Kategorie: Microsoft Azure

Bring Interactive Analytics to Azure HDInsight: Kyligence Analytics Platform enables sub-second query

In resource-intensive systems, queries will compete for runtime resources and it takes hours to return when the work load is high. SQL on Hadoop is improving continuously, but it is still common to wait minutes or even a couple hours for one single query to return, especially when the dataset is huge. Most of these systems are resource-intensive where queries compete for runtime resources and performance declines when the workload is high.

To solve this problem, Kyligence Analytics Platform (KAP) enables interactive analytics with sub-second query latency on massive dataset. KAP is a leading big data intelligence platform powered by Apache Kylin. It enables interactive analytics with sub-second query latency, even on massive data-set, and is widely adopted by enterprises such as Lenovo, China Mobile, and many more. We are happy to announce that the Kyligence team and Azure HDInsight team have worked closely with each other to bring OLAP capabilities to HDInsight, and KAP is now available on Azure HDInsight as an HDInsight application.

HDInsight Application Platform

Azure HDInsight is the only fully-managed cloud Hadoop offering that provides optimized open source analytical clusters for Spark, Hive, MapReduce, HBase, Storm, Kafka, and R Server backed by a 99.9% SLA. Each of these big data technologies and ISV applications are easily deployable as managed clusters with enterprise-level security and monitoring.

The open source ecosystem of applications has grown with the goal of making it easier for customers to build their big data and analytical solutions. Today, customers find it challenging to discover these productivity applications, and struggle to install and configure the apps. To address this gap, HDInsight Application Platform provides a unique experience to Microsoft where ISV’s can directly offer their applications to customers, and customers can easily discover, install, and use ISV applications built for the big data ecosystem.

As part of this integration, KAP can be easily deployed by one-click on HDInsight.

Interactive Analytics with Trillions of Data on HDInsight

Hadoop is designed for large scale data processing, but is not efficient enough for interactive analytics. KAP provides interactive analytics ability on HDInsight by providing the following integration with HDInsight:

Native SQL support on Hadoop and HDInsight: Many existing big data analytics technologies have their own query language or proprietary storage engine optimized for analytics scenarios. It is difficult for analysts to learn a new query language or move data out of HDFS/BLOB storage to other platforms. With KAP's native SQL support and ODBC drivers, customers can use the standard SQL interface and choose their favorite BI tools on their large amount of data.
Sub-second query response: The query performance is the bottleneck for most big data use cases. The performance will decline if the cluster resource cannot scale out when the original data grows 10x. To make the sub-second query response consistent is the key for interactive analytics and KAP on HDInsight solves this problem by providing pre-calculated Cubes.
Elastic architecture: The dataset normally ranges from gigabytes, terabytes, and more. Hadoop provides the elastic infrastructure for batch processing, and KAP as an interactive analytics technology, also leverages the elastic capability of Hadoop to enable the scale-out solutions.
Native Integration with HDInsight: Cloud is an effortless way to adopt new technology without worrying about deployment or monitoring. With KAP + HDInsight as a full-managed cloud solution, it can help users reduce operation cost as well as achieve high availability. KAP can work with all the supported Azure storage services (Azure BLOB storage and Azure Data Lake Store), and can also work with HDInsight Kafka clusters to ingest data from Kafka.

KAP – Enterprise-ready data warehouse powered by Apache Kylin

KAP, an enterprise OLAP on Hadoop powered by Apache Kylin, enables sub-second SQL query latency on petabyte scale dataset, provides high concurrency at internet scale, and empowers analysts to architect BI on Hadoop with industry-standard data warehouse and business intelligence methodology. KAP is a unified analytics platform simplified Big Data Analytics for business users, analysts, and engineers with self-service, seamless integrated with BI tools and no programming required. KAP is a native on Hadoop OLAP solution which interacts with cluster only via standard APIs and supports main Hadoop distributions from on-prem environment to in the Cloud.

On Azure, most data are stored in Azure BLOB storage or Azure Data Lake Store, and then are loaded into Hive as external tables. KAP builds the cube (index) by using MapReduce/Spark according to the data model designed by the modeler before analysis. During query runtime, all queries can access the pre-aggregated cube data and the result will be returned in sub-second. By leveraging the unique pre-calculation technology, KAP provides consistent query latency regardless of how much data grows, even with limited resources. KAP also provides native integration with various Azure storage services, such as Azure BLOB storage and Azure Data Lake Store. It can also connect with HDInsight Kafka clusters to ingest data from Kafka.

