Java Support for Azure Functions in Public Preview, More in Cloud Services

The new Azure Functions Core Tools will support you to run and debug your Java Functions code locally on any platform.

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At the JavaOne conference in San Francisco, Java support for Azure Functions, announced Wednesday, a part of Microsoft initiative that enabled a broadened support for different programming languages to be run on its runtime.

The new Java runtime in public preview will share all differentiated features provided by Azure Functions, such as wide range of triggering options and data bindings, as well as serverless execution model with auto-scale and pay-per-execution pricing, writes the team.

Serverless provides an effective model for accelerating app development.

Get started to create Java function and deploy it to Azure using Maven and Jenkins with Azure Functions Java tutorial.

Additionaly, in the last few weeks, Microsoft delivered a range of exciting new features in Maven, Jenkins, Visual Studio Code and IntelliJ.

These features help Java developers rapidly adopt cloud-native patterns in Azure and debug faster, as well as added support for Managed Disks, Cosmos DB and Container Service in the Azure Management Libraries for Java.

Microsoft is also collaborating with Red Hat, Pivotal, CloudBees and Azul, to bring Java closer to the cloud.

Azure CLI 2.0 preview now helping manage Windows and Linux Azure VM backups by using Azure Backup.

The CLI support bring features such as smart defaults for most common operations, tab completion and pipe-able outputs to simply and quickly manage VM backup and recovery operations.

Here is how CLI will help:

  • Cross-platform support: With Azure CLI, start managing Azure VM backups from anywhere, irrespective of the platform you use.
  • Managing at scale: Easily handle operations at scale by including CLI commands in scripts and programs.
  • Automation: In conjunction with command line tools, query on command outputs to identify important triggers and program subsequent actions. For example, backup health information can be programmatically retrieved, and subsequent operations can be initiated based on the health status.

Azure Backup is supported with Azure CLI 2.0 only and is currently in preview.

With SQL Server 2017 now generally available on Linux and Windows, Microsoft announcing new Azure VM images on Azure Marketplace.

Deploying SQL Server in Azure VMs combines the industry-leading performance and security, built-in artificial intelligence, and business intelligence of SQL Server, now available on both Linux and Windows, with the flexibility, security, and hybrid connectivity of Azure.

Azure VM images: SQL Server 2017 on Linux and Windows

Azure SQL Data Sync, which enables customers to easily synchronize data either bidirectionally or unidirectionally between multiple Azure SQL databases and/or on-premises SQL Databases.

Previously, manually to pull the log and detect errors and warnings, users had to look at SQL Azure Data Sync in Azure portal or use PowerShell/RestAPI's, now using OMS Log Analytics, a custom solution which will greatly improve Data Sync monitoring experience, users will be able to monitor all Sync Groups from any of their subscriptions in one place using a custom OMS view.

Get start with the configuration, follow these steps.

Kentico Cloud, a cloud-first Headless CMS for digital agencies and end clients — has made its way to Azure Marketplace with its sample site.

With this solution, Azure App Service web application can read content from Kentico Cloud, that stores content, tracks visitors, provides statistics and allows personalization of the content for various customer segments. So, you can distribute content to any channel and device, such as websites, mobile devices, mixed reality devices, presentation kiosks, etc, through an API.

Highlights of using Kentico Cloud:

  • Content is served via REST, backed by a super-fast CDN. This means that the app or website can be developed using any programming language on any platform.
  • SDKs for multiple programming languages are provided as open source projects developed by Kentico in collaboration with a developer community.
  • Built-in visitor-tracking feature tracks individual visitors. It allows you to analyze the data to identify customer segments with similar profiles or behavior.
  • Based on the gathered data, Kentico Cloud allows you to deliver personalized content and interactions with customers.

The general availability of Azure Application Insights SDK for Node.js 1.0, primarily brings performance, reliability and stability improvements to the Application Insights SDK for Node.js.

Application Insights is an Application Performance Management tool that monitors apps, services and components in production, after deployed. So, users can discover and rapidly diagnose performance bottlenecks and other issues.

A beta release of a open source python CLI tool called Azure Distributed Data Engineering Toolkit, allows provisioning on-demand Spark clusters and submit Spark jobs directly from your CLI.

Azure Distributed Data Engineering Toolkit is designed to extend native Spark experience, allowing you to provision clusters, and giving you full end-to-end experience of running Spark at scale, writes azure team.

Furthermore, this toolkit inherits Azure Batch's fast provision time, taking only 3-5 minutes to provision Spark cluster. With a Spark native experience and fast spin-up time, this toolkit allows you to easily run your Spark experiments, enabling you to do more, easily and in less time.

Microsoft introducing Deep Learning Virtual Machine on Azure, is a pre-configured environment with all tools needed for data science and AI development pre-installed.

The Deep Learning VM is designed specifically for GPU-enabled instances, and comes with a complete suite of deep learning frameworks including Tensorflow, PyTorch, MXNet, Caffe2 and CNTK.

It also comes witth example scripts and data sets to get you started on deep learning and AI problems, including:

  • Jupyter notebooks to compare performance and accuract of deep learning frameworks;
  • A how-to guide for building an object recognition system using deep learning;
  • Data and scripts to build a LSTM-based hierarchical attention network to classify Amazon reviews;
  • Unstructured text analytics with Biomedical entity extraction.

Deep Learning Virtual Machine on Azure Diagram

The DLVM along with all the DSVMs also provides a complete suite of data science tools including R, Python, Spark, and much more.

Today, Microsoft expands its portfolio of Power BI solutions on Azure with addition of Tableau Server in Azure Government Marketplace, and can be deploy in just a few clicks within the strict, exclusive boundaries of Azure Government.

