises $100 million, Alphabet backs it to surpass Microsoft’s GitHub Gitlab 11.2 releases with preview changes in Web IDE, Android Project Import and more GitLab is moving from Azure to Google Cloud in July

Today, ID R&D, the biometric solutions provider offering proprietary AI-based behavioral, voice, and anti-spoofing user authentication capabilities, releases SafeMessage, the industry’s first biometric authentication technology for messaging. ID R&D will be demoing SafeMessage, as well as its other award-winning voice and behavioral biometric products, today at FinovateFall. SafeMessage, offers multi-layer continuous authentication of verified users when integrated across messaging platforms (WhatsApp, Telegram, Skype, Slack, and others) without impacting user’s experience. It combines voice, behavioral, and facial recognition, along with voice and facial “liveness” analysis to provide unmatched authentication and security. Requiring a mere 1-2 seconds of free speech, keystrokes, or facial scans, SafeMessage adds to ID R&D’s suite of comprehensive, frictionless biometric solutions. SafeMessage provides frictionless, yet continuous authentication, and can also differentiate between an authorized user and a voice recording or a computer-generated simulation (for voice input). Alexey Khitrov, CEO of ID R&D says, “ID R&D is thrilled to be the first to introduce frictionless authentication to messaging apps and platforms. Now secure and seamless communication is a possibility for all end users, whether over text, mobile app, instant message, chat, or the internet.” To know more about SafeMessage, visit ID R&D website. Microsoft Edge introduces Web Authentication for passwordless web security Machine learning APIs for Google Cloud Platform Google updates biometric authentication for Android P, introduces BiometricPrompt API

Go, the popular programming language is adding an internal language server protocol (LSP). This is expected to bring features like code autocompletion and diagnostics available in Golang. LSP is used between a user and a server to integrate features such as autocomplete, go to definition, find all references and alike into the tool. It was created by Microsoft to define a common language for enabling programming language analyzers to communicate. It is growing in popularity with adoption from companies like Codenvy, Red Hat, and Sourcegraph. There is also a rapidly growing list of editor and language communities supporting LSP. Golang already has a language server available on GitHub. This version has support for Hover jump to def, workspace symbols, and find references. But, it does not support code completion and diagnostics. Sourcegraph CEO Quinn Slack stated in a comment on Hacker News: “The idea is that with a Go language server becoming a core part of Go, it will have a lot more resources invested into it and it will surpass where the current implementation is now.” The Go language server made by Sourcegraph available currently on GitHub is not a core part of Golang. It uses tools and custom extensions not maintained by the Go team. The hope is that the core LSP implementation will be good enough and that SourceGraph can re-use this implementation in the future to bring down the number of implementations to just one. Slack said in a comment that they are very happy with this implementation: “We are 10,000% supportive of this, as we've discussed openly in the golang-tools group and with the Go team. The Go team was commendably empathetic about the optics here, and we urged them very, very, very directly to do this.” This core implementation of LSP by the Golang team is also beneficial for Sourcegraph from a business perspective. Sourcegraph sells a product that lets you search and browse all your code, which involves using language servers for certain features like hovers, definitions and references. Since the core work will be done by the Golang team, Sourcegraph won’t have to invest more time into building their implementation of Go language server. For more information, visit the Googlesource website. Golang 1.11 is here with modules and experimental WebAssembly port among other updates Why Golang is the fastest growing language on GitHub Go 2 design drafts include plans for better error handling and generics