The screenshot below shows the KAP modeling GUI:

 

Compared to Hive query, KAP is 100x faster without modifying the queries into HiveQL dialect. ANSI SQL and JDBC/ODBC drivers are also supported, so users can choose their familiar BI tools to do interactive analytics, for example PowerBI or Tableau. Below is the performance comparison between Apache Kylin and Apache Hive on SSB dataset:

Installing KAP on Azure HDInsight

With the KAP on Azure HDInsight solution, user can install KAP on their exiting HDInsight cluster or standalone optimized cluster designed for KAP with a single click. Currently, KAP works as an application on HDInsight HBase cluster.

After the one-click installation, you will get the following components:

KAP: The enterprise version of Apache Kylin, which provides the core OLAP analysis on HDInsight by building pre-calculated cubes.
KyAnalyzer: The built-in OLAP agile BI tool for quick BI analysis by connecting to KAP.

KAP will be installed on the Edge Node in the HBase cluster. To learn more details on how to use KAP on HDInsight, please check the Kyligence blog post.

Summary

KAP on Azure HDInsight brings quick insight into massive dataset in sub-second latency and empowers interactive analytics on Hadoop for trillion level records. It offers web-scale OLAP solutions for various industries to build their online and offline analytics platforms. With the cloud based technologies, computing resources can extend and shrink when processing burst data, with a more efficient deployment model, thus helping customers reduce cost and improve productivity.

For more resources to get started, please check the "more resources" section below. If you have any feedbacks or questions, feel free to drop us an email at hdiask@microsoft.com. We love to hear from you!

More resources

Getting Started to use KAP on HDInsight (Kyligence Blog or MSDN blog)
Video Tutorial for KAP on HDInsight
KAP Documentation
Learn more about Azure HDInsight
Ask HDInsight questions on stackoverflow
Learn more about Apache Kylin
Learn more about Kyligence Analytics Platform

Quelle: Azure

Root cause analysis and time exploration updates to Azure Time Series Insights

Azure Time Series Insights is currently in public preview, and we’ve been hard at work the last couple months to help our customers better manage and find value in their time series data.  Time Series Insights is a fully managed analytics, storage, and visualization service that makes it simple to explore and analyze billions of IoT events simultaneously. Additionally, it allows you to visualize and explore time series data streaming into Azure in minutes, all without having to write a single line of code. For more information about the product, pricing, and getting started, please visit the Time Series Insights website on Azure.com.

Faster root cause analysis and investigations

We’ve heard a lot of feedback from our manufacturing, and oil and gas customers that they are using Time Series Insights to help them conduct root cause analysis and investigations, but it’s been difficult for them to quickly pinpoint statistically significant patterns in their data. To make this process more efficient, we’ve added a feature that proactively surfaces the most statistically significant patterns in a selected data region. This relieves users from having to look at thousands of events to understand what patterns most warrant their time and energy. Further, we have made it easy to then jump directly into these statistically significant patterns to continue conducting an analysis.

This new feature is also helpful for post-mortem investigations into historical data. Most of our customers have existing alerting mechanisms in place (for example, Azure Stream Analytics jobs) and use Time Series Insights as a complementary investigative tool to understand the context of an alert. These customers are using Time Series Insights to look back during a postmortem for additional clues to help mitigate and prevent similar issues from occurring in the future.

Below is a GIF showing patterns in the stats tab and adding a pattern as a new term:

Greater control of time for data exploration

Additionally, we have heard from customers across many verticals that they are using Time Series Insights to help them triage and diagnose issues involving sensor data from their key assets, but they have been asking for finer control over their ability to navigate time in our visualizations. To give these customers more control, we have provided several new usability improvements to time navigation to make triage and diagnosing easier.

First, we’ve added a time interval slider for more precise control of movement between large slices of time that show smooth trends down to slices as small as the millisecond, allowing customers to see granular, high-resolution cuts of their data. Further, we’ve set the slider’s default starting point to be the most optimal view of the data from their selection; balancing resolution, query speed, and granularity.

Below is a GIF showing the slider in action:

Secondly, we heard from customers that they would like an easier way to move between time ranges when conducting diagnostics on their sensor data. Previously, a user needed to leave their search and reselect the period they wanted to explore from their environment all over again to complete this task. To make their workflow more seamless, we have added a time brush to make it easier to navigate from one time span to another, putting intuitive UX front and center for easy movement between time ranges.

