Kategorie: Microsoft Azure

Azure Marketplace Test Drive

Azure Marketplace provides a rich catalog of thousands of products and solutions from independent software vendors (ISVs) that have been certified and optimized to run on Azure. In addition to finding and deploying ISV products, customers often use Azure Marketplace as a learning tool to discover and evaluate products. One feature in Azure Marketplace that is especially useful for learning about products is “Test Drive.”  Test Drives are ready to go environments that allow you to experience a product for free without needing an Azure subscription. An additional benefit with a Test Drive is that it is pre-provisioned – you don’t have to download, set up or configure the product and can instead spend your time on evaluating the user experience, key features, and benefits of the product. To get started with a Test Drive, follow this 3-step process: Visit the Test Drive page on Azure Marketplace Choose a Test Drive, sign in and agree to the terms of use. Once you complete the form, your Test Drive will start deploying and in a few minutes you will get an email notification that the environment is ready. Just follow instructions in the email, and you will be able to access a fully provisioned and ready to use environment. Once provisioned, the Test Drive will be available for a limited time, typically a few hours. After the Test Drive is over, you will receive an email with the instructions to purchase or continue using the product. As you start thinking about your next DevOps tool or Web application firewall, consider using Test Drives. It is easy, free, and the hands-on experience will help you make the right decision. Happy Test Driving.
Quelle: Azure

Gartner names Microsoft Azure as a leader in the Cloud IaaS MQ

As customers bet more and more on Cloud to drive digital transformation within their organizations, we’re seeing tremendous usage of Azure. Recently, Forbes reported a study done by Cowen and Company Equity Research, and stated that Microsoft Azure is the most used Public Cloud as well as most likely to be renewed or purchased. More than 90 percent of the Fortune 500 use Microsoft’s cloud services today. Large enterprises such as Shell, GEICO, CarMax and MetLife, as well as smaller companies like Medpoint Digital and TreasuryXpress are all leveraging Azure to fuel business growth and reinvent themselves. We strongly believe that the momentum we’re seeing has been possible because of what Azure offers and stands for – a comprehensive and secure Cloud platform across IaaS and PaaS, unparalleled integration with Office 365, unique hybrid experience with Azure Stack, first-class support for Linux and open-source tooling, and a robust partner ecosystem.

Today we’re delighted that Gartner has recognized Microsoft as a leader in their Cloud Infrastructure as a Service (IaaS) MQ for the fourth consecutive year. We’re excited that Gartner continues to recognize Microsoft for completeness of our vision and ability to execute in this key area.

While we’re honored by our placement in the leaders quadrant for Cloud IaaS, we believe many of our customers choose Microsoft not just for our leadership in this area but because of our leadership across a broad portfolio of cloud offerings spanning Software as a Service (SaaS) offerings like Office 365, CRM Online and Power BI in addition to Azure Platform Services (PaaS). It’s the comprehensiveness of our cloud portfolio that gives customers the confidence that that no matter where they’re in their cloud adoption journey, they’re covered with a breadth of solutions for their problems instead of having to work with multiple vendors.

Here’s the list of cloud-related Gartner MQs where Microsoft is placed in the leader’s quadrant:

We look forward to continuously innovating and delivering across our portfolio of cloud offerings, and sincerely believe that every customer – whether big or small, new or seasoned to the Cloud, relying on open-source or otherwise –  has meaningful business value to gain from Azure. If you haven’t dug into Azure yet, here’s an easy way to do it!

If you’d like to read the full report, “Gartner: Magic Quadrant for Cloud Infrastructure as a Service,” you can request it here.

Disclaimer: Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

**Gartner Magic Quadrants

Gartner Magic Quadrant for Cloud Infrastructure as a Service; Lydia Leong, Raj Bala, Craig Lowery, Dennis Smith; June 17, 2017
Gartner Magic Quadrant for Public Cloud Storage Services, Raj Bala, Arun Chandrasekaran, July 26, 2016
Gartner Magic Quadrant for Access Management, Gregg Kreizman, Anmol Singh, June 7, 2017
Gartner Magic Quadrant for Business Intelligence and Analytics Platforms, Rita Sallam, Cindi Howson, Carlie Idoine, Thomas Oestreich, James Laurence Richardson, Joao Tapadinhas, February 16, 2017
Magic Quadrant for the CRM Customer Engagement Center, Michael Maoz, Brian Manusama, May 8, 2017
Magic Quadrant for Data Management Solutions for Analytics, Roxane Edjlali, Adam Ronthal, Rick Greenwald, Mark Beyer, Donald Feinberg, February 20, 2017
Magic Quadrant for Enterprise Agile Planning Tools, Thomas E. Murphy, Mike West, Keith James Mann, April 27, 2017
Magic Quadrant for Horizontal Portals, Jim Murphy, Gene Phifer, Gavin Tay, Magnus Revang, October 17, 2016
Magic Quadrant for Mobile Application Development Platforms, Jason Wong, Van Baker, Adrian Lowe, Marty Resnick, June 12, 2017
Magic Quadrant for Operational Database Management Systems, Nick Heudecker, Donald Feinberg, Merv Adrian, Terilyn Palanca, Rick Greenwald, October 5, 2016
Magic Quadrant for Sales Force Automation, Tad Travis, Ilona Hansen, Joanne Correia, Julian Poulter, August 10, 2016
Magic Quadrant for Unified Communications, Bern Elliot, Megan Marek Fernandez, Steve Blood, July 13, 2016
Magic Quadrant for Web Conferencing, Adam Preset, Mike Fasciani, Whit Andrews, November 10, 2016

