Kategorie: Microsoft Azure

Azure #DocumentDB Service Level Agreements

Why enterprises trust us for their globally distributed applications.

Enterprise applications and massive scale applications need a data store that is globally distributed, offers limitless scale, geographical reach, and is fast and performant. Along with enterprise grade security and compliance, a major criterion is the level of service guarantees the database provides in terms of availability, performance, and durability. Azure DocumentDB is Microsoft’s globally distributed database service designed to enable you to build planet-scale applications, enabling you to elastically scale both throughput and storage across any number of geographical regions. The service offers guaranteed single-digit millisecond low latency at the 99th percentile, 99.99% high availability, predictable throughput, and multiple well-defined consistency models.

We recently updated our Service Level Agreements (SLA) to make them comprehensive to include latency, availability, throughput, and consistency. By virtue of its schema-agnostic and write-optimized database engine, DocumentDB, by default, is capable of automatically indexing all the data it ingests and serves across SQL, MongoDB, and JavaScript language-integrated queries in a scale-independent manner. As one of the foundational services of Azure, DocumentDB has been used virtually ubiquitously as a backend for first-party Microsoft services for many years. Since its general availability in 2015, DocumentDB is one of the fastest growing services on Azure.

Industry leading comprehensive SLA

Since its inception, Azure DocumentDB always offered the best SLA in the industry with 99.99% guarantees for availability. Now, we are the only cloud service offering a comprehensive SLA for:

Availability: The most classical SLA. Your system will be available for more than 99.99% of the time or you get refund.
Throughput: At a collection level, we guarantee the throughput for your database collection is always executed according to the maximum throughput you provisioned.
Latency: Since speed is important, we guarantee that 99% of your requests will have a latency below 10ms for document read or 15ms for document write operations.
Consistency: We ensure that we will honor the consistency guarantees in accordance with the consistency levels chosen for your requests.

While everyone is familiar with the notion of SLA on availability or uptime, providing financial guarantees on throughput, latency, and consistency is a first and industry leading initiative. This is not only difficult to implement but also hard to provide transparency to users. Thanks to the Azure portal, we provide full transparency on uptime, latency, throughput, and the number of requests and failures. In the rare case that we are unable to honor any of these SLA, we will provide credits from 10% to 25% of your monthly bill as a refund.

Availability SLA – 99.99%

The following equation shows the SLA formula for availability, given a month with 744 hours:

A failed request has the HTTP code 5xx or 408 (for document Read/Write/Query operations) as shown in the portal.

Throughput SLA – 99.99%

The following equation shows the SLA formula for throughput, given a month with 744 hours:

What defines "Throughput Failed Requests", are requests that are throttled by the DocumentDB collection resulting in an error code, but before consumed RUs have exceeded the provisioned RUs for a partition in the collection for a given second. To avoid being throttled due to a misuse, we highly recommend you to look into the best practice in partitioning and scaling DocumentDB.

Consistency SLA – 99.99%

"Consistency Level" is the setting for a particular read request that supports consistency guarantees. You can monitor the consistency SLA through Azure portal:

Note: In this screenshot SLA = Actual

The following table captures the guarantees associated with the Consistency Levels. Please note:

"K" is the number of versions of a given document for which the reads lag behind the writes.
"T" is a given time interval.

 

CONSISTENCY LEVEL
CONSISTENCY GUARANTEES

Strong
Strong

Session
Read Your Own Write

 
Monotonic Read

 
Consistent Prefix

CONSISTENCY LEVEL
CONSISTENCY GUARANTEES

Bounded Staleness
Read Your Own Write (Within Write Region)

 
Monotonic Read (Within a Region)

 
Consistent Prefix

 
Staleness Bound < K,T

Consistent Prefix
Consistent Prefix

Eventual
Eventual

If a month has 744 hours, the SLA formula for consistency is:

Latency SLA – P99

For a given application deployed within a local Azure Region, in a month, we sum the number of one-hour intervals during which Successful Requests submitted by an Application resulted in a P99 latency greater than or equal to 10ms for document read or 15ms for document write operations. We call these hours “Excessive Latency Hours.”

If Monthly P99 Latency Attainment % is below 99%, we consider it a violation of the SLA and we will refund you up to 25% of your monthly bill.