Below is a GIF showing how simple it is to navigate using the brush:

We are excited about these new updates, but we are even more excited about what’s to come, so be on the lookout for more product news soon! You can also explore Time Series Insights and take these new updates for a test drive using our free demo environment, you’ll just need an Azure.com account to get started. You can also stay up to date on all things Time Series Insights by following us on Twitter.
Quelle: Azure

August updates to the Azure Analysis Services web designer

Last month we released a preview of the Azure Analysis Services web designer. This new browser-based experience will allow developers to start creating and managing Azure Analysis Services (AAS) semantic models quickly and easily. While SQL Server Data Tools (SSDT) and SQL Server Management Studio (SSMS) are still the primary tools for development, this new experience is intended to make simple changes fast and easy. It is great for getting started on a new model or to do things such as adding a new measure to a development or production AAS model.

Today we are announcing the first set of updates which include a mix of fixes and new features. In the upcoming months, we will continue to evolve the web designer to allow for easier and more advanced model creation in the web. New functionality includes:

DAX syntax highlighting for measures

Adding measures is a bit simpler with the use of a multiline code editor which recognizes DAX formula syntax.

New mini map in JSON editor

The model JSON editor now includes a mini document map on the right hand side to make browsing the JSON document simpler.

Display folder and hierarchy support in the query designer

You can now use hierarchies and display folders when graphically designing queries.

Table relationship editor

Create new relationships or edit existing ones between table with the new relationship editor dialog.

Copy server name

When needing to connect to your server from other tools such as SSMS or SSDT, you can now simply copy your full server name from the server blade.

You can try the Azure Analysis web designer today by linking to it from a server in the Azure portal.

Submit your own ideas for features on our feedback forum. Learn more about Azure Analysis Services and the Azure Analysis Services web designer.
Quelle: Azure

Introducing the #Azure #CosmosDB Change Feed Processor Library

Azure Cosmos DB is a fast and flexible globally-replicated database service that is used for storing high-volume transactional and operational data with predictable millisecond latency for reads and writes. To help you build powerful applications on top of Cosmos DB, we built change feed support, which provides a sorted list of documents within a collection in the order in which they were modified. Now, to address scalability while preserving simplicity of use, we introduce the Cosmos DB Change Feed Processor Library. In this blog, we look at when and how you should use Change Feed Processor Library.

Change feed: Event Sourcing with Cosmos DB

Storing your data is just the beginning of the adventure. With change feed support, you can integrate with many different services depending on what you need to do once changes appear.

Example #1: You are building an online shopping website and need to trigger an email notification once a customer completes a purchase. Whether you prefer to use Azure Functions, Azure Notification Hub, Azure App Services, or your custom-built micro services, change feed allows seamless integration by surfacing changes in the order that they occur.

Example #2: You are storing data from an autonomous vehicle and need to detect abnormalities in incoming sensor data. As new entries are stored in Cosmos DB, these changes that appear on the change feed can be directly processed by Azure stream analytics, Azure HDInsight, Apache Spark, or Apache Storm. With change feed support, you can apply intelligent processing in real-time while data is stored into Cosmos DB.

Example #3: Due to architecture changes, you need to change the partition key for your Cosmos DB collection. Change feed allows you to move your data to a new collection while processing incoming changes. The result is zero down time while you move data from anywhere to Cosmos DB.
 

What about working with larger data storage with multiple partitions?

As your data storage needs grow, it’s likely that you will use multiple partitions to store your data. Although it’s possible to manually read changes from each partition, the Change Feed Processor makes it easier by abstracting the change feed API. This function facilitates the reading across partitions and distributes change feed event processing across multiple consumers. This library provides a thread-safe, multi-process, safe runtime environment with checkpoint and partition lease management for change feed operations. The Change Feed Processor Library is available as a NuGet package for .NET development.

When to use Change Feed Processor Library:

Pulling updates from the change feed when data is stored across multiple partitions
Moving or replicating data from one collection to another
Parallel execution of actions triggered by updates to data and the change feed

Getting started with the Change Feed Processor Library is simple and lightweight. In the following example, we have a collection of documents containing news events associated with different cities. We use “city” as the partition key. In just a few steps, we can print out all changes made to any document from any partition.