Quelle: Azure

Announcing public preview of Apache Kafka on HDInsight with Azure Managed disks

HDInsight set a firm goal of helping enterprises build secure, robust, scalable open source streaming pipelines on Azure. To meet this goal, a few months ago we announced a limited preview of Managed Kafka on Azure HDInsight. The addition of Kafka on HDInsight completes the ingestion piece for scalable open source streaming on Azure. In addition to the scale and performance benefits of Apache Kafka, HDInsight Kafka customers reap the following advantages:

The promise of a managed open source Kafka backed by a 99.9% uptime SLA

This includes installation, configuration, and management of open source components
HDInsight additionally provisions and monitors a Zookeeper quorum as part of the cluster shape.

Managed rebalance of replicas and partitions across Azure update domains and fault domains. This ensures high availability of Kafka partitions on environments with a multidimensional view of a rack. This tool is also open sourced here.
Security and compliance benefits of Azure and HDInsight with certifications such as SOC, PCI, DSS.
An integrated experience to deploy a managed and secure streaming pipeline (Kafka, Storm or Spark streaming) within minutes via prebuilt architectures on ARM templates.

Today we are pleased to announce the Public Preview of Apache Kafka with Azure Managed Disks on the HDInsight platform. Users can now deploy Kafka clusters with managed disks straight from Azure portal, with no signup necessary. This allows for the powerful advantage of exponentially higher scalability, alongside exponential lower cost as workloads scale. This feature is discussed in more detail below.

Customer Success Stories: Toyota Connected

Over the last year HDInsight has worked very closely with is Toyota Inc. to build one of the world’s largest and most distributed connected car streaming platform. This platform processes millions of large events/day in production on HDInsight Kafka to unlock insights in real-time. A platform at this scale was made possible by the secure, managed, and elastically scalable nature of HDInsight. The benefits are best explained by the Chief Product Owner of Toyota Connected below.

"Toyota manufactures millions of cars running globally, and building a connected car platform to process real-time data at Toyota scale is a monumental challenge. To process events at Toyota’s scale, technologies such as Kafka need to be leveraged. Since HDInsight is the only managed platform that provides Kafka as a managed service with a 99.9% SLA, Toyota was able to leverage the scalable technology of Kafka, Storm and Spark on Azure HDInsight. Using the HDInsight platform, we were able to deploy enterprise grade streaming pipelines to process events from millions of cars every second. This is just scratching the surface – the future of global connected cars on Azure HDInsight is bright, and we are excited for what's in store."   -Vijay Chemuturi, Chief Product Owner, Toyota Connected

A high-level architecture of the connected car architecture is depicted below. As Vijay states, this is just the beginning – we are very excited to build upon this powerful streaming platform in the upcoming months.

More details on architecting similar IoT scenarios will follow in upcoming series of blogs.

Integration of HDInsight Kafka with Azure Managed Disks

With this public preview, HDInsight Kafka is also releasing native integration with Azure Managed Disks.

Azure Managed Disks is a new feature that abstracts the storage account specification for the customer allowing for an easier and managed route to use disks. They provide for a higher scale by abolishing the storage account IOPS limitation, along with the ability to create hundreds of VMs from a given VHD in a centralized storage account. A disk can be either Premium (SSD) or Standard (HDD), and 1 TB in size. More information on this feature is located here.

Kafka is a high throughput, low latency messaging service that is I/O heavy. Prior to Azure Managed Disks, HDInsight Kafka’s original preview offering stored data on the largest persisted disk of the node. This meant that each node had a limitation of 1 TB. Given Kafka’s I/O heavy nature the disk would often become the bottleneck and additional nodes needed to be added for more storage. This resulted in high cost, with a gross underutilization of the CPU and memory on the cluster. With this release, we are implementing the HDInsight Kafka with Managed Disks feature, which is pictorially depicted below.

With this feature, one can have both persisted and scalable data, up to 16 TBs per node. This allows for an exponentially lower cost, higher scalability and better performance as the workloads increase. Since the cost of a disk is a fraction of the cost of a node, the below figure shows how the number of nodes and cost scales down exponentially as scaling needs increase.