We hope that this short blog helped you understand the large coverage of our Enterprise SLAs.

Azure DocumentDB, home for Mission Critical Applications

Azure DocumentDB hosts a growing number of customer mission critical apps. Our customers come from diverse verticals such as banking and capital markets, professional services, discrete manufacturers, startups, and health solutions. However, they share a common characteristic, the need to scale out globally while not compromising on speed and availability. Thanks to one of the best architectures, Azure DocumentDB can deliver on these promises and at a very low cost.

Build your first globally distributed application

Our vision is to be the database for all modern applications. We want to enable developers to truly transform the world we are living in through the apps they are building, which is even more important than the individual features we are putting into DocumentDB. Developing applications is hard, developing distributed applications at planet scale that are fast, scalable, elastic, always available, and yet simple, is even harder. Yet it is a fundamental pre-requisite in reaching people globally in our modern world. We spend limitless hours talking to customers every day and adapting DocumentDB to make the experience truly stellar and fluid.

So what are the next steps you should take? Here are a few that come to mind:

First, understand the core concepts of Azure DocumentDB.
Download the emulator and start developing locally!
Build a web/mobile app easily (you have a lot of choices):

.Net web app
.Net web app for MongoDB API
Build a mobile application with Xamarin and DocumentDB
Node.js web app
Java web app
Python Flask web app

If you need any help or have questions or feedback, please reach out to us on the developer forums on Stack Overflow. Stay up-to-date on the latest DocumentDB news and features by following us on Twitter (@DocumentDB) and join our LinkedIn Group.
Quelle: Azure

Use Azure Media Services with PowerApps

You can now build PowerApps with media hosted on Azure Media Services. In this walkthrough, Contoso Corp. wants to build an online learning app for its employees with videos of their products and services.

Create a new Azure Media Services account, if you don’t have one already

From your Azure Media Services account, locate and publish your video assets from Settings > Assets.

Encode your videos. After the videos are published, copy the manifest URLs. Start the streaming endpoint of your service, if not already.

If you haven’t tried PowerApps, you can always sign-up for a trial using your work or school account. Download PowerApps Studio from the Windows Store. Alternatively, login to the PowerApps portal and choose New app.
Choose the Blank app > Tablet layout.

We want to build a gallery of all the available AMS videos and have the user pick a video to play. An Excel spreadsheet is a quick way to load the data to the app. Here is the Excel Table we will use with the links to the AMS video URLs:

From PowerApps, choose Content > Data sources. From the right panel, choose Add data source and Add static data to your app. Browse and load the Excel file.
From PowerApps, add a Horizontal Gallery control from Insert > Gallery > Custom gallery. Choose Add an item from Insert tab and add the Video control from Media.
Bind the Gallery to the Excel table by setting the Items property of the gallery to the name of the table.

Set the Media property of the first video control in the gallery to ThisItem.VideoURL. You should see the list of the AMS videos load in the gallery. Set the Disabled property for the video control to true.

Add a Video control from Insert > Media for the main video. Bind its Media property to Gallery1.Selected.VideoURL

You can also add text fields for the Title and description from the Excel file and show them in the app. The complete app is shown in the picture below:

You can learn more about building apps from the PowerApps documentation. For feedback and questions on AMS videos in PowerApps, please post them on our forums.

 

Happy app building!
Quelle: Azure

Infuse some AI into your Azure apps at hands-on Seattle workshop

If you’re an Azure developer interested to incorporate the very latest AI and machine learning techniques into your apps and enterprise solutions, here’s a free in-person workshop you’ll want to register for.

Microsoft is running an all-day AI Immersion Workshop on Tuesday, May 9th, at the W Hotel in Seattle. We’ll provide an overview of Microsoft’s extensive AI investments and offerings at this event, followed by deep technical tutorials, specifically designed for hands-on developers such as yourself. The tutorials being featured at this event include:

Applied Machine Learning for Developers.
Big AI – Applying Artificial Intelligence at Scale.
Weaving Cognitive and Azure Services to Provide Next-Generation Intelligence.
Deep Learning and the Microsoft Cognitive Toolkit.
Building Intelligent SaaS Applications.