To set this up, install the Change Feed Processor Library Nuget package and create a lease collection. The lease collection should be created through an account close to the write region. This collection will keep track of change feed reading progress per partition and host information.
 

To define the logic performed when new changes surface, edit the ProcessChangesAsync function. Here, we are simply printing out the document ID of the new or updated document. You can also modify this function to perform different tasks.

 

public Task ProcessChangesAsync(ChangeFeedObserverContext context, IReadOnlyList<Document> docs)
{
Console.WriteLine("Change feed: total {0} doc(s)", Interlocked.Add(ref totalDocs, docs.Count));
foreach (Document doc in docs)
{
Console.WriteLine(doc.Id.ToString());
}

return Task.CompletedTask;
}

 

Next, to begin the Change Feed Processor, instantiate ChangeFeedProcessorHost, providing the appropriate parameters for your Azure Cosmos DB collections. Then, call RegisterObserverAsync to register your IChangeFeedObserver (DocumentFeedObserver in this example) implementation with the runtime. At this point, the host attempts to acquire a lease on every partition key range in the Azure Cosmos DB collection using a "greedy" algorithm. These leases last for a given timeframe and must then be renewed. As new nodes come online, in this case worker instances, they place lease reservations. Over time the load shifts between nodes as each host attempts to acquire more leases.

 

DocumentFeedObserver docObserver = new DocumentFeedObserver();

ChangeFeedEventHost host = new ChangeFeedEventHost(hostName, documentCollectionLocation, leaseCollectionLocation, feedOptions, feedHostOptions);

await host.RegisterObserverFactoryAsync(docObserverFactory);

 

Next steps

Review the documentation: Working with the Change Feed support in Azure CosmosDB.
Try out sample code: An example to read and copy changes to new collection.
Download the NuGet Package to get started.

Stay up-to-date on the latest Azure Cosmos DB news and features by following us on Twitter @AzureCosmosDB and #CosmosDB, and reach out to us on the developer forums on Stack Overflow.
Quelle: Azure

First Hyperscale CSP with Graphic-Intensive Supportable VMs in UK: Microsoft Azure’s G/GS/LS/H/N-series now available in UK South  

Today Microsoft Azure Virtual Machine customers can take advantage of the Azure G/GS/LS/H/N-series of Virtual Machine sizes, available in UK South. We’re also excited to announce that Microsoft Azure is the first Hyperscale Cloud Provider offering VMs able to run graphic intensive workloads in the UK (see N series below)!

New Azure N series – The NC and NV sizes are also known as GPU-enabled instances. These are specialized virtual machines that include NVIDIA®'s GPU cards, optimized for different scenarios and use cases. The NV sizes are optimized and designed for remote visualization, streaming, gaming, encoding, and VDI scenarios utilizing frameworks such as OpenGL and DirectX. The NC sizes are more optimized for compute-intensive and network-intensive applications and algorithms, including CUDA- and OpenCL-based applications and simulations.

Learn more about N-Series.

New Azure G/GS/LS series –  G/GS series are ideal for applications that demand faster CPUs, better local disk performance, or have higher memory demands. They offer a powerful combination for many enterprise-grade applications. The LS-series is optimized for workloads that require low latency local storage, like NoSQL databases (for example, Cassandra, MongoDB, Cloudera, and Redis).

Learn more about G/LS-Series.

New Azure H series –  H-series VMs are an excellent fit for compute-intensive workloads and provide cutting-edge performance, as well as an RDMA back-end network for MPI workloads.

Learn more about H-Series.

For more information, please visit the Virtual Machines page and the Virtual Machines pricing page.
Quelle: Azure

Mesosphere DCOS, Azure, Docker, VMware & Everything Between – Deploying DC/OS with Azure Container Service

This post is part of the “Mesosphere DC/OS, Azure, Docker, VMware & Everything Between” multiple blog post series. In the previous posts for this series, I looked at the following topics:

Mesosphere DCOS, Azure, Docker, VMware and everything between – Architecture and CI/CD Flow

Mesosphere DCOS, Azure, Docker, VMware and everything between – Security & Docker Engine Installation

Mesosphere DCOS, Azure, Docker, VMware & Everything Between – SSH Authorized Keys

Mesosphere DCOS, Azure, Docker, VMware & Everything Between – Deploying DC/OS on VMware vSphere

Mesosphere DCOS, Azure, Docker, VMware & Everything Between – Deploying DC/OS with Azure Container Service

We have two working DC/OS clusters, one on Azure and another on vSphere – Great progress so far! Now, it’s time deploy Azure Container Registry (ACR) which will be used as a private catalog for our Docker images.