Tableau Server enables you to see, understand, and share the data you have in Microsoft SQL Server, Azure SQL Database, Azure Data Warehouse and Azure HDInsight.

CSV format support for Azure Consumption and Charge Usage Detail API now in public preview brings in two mechanisms for pulling CSV formatted data: Synchronous i.e. non-polling, and Asynchronous i.e. polling based.

In addition, a Power BI Content Pack and Power BI Connector available for Enterprise customers to perform detailed analysis on their Azure usage and spend details.

Support for email subscriptions on dashboards and reports distributed through Power BI apps announced enable end users to install a Power BI app, and then subscribe themselves to email updates on any dashboard or report page contained within the app.

To get started, simply go to any dashboard or report that was distributed through an app, and looking for subscribe icon in the top right. Your end users will then be notified of changes to their critical data, right as it happens, multiplying the impact of your Power BI solutions.

Going forward, Power BI will leverage power of cutting-edge Artificial Intelligence (AI) to suggest interesting and relevant content to end users.

To see it in action, go to apps tab in Power BI service left nav. From there, select the option to "get more apps from Microsoft AppSource."

Power BI Embedded is generally available now offering independent software vendors (ISVs) and developers a way to quickly add stunning visuals, reports, and dashboards into their apps – through a capacity-based, hourly-metered model.

Power BI Embedded brings the strength and capabilities of Power BI into the hands of ISVs

  • Author and create visuals with Power BI Desktop (free download)
  • Connect to hundreds of data sources to expose insights to your customers
  • Adopt our visuals, community created options, or dream up your own
  • Deploy across all device types with consistent rendering in HTML5
  • Publish your visuals to your app with fully documented APIs and SDKs

SQL Server 2017, the first ever release available on Linux, in addition to Windows and Docker, means users can run the SQL Server on virtual machines based on Windows, Red Hat Enterprise Linux, SUSE Enterprise Linux Server, or Ubuntu.

SQL Server 2017 also provides new capabilities, including graph data management and analysis, Adaptive Query Processing, and advanced intelligence built-in with Python and R analytics plus native scoring in t-SQL.

Here is an highlight of some of new features:

  • Cross-platform support – you can run SQL Server 2017 on Windows, Linux, and Docker containers.
  • Built-in intelligence – SQL Server supports Python and native scoring using PREDICT function.
  • T-SQL Language enhancements – There are several enhancements in T-SQL language such as graph processing, new string functions (TRIM, TRANSLATE, CONCAT_WS), and aggregate (STRING_AGG) new Japanese collations that are unique in database market.
  • Intelligent query processing – Automatic tuning continuously monitors query performance and forces previous plans if some performance regression is detected. Execution of Multi-statement Table value functions is faster thanks to the interleaved execution. Batch mode processing now includes adaptive join operators and leverages memory grant feedback information to execute plans more efficiently.
  • Column-store enhancements – Clustered columnstore indexes support LOB types and enable you to compress your NVARCHAR(MAX), VARCHAR(MAX) and VARBINARY(MAX) columns up to 25x ratio, non-persisted computed columns enable you to put calculated expressions directly in table definitions, NONCLUSTERED COLUMNSTORE indexes support online build/rebuild.
  • In-memory OLTP enhancements – Memory optimized tables now support computed columns, naively compiled modules support new string and JSON functions, CASE statement, and WITH TIES.
  • Native import enhancements – BULK INSERT and OPENROWSET enable you to and to load files from Azure Blob Storage. Also, import functions are able to parse real CSV files and correctly process quoted characters.
  • Better monitoring – Query Store now provides you information about wait stats and a lot of new DMVs are added.

Also, SQL Server Integration Services on Linux is available now, so that you can perform data integration just like on Windows. SQL Server 2017 supports Red Hat Enterprise Linux, SUSE Linux Enterprise Server, and Ubuntu.

SQL Server 2017 Reporting Services now generally available is built on the success of SSRS 2016, deliver several enhancements, from a lightweight installer, to a modern REST API, to an updated Report Viewer control and web part, and more.

Public Preview for SQL Server Management Packs Update (6.7.34.0) comes included with Microsoft System Center Management Packs for SQL Server 2008/2008 R2/2012/2014/2016 (6.7.34.0).

New SQL Server 2008-2012 MP Features and Fixes

  • Reimplemented Always On workflows to enable monitoring of Availability Groups hosting over 200 databases
  • Updated alert descriptions of Availability Group monitors: added cluster name and primary replica name
  • Implemented 3 alerting rules for events #5105 (error with physical file access), #833 (IO request has taken longer than 15 seconds), and #41144 (AO availability group failed); they are disabled by default
  • Added debug information to Always On monitoring scripts
  • Disabled the alerting rule for event #18456 by default
  • Fixed issue: Invalid encoding of SQL names in Always On console tasks

Create Powerpoint presentations from R with the OfficeR package that allow you to take a Word or PowerPoint template and programmatically insert text, tables and charts generated by R into the template to create a complete document.

The OfficeR package also represents a leap forward from the similar ReporteRs package: it's faster, and no longer has a dependency on a Java installation.

Here is how to create a PowerPoint deck using R:

  • Create a template PowerPoint presentation to host the slides. You can use the Slide Master mode in PowerPoint to customize the style of the slides you will create using R, and you can use all of PowerPoint's features to control layout, select fonts and colors, and include images like logos.
  • For each slide you wish to create, you can either reference a template slide included in your base presentation, or add a new slide based on one of your custom layouts.
  • For each slide, you will reference one or more placeholders (regions where content goes) in the layout using their unique names. You can then use R functions to fill them with text, hyperlinks, tables or images. The formatting of each will be controlled by the formatting you specified within PowerPoint.