Seasoned Microsoft engineers and data scientists are running the tutorials, and they will there to guide and help you along the way, as you complete your apps through the day.

Spots are filling up rapidly, so be sure to register now and reserve your spot:

For more details, including session abstracts and instructor names, be sure to check out our event agenda page here. See you in Seattle next month!

Quelle: Azure

Microsoft + Docker – Investing in the future of your applications

This post was authored by the Microsoft and Docker teams.

Did you know when you combine Docker’s cross platform support of Linux and Windows containers and Microsoft cloud technologies, you get a comprehensive offering that can support virtually every enterprise workload?

One platform, one journey for all applications

Microsoft and Docker aim to provide a modern platform for developers and IT pros to build, ship, and run any application on-premises, in the cloud, or through service providers across both Windows and Linux operating systems. Together we are bringing container applications across platforms, integrating across our developer tools, the operating system, and cloud infrastructure to provide a seamless experience across the application environment from development to test and production.

Whether you host your workloads in private datacenters, public cloud, or hybrid, Microsoft and Docker offer great end-to-end solutions or individual components from the developer’s keyboard to cloud. Azure Container Service provides the simplest way to deploy your container orchestration environment, such as Docker Swarm, so your app teams can deploy their apps more quickly. Windows Server Containers are powered by the same Docker toolchain, so you use the same Docker tooling to build and run those containers as you do your Linux containers and with the tooling you choose including Eclipse, Visual Studio, Jenkins, and Visual Studio Team Service. Windows Server Containers help secure and modernize existing enterprise .NET and line-of-business server applications with little or no code changes. Package existing apps in containers to realize the benefit of a more agile DevOps model, then deploy on-premises, to any cloud, or in a hybrid model. Reduce infrastructure and management costs for those applications, as well.

See it in action @ DockerCon 2017

Come visit Docker + Microsoft sessions @ DockerCon, taking place in Austin, TX from April 17th –20th. Learn how to modernize traditional applications as well as learn new technologies to help you build your next great application. You’ll learn about customer success stories on how they were able to achieve ROI targets and up to 80% cost savings through infrastructure consolidation and operational efficiencies with Docker Enterprise Edition (EE) and Azure.

Check out our sessions

Docker + Microsoft – Investing in the future of your applications on Tuesday, April 18th from 11:45am-12:25pm
Beyond – the path to Windows and Linux parity in Docker on Tuesday, April 18th from 2:00pm-2:40pm

There will also be hands-on labs for you to experience Docker on Windows. We’ll provision a Docker environment for you in Azure, and provide self-paced learning guides. You can learn more by reading Elton Stoneman’s blog on Docker + Microsoft sessions at DockerCon, from modernizing traditional apps like .NET to building new Windows Server Container apps.
Quelle: Azure

Using xEvents to monitor Azure Analysis Services

In addition to providing BI queries at the speed of thought and a user friendly BI semantic model, Azure Analysis Services supports many manageability features.  One such feature is a rich set of Extended Events (xEvents) which can be used for scenarios ranging from trouble shooting and diagnostics to in depth auditing and usage analysis.

You can use SQL Server Management Studio (SSMS) to configure a xEvents for Azure Analysis Services. Today, you can only configure Azure Analysis services to log to a stream or ring buffer and not to a file. In some cases, you may want to log events for offline analysis or to retain historically. We have provided an example of using the Tabular Object Modeling APIs to create an xEvents session and logging the data to disk and a richer sample to trace to a database with a windows service. The xEvents Logging for Azure Analysis Services sample and ASTrace samples are available on GitHub at https://github.com/Microsoft/Analysis-Services.

The easiest way to use this sample is to use SSMS to configure streaming xEvents to see which events you would like to log. First, create an xEvents session in SSMS. Then pick which events you like to record and set the data mode to streaming. Run some queries or do other operations, and then look at the xEvents in the “Watch Live Data” option on the trace session in SSMS to verify the data. If these events are the ones you want, then you can script these out to a file. 

The sample program takes the TMSL script file to define the events it will record. Then you can run the sample program to create a new session, and it will trace these events to a file.  Be sure to install the latest Azure Analysis Services client libraries to ensure you have support to integrated authentication. 