This is going to be a VERY short post as deploying ACR takes no more than 5min tops as the process is super straightforward.

Azure Container Registry Deployment

So, let’s get to work and look for ACR in Azure Marketplace. Start the deployment wizard.

Read more about all the details around DC/OS 1.9 deployment on top of VMware vSphere on my personal blog.
Quelle: Azure

Migrating a Web App from ClearDB to Azure Database for MySQL

With the introduction of Azure Database for MySQL, I’ve seen a lot of interest and questions from customers on how they can move their existing Web App from using ClearDB as their MySQL database provider over to Azure Database for MySQL. If you’re not using ClearDB, but rather MySQL In-App as your provider, and want to move over to Azure Database for MySQL, a great blog has already been written on this that you should check out. For this blog, I’ll be migrating my WordPress website’s database from ClearDB to Azure Database for MySQL, as well as updating my Web App to point to the new database server.

Preparing for the migration

I’ll be using MySQL Workbench as the tool to do the data migration. Of course you can use other common tools or CLI as well. First, download MySQL Workbench. Once you’ve downloaded and installed MySQL Workbench, you will need to create a connection to your ClearDB database in order to kick-off the migration. To create the connection, you’ll need some information about your ClearDB database.

In the Azure Portal, open up your ClearDB database and click on the Properties task on the left navigation pane. Keep this open as you’ll need this for the next step in creating your MySQL Workbench connection.

Now open MySQL Workbench and create a new connection by clicking on the + icon at the top of the home screen. In the Setup New Connection screen, give your connection a name (this can be anything – I choose “My ClearDB Database”), and then switch back over to your browser where you have your ClearDB database properties open and copy the HOSTNAME, USERNAME, and PASSWORD into the respective fields of MySQL Workbench. Note that to enter the password in MySQL Workbench, you’ll need to click on the Store in Vault… button first.

Once this is done, save your connection and create another new connection for your Azure Database for MySQL. If you haven’t created an Azure Database for MySQL yet, refer to our documentation Quickstart on how to do so. Similar to ClearDB, you’ll need to get the hostname, username, and password of your Azure Database for MySQL through the portal.  But before we do that, we’ll need to configure control access to your Azure Database for MySQL server.

Open your Azure Database for MySQL Server in the Azure Portal and click on the Connection Security setting on the left navigation pane.  For simplicity in the migration process, I have disabled SSL connectivity by clicking on the Enforce SSL connection toggle switch to Disabled. With regards to firewall rules, you can either click on the + Add My IP icon at the top of the screen which will add your local IP address to the firewall, or in my case I’ve created a firewall rule that allows all IP addresses access to my server for the time being. Later, when I configure my Web App to connect to my database server, I’ll add the appropriate IP addresses and remove this rule. For more information on configuring SSL connectivity for your server, check out our documentation.

Now that you have configured access to your Azure Database for MySQL server, you can continue to create a new MySQL Workbench connection just as you did for your ClearDB database server.  Open your Azure Database for MySQL server and in the main Essentials dashboard, you’ll see that the Server Name and Server admin login name are in the main page. Your password is not exposed here. If you don’t remember what your password is for your server, you can always reset it using the Reset Password option in the upper left corner of the Essentials pane. With this information, create another connection in MySQL Workbench for your Azure Database for MySQL server the same way you did for your ClearDB database.

Migrate your database

Open MySQL Workbench and click on Database and then Migration Wizard from the drop-down to start your database migration.

In the migration wizard you’ll be asked to select your source and target database servers. For your source, choose the ClearDB database server connection you created first, and for the destination choose the Azure Database for MySQL Server you just finished creating.

Continue through the wizard until you get to the screen that asks you which schemas you want to migrate. Make sure to only migrate the schema that is applicable to your application. In this case of a WordPress application, there is only one schema which is applicable. The name of your schema should be similar as ClearDB randomly generates the schema name.

Once you select your source schema to migrate, the remainder of your migration should be the defaults selected in the migration wizard. The time it takes to migrate your database should not be long, even for larger databases assuming your Azure Database for MySQL server is in the same region as your source ClearDB database.