Facebook made its distributed log system, called LogDevice available as an open source project, two weeks back. It is a highly scalable and fault-tolerant distributed data store for sequential data. LogDevice comes with features such as high write availability, consistency guarantees, non-deterministic record placement, and a local log store. LogDevice is widely used at Facebook. Existing use cases of LogDevice include stream processing pipelines, distribution of index updates in large distributed databases, machine learning pipelines, replication pipelines, and durable reliable task queues. LogDevice is designed from the ground up so that it can serve different logs with high reliability and efficiency at scale. It is also highly tunable which allows the use cases as mentioned above to be optimized for the right set of trade-offs when it comes to durability-efficiency and consistency-availability space. Let’s have a look at the key features of LogDevice. High write availability LogDevice comes with high write availability which is uncommon in most of the existing logging applications of Facebook. LogDevice efficiently separates record sequencing from record storage. It uses non-deterministic placement of records which improves the write availability and betters the tolerate temporary load imbalances caused by spikes in the write load on individual logs. Consistency Guarantees The consistency guarantees provided by a LogDevice log are similar to the ones provided by a record-oriented file system. It comes with built-in data loss detection and reporting. In case data loss occurs, the Log sequence numbers (LSNs) of all records that were lost gets reported to every reader attempting to read the affected log and range of LSNs. Although, there are no ordering guarantees provided for records of different logs. Non-deterministic record placement LogDevice has a different approach when it comes to record placement. First, the ordering of records in a log is decoupled from the actual storage of record copies. For each log in a LogDevice cluster, it runs a sequencer object whose only job is issuing the monotonically increasing sequence numbers as records. The sequencer runs either on a storage node or on a node which has been reserved for sequencing. After the record has been stamped with a sequence number, the copies of that record get stored on any storage node within a cluster. The LogDevice client library is capable of performing the reordering and occasional de-duplication of records. This makes sure that the records get delivered to the reader application in the order of their LSNs. Local Log Store The local log store of LogDevice is called LogsDB. LogsDB is a write-optimized datastore which has been designed to keep the number of disks seeks small and controlled. Also, the write and read IO patterns on the storage device are mostly sequential. The write-optimized data stores offer great performance when writing data, even if it belongs to multiple files or logs. Apart from that, LogsDB is quite efficient for log tailing workloads, which is a common pattern of log access where records are delivered to readers soon after they are written. These records are never read again except in rare cases such as massive backfills. LogsDB is built on top of RocksDB, which is an ordered durable key-value data store based on LSM trees. LogsDB act as a time-ordered collection of RocksDB column families. Each RocksDB instance is called a LogsDB partition. For more information, visit the official LogDevice website. Facebook researchers build a persona-based dialog dataset with 5M personas to train end-to-end dialogue systems Facebook Dating app to release as a test version in Colombia ACLU sues Facebook for enabling sex and age discrimination through targeted ads

Alibaba Group now enters the semiconductor industry by launching its new subsidiary named ‘Ping-Tou-Ge’ that will develop computer chips specifically designed for artificial intelligence. The company made this announcement last week at its Computing Conference in Hangzhou. Why the name, ‘Ping-Tou-Ge’? “Ping-Tou-Ge” is a Mandarin nickname for the Honey Badger, an animal native to Africa, Southwest Asia, and the Indian subcontinent. Alibaba chief technology officer Jeff Zhang says, “Many people know that the honey badger is a legendary animal: it’s not afraid of anything and has skillful hunting techniques and great intelligence”. He further added, “Alibaba’s semiconductor company is new; we’re just starting out. And so we hope to learn from the spirit [of the honey badger]. A chip is small [like the honey badger], and we hope that such a small thing will produce great power.” Ping-Tou-Ge is one of Alibaba’s efforts to improve China's semiconductor industry The main reason for the creation of ‘Ping-Tou-Ge’ was the US ban on Chinese telecom giant ZTE, which brought a realization as to how much China's semiconductor industry depends heavily on imported chipsets. Alibaba has constantly been increasing its footprint in the chip industry. DAMO Academy, which was established in 2017, focuses on areas such as machine intelligence and data computing. Alibaba had also acquired Chinese chipmaker Hangzhou C-SKY Microsystems in April to enhance its own chip production capacity. C-SKY Microsystems is a designer of a domestically developed embedded chipset. Zhang Jianfeng, head of Alibaba's DAMO Academy said in a statement that the Hangzhou-based company will produce its first neural network chip in the second half of next year with an internally developed technology platform and a synergized ecosystem. Ping-Tou-Ge will combine the powers of both DAMO’s chip business and C-Sky Microsystems. It will operate independently in the development of its embedded chip series CK902 and its neural network chip Ali-NPU. The Ali-NPU chip is designed for AI inferencing in the field of image processing, machine learning, etc. Some of its features include: It is expected to be around 40 times more cost-effective than conventional chips perform 10 times better than mainstream CPU and GPU architecture AI chips in the current market cut down power and manufacturing costs to half Pingtouge will also focus on customized AI chips and embedded processors to support Alibaba's developing cloud and Internet of Things (IoT) business. These chips could be used in various industries such as vehicles, home appliances, and manufacturing. To know more about Ping-Tou-Ge in detail, visit MIT Technology Review blog. OpenSky is now a part of the Alibaba family Alibaba Cloud partners with SAP to provide a versatile, one-stop cloud computing environment Why Alibaba cloud could be the dark horse in the public cloud race