This feature is automatically turned on, and taking advantage of this feature is simple – the user just needs to specify the number of disks to be attached to a given node. This can be done via the portal, or by specifying a single property in the ARM template, shown in the below figures. Note that the type of disk – Premium or Standard is determined by the type of VMs chosen the worker nodes. Premium disks are attached to DS and GS series VMs, whereas standard are attached to all other VM types. End to end templates, with examples on how to create these clusters are detailed in the next section. More information on this is located in our documentation.

Disk Specification via Portal 1

Disk Specification via ARM template 1

Start deploying and using Spark, Storm, and Kafka with Managed Disks on HDInsight within minutes

We have updated our documentation and samples to help deploy scalable open source streaming solutions on HDInsight. Each of these examples walks through creating the clusters step by step, and contain one-click deploy ARM templates to enable powerful pipelines. We have additionally updated the Spark Streaming examples to include the new examples for Structured Streaming, and creating an end to end pipeline using Twitter, Kafka, Spark Streaming..

Getting Started with Kafka for HDInsight
Deploy HDInsight Kafka + Spark streaming
Deploy HDInsight Kafka + Storm
Stream data from on-premise to HDInsight Kafka in the cloud
Stream tweets to HDInsight Kafka and process with Spark structured streaming

For any questions, suggestions or feedback, please do not hesitate to reach out to us via HDIFeedback@microsoft.com. We are really excited to have you onboard, and would love to hear from you.
Quelle: Azure

Solve Node.js issues faster with Application Insights for Node.js

Azure Application Insights is an application performance management (APM) platform which provides performance and diagnostic information about your running services and applications to help you discover and diagnose issues quickly. Use App Insights wherever your Node.js application runs containers, PaaS, IoT, and even Electron desktop apps. Just follow the instructions to drop the Node.js SDK into your app and watch helpful information flow into the Azure Portal in minutes.

APM’s help you understand and act upon what’s happening in your application, so one of our missions is to automatically collect and display information to help you pinpoint issues. We hope the following capabilities available in our latest SDK release (0.21.0) contribute to that.

 

Find related events

When pinpointing issues, reviewing all your traces and logs might be helpful, but it would be even more helpful to quickly filter to only those directly related to the problem at hand. For example, if your API service returns an error response, we’d like to help you quickly find all other traces related to that error. There’s a good chance some of them will lead you much closer to the source of the problem!

In this release we’ve made this correlation possible by including a shared correlation identifier in each App Insights item. Once you’ve drilled into the details view of any item, click “All available telemetry for this root operation” and instantly get a filtered view of related items based on correlation ID. For example, in the following screenshot a Node.js API request in turn leads to a MongoDB call, and the Request and Dependency items can be instantly viewed together.

Get started and learn more about Navigation and Dashboards in the Application Insights portal.

How it works

Instant filtering of correlated items in the Azure Portal requires adding the same correlation identifier (ID) to all related items sent by the SDK. That means we have to share that ID in the SDK between the original request context and the context of later operations like database and HTTP calls or sending a response. Sharing such data across callbacks and other asynchronous tasks in Node.js and JavaScript is challenging because JavaScript and Node.js don’t yet include a standard way to share context across callbacks, though several efforts are in progress.

Let’s illustrate the problem with a simple weather API service. A request to this API returns forecast details for a US zip code specified by an HTTP query parameter. The service itself gets these forecast details from the OpenWeatherMap HTTP API. The main handler follows and full code is in this gist.

function httpHandler (request, response) {

let parsed_url = url.parse(request.url);
let zip = parsed_url.query.zip;
let country = parsed_url.query.country || 'us';

let query_string = `zip=${zip},${country}&APPID=${appid}`
http.get(`http://api.openweathermap.org/data/2.5/forecast?${query_string}`,
weather_response => { weather_response.pipe(response) }
);
}
http.createServer(httpHandler).listen(8080);

The topology of this app as discovered by App Insights’ App Map.

In this code, when Node’s HTTP server receives an incoming request, the App Insights SDK collects information about that request for you and specifies a correlation identifier (ID). When the associated HTTP request is then sent to the OpenWeatherMap API the SDK collects information about that request too including the previously specified correlation ID. When the response from the OpenWeatherMap API is received by the service and sent along to our original caller more events are sent by App Insights which include the original correlation identifier too.

To share such an identifier across all these operations and callbacks the App Insights SDK needs to access some storage shared across all of them. To meet this challenge, App Insights now utilizes the popular zone.js library by default to provide a persistent context to store and share a correlation identifier when a request starts, and retrieve and use it when other information is collected and sent to you. As a result, you, and the Portal on your behalf, can filter by this identifier to discover and fix problems in your app faster.