Configuring your Azure Web App

Now that your database has been migrated, you’ll need to connect your Web App to your Azure Database for MySQL. You’ll need to both update your Web App as well as the firewall rules of your Azure Database for MySQL if you choose to restrict access to your database to your Web App exclusively. We’ll start with this, so open up your Web App in the Azure portal. On the left navigation pane, select Properties and note the Outbound IP Addresses. These will be the specific IP addresses that you will create firewall rules for in your Azure Database for MySQL.

Open up your Azure Database for MySQL server and create firewall rules for each IP address to allow access for your Web App to the server. This is the same process as described above for adding firewall rules.

Now go back to your Azure Web App and open up the Application settings on the left navigation pane, and then scroll down to the Connection strings section of the main pain. You will now need to modify your connection string to point to the new database server. You can simply click on the string value and edit it directly. You will need to replace all of the values except the Database value, as you migrated the database (schema) intact from ClearDB which preserves the database name. In my case, my original connection string was as follows:

Database=acsm_8cb9eb8d372ebbd;Data Source=us-cdbr-azure-west-b.cleardb.com;User Id=b8c2e429e67ac2;Password=47bd9069

After I updated it to point to my new Azure Database for MySQL Server, it looks like this:

Database=acsm_8cb9eb8d372ebbd;Data Source=jasonsnewserver.mysql.database.azure.com;User Id=jason@jasonsnewserver;Password=MyPassword12

Make sure to click the Save button at the top of the screen, and that’s all you need to do. You’re now using Azure Database for MySQL on your existing Web App. Congratulations!

Jason – JasonMA_MSFT
Quelle: Azure

Announcing public preview of Azure Batch Rendering

This week SIGGRAPH 2017 is blasting away in Los Angeles and I can’t imagine a better place than the premier event for computer graphics to announce that Azure Batch Rendering will now move into public preview.

The complexities of cinematic productions, associated workflows, and infrastructure have always intrigued me, and they are honestly one of the very best examples of the hands-on value that Azure provides. Abstracting away infrastructure considerations, deployment, and management rarely made more sense, while at the same time being able to scale beyond the physical boundaries of your on-premises environments.

Enabling artists, engineers, and designers to submit rendering jobs seamlessly via client applications such as Autodesk Maya, 3ds Max, or via our SDK, Azure Batch Rendering accelerates large scale rendering jobs to deliver results to our customers faster.

Back in May during the Microsoft Build conference, we announced the first limited preview of Batch Rendering, a milestone in integrating the high-end graphics user experience with the power of Azure. Since then, hundreds of curious and excited customers have been putting Batch Rendering through its paces and have provided invaluable feedback to us on the product – thank you!

While Azure Batch Rendering with Autodesk is moving to public preview, we are also excited to announce a limited preview of V-Ray in partnership with Chaos Group. With V-Ray being supported for Maya and 3ds Max, this is another great step forward in supporting a rich and vibrant ecosystem on Azure.

Azure will continue to work with Autodesk, Chaos Group, and other partners to enable customers to run their day to day rendering workloads seamlessly on Azure. Batch Rendering will provide tools, such as client plugins, offering a rich integrated experience allowing customers to submit jobs from within the applications with easy scaling, monitoring, and asset management. Additionally, the SDK, available in various languages, allows custom integration with customer’s existing environments.

In addition to the our Batch Rendering announcements, we are launching a preview of a cool new management application, Batch Labs! Batch Labs is a cross-platform desktop management tool which includes job submission capabilities as well as a rich management and monitoring experience, along with the ability to manage asset uploads and downloads. Batch Labs hosts a marketplace of supported applications which can be easily extended by customers for their own applications and custom workflows.

Lastly, I’d like to invite you to come and meet our team at SIGGRAPH 2017. We’re hosting sessions and will be at booth #923, showing off a bunch of cool demos with partners like Conductor, Avid, Vizua, JellyFish Pictures, and PipelineFX along with exciting Microsoft hardware like the HoloLens and new Surface Studio.

If you are in the Los Angeles area during the week, you’re more than welcome to use the promo code “MSFT2017” to register for a complimentary visitor pass to the expo floor of SIGGRAPH.

Thank you all for your support in hitting this important milestone for Azure Batch Rendering. We are looking forward to continue working with you on the further expansion of the product and welcome your continued feedback!

Get more information and documentation on using Azure Batch Rendering.
Quelle: Azure