Let us know how it works for you or check out the other Azure Analysis Serivces samples on GitHub!
Quelle: Azure

Introducing Dataiku’s DSS on Microsoft Azure HDInsight to make data science easier

We are pleased to announce the expansion of HDInsight Application Platform to include Dataiku.

Azure HDInsight is the industry leading fully-managed cloud Apache Hadoop & Spark offering which allows customers to do reliable open source analytics with an industry-leading SLA. Dataiku develops Data Science Studio (DSS), a collaborative data science platform that enables companies to build and deliver their analytical solutions more efficiently.

This combined offering of DSS on HDInsight enables customers to easily use data science to build big data solutions and run them at enterprise grade and scale.

Microsoft Azure HDInsight – Reliable Open Source Analytics at Enterprise grade & scale

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

The ecosystem of applications in Big data has grown with the goal of making it easier for customers to solve their big data and analytical problems faster. Today, customers often find it challenging to discover these productivity applications and then in turn struggle to install and configure these apps.

To address this gap, the HDInsight Application Platform provides an experience unique to HDInsight where Independent Software Vendors (ISV’s) can directly offer their applications to customers – and customers can easily discover, install and use these applications built for the Big data ecosystem.
As part of this integration, Dataiku is bringing DSS to make collaborative data science much easier.

Dataiku Data Science Studio (DSS) – Prototype, deploy and run at scale

Dataiku provides Data Science Studio, the collaborative data science platform that enables professionals (data scientists, data engineers etc.) to collaborate on building analytical solutions. DSS has an easy to use team-based interface for data scientists and beginner analysts. A user can use DSS to implement a complete analytical solution – which could range from data ingestion (all data types, sizes, format etc.), data preparation, data processing, training and applying machine learning models, visualization and operationalizing the solution.

A user can use DSS to implement a complete analytical solution – which could range from data ingestion (all data types, sizes, format etc.), data preparation, data processing, training and applying machine learning models, visualization and operationalizing the solution.

DSS on HDInsight – Data science at enterprise grade & scale

A customer can install DSS on HDInsight using Hadoop or Spark clusters. They can install DSS on existing clusters which are running, or while creating new clusters. DSS 4.0 also added support for using Azure Blob Storage as a connector for reading data from.

When a user installs DSS on HDInsight, the user can make use of the benefits of Hadoop or Spark on HDInsight. Users can utilize DSS to build projects; the projects can generate MapReduce or Spark jobs, which makes DSS a great compliment to your HDInsight cluster. These jobs are executed as regular MapReduce or Spark jobs, and hence they get all the benefits of running these jobs on an enterprise grade platform. Since these jobs are running on HDInsight, customers can scale the cluster on demand, which allows a customer to run DSS at scale on HDInsight.

Getting started with DSS on HDInsight

Let us show a quick walkthrough of installing and getting started with DSS on HDInsight: The following screen shot shows a Spark cluster in the Microsoft Azure portal. A user can click the Applications tile to see the list of applications installed.

A user can select DSS, agree to the terms of agreement and install DSS. This is the simplicity associated with a one-click deployment experience. After the user has selected DSS, DSS is installed on the edge node, which is part of the cluster.

After DSS is installed, a customer can launch DSS using the “WEBPAGE” link. (This is the link to the DSS product.) A user must first authenticate with the cluster user credentials and then they can login with their DSS credentials

The following screenshot shows what a typical data science project’s landing page would look like in DSS. This shows both the summary of the project, as well as the timeline of the changes made to the project.

Resources

Following are some resource on learning more on this integration along with tutorials and videos.

Learn more about Azure HDInsight
Learn more about Dataiku DSS
Getting started with DSS on HDInsight
Use HDInsight and DSS to predict credit default
Dataiku and Microsoft HDInsight Integration
Video recording between Microsoft & Dataiku on using DSS on HDInsight
Install DSS on HDInsight from Azure marketplace
Ask HDInsight questions on stackoverflow
Dataiku Q&A

Summary

We are pleased to announce the expansion of HDInsight Application Platform to include Dataiku’s Data Science Studio. By deploying DSS on HDInsight, customers can easily build analytical solutions and run them at enterprise grade and scale.
Quelle: Azure