Find related events *across services*

With zone.js included App Insights is able to get you to the root of a problem in a single service faster. However, your application may in fact consist of multiple services, and the root problem may be in another one. To address this and provide you a filtered view of related traces and logs across all your services takes another step which we and the .NET team have begun to implement in this release.

We can now properly relate traces between multiple Node.js and .NET services, which communicate via HTTP when both utilize the same instrumentation key (ikey). As an example, in the following screens I’m investigating a failed request in an application with a Node.js API service which in turn invokes a .NET service. I drill in to one of these failures to find related events, where I find some events from the Node.js service and some from .NET. I’m quickly able to identify that an exception in the .NET service led to the failed Node.js API call!

Better App Map support

The correlation work described above also allows us to better represent your app’s topology in App Insights’ App Map. The same Node.js and .NET app mentioned above is represented in App Map as follows, with the Node.js API as “appinsights-node-02” and the .NET service as “api-11”. The error (“!”) icon in the api-11 node could serve as another entry point for the investigation described in the previous section.

We continue to experiment with details provided by App Map and would like your feedback on what’s needed to meet your needs.

Info from third-party modules

Next, we know how much value you derive from Node.js’s massive module ecosystem. To truly help you pinpoint problems you’ll often need insight into third-party modules too. For example, if a MongoDB call fails, it would help to know what message was sent to the Mongo service and what error was received.

App Insights provides a user API you can use to trace this activity yourself, but with this release we begin collecting this information for you automatically for MongoDB, MySQL, and Redis, as well as the Bunyan logging framework and console APIs. So now more detail about that failed database call is automatically available to you.

For example, you may have noticed in an earlier example that a Node.js API call which led to a MongoDB call actually listed *2* calls to Mongo. Drilling in helped me determine that both find and getMore commands were sent, as shown in the following screenshot.

Since maintaining module patches for third-party modules is hard to do consistently and reliably, we’ve published these patches and the mechanism we use to utilize them as the open source node-diagnostic-channel project on GitHub. We’re working on sharing this project with Project Glimpse and would love feedback from other tool providers and module authors on how we can collaborate.

Sampling

Last but not least, this release also includes support for percentage-based sampling so you can reduce the amount of data sent to your App Insights resource and thereby reduce costs. Don’t worry, our sampling algorithm is sensitive to the correlation work described above, so even if you’ve enabled sampling we’ll still send all related events from a sampled request.

To enable sampling, specify a percentage before starting the client as follows:

const appInsights = require("applicationinsights");
appInsights.client.config.samplingPercentage = 33;
appInsights.start();

Conclusion

For all the details on these and other updates, see the changelogs for v0.20.0, v0.20.1, and v0.21.0.

Our goal is to help you quickly discover and diagnose performance and functional issues in your Node.js services and applications. We’d love your feedback here and in GitHub on how we’re doing and what’s most important to you. Thanks!
Quelle: Azure

May 2017 Leaderboard of Database Systems contributors on MSDN

Congratulations to the May 2017 top-10 contributors! Hilary Cotter and Alberto Morillo continue to top the Overall and Cloud database for the fourth successive month.

This Leaderboard initiative was started in October 2016 to recognize the top Database Systems contributors on MSDN forums. The following continues to be the points hierarchy (in decreasing order of points):

For questions related to this leaderboard, please write to leaderboard-sql@microsoft.com
Quelle: Azure

New Azure VPN Gateways now 6x faster

Running mission-critical workloads require both performance and reliability. To improve your Azure VPN experience, we are introducing a new generation of VPN gateways with better performance, a better SLA, and at the same price as our older gateways.

Many customers with network intensive workloads in Azure Virtual Networks (VNets) are driving the need for increased cross-premises and cross-region VPN performance. To accommodate even more demanding workloads we re-engineered our VPN Gateway service to provide 6X more performance coupled with better reliability and backed by an even stricter SLA.

In addition to performance, many customers with mission-critical workloads need control over the VPN policies to meet compliance regulations. We now provide custom IPsec/IKE policy selection giving you more flexibility to choose your encryption policy. We are also enhancing the new gateways to accommodate both route-based and policy-based VPNs. Although a route-based VPN using BGP to automatically learn routing is easier to manage, many customers have already deployed policy-based VPNs at their branch offices. The new VPN gateways allow multiple sites using policy-based VPNs to connect to the same VPN gateway.

New guidance

As we introduce the new VPN gateways, called VpnGw1, VpnGw2, and VpnGw3, we are also updating our deployment guidance. The existing Basic VPN gateway is unchanged with the same 80-100 Mbps performance and a 99.9% SLA. The Basic VPN gateway is appropriate for non-production dev/test scenarios. The Basic VPN gateway should not be used for any production scenarios.

For your production services, we strongly recommend that you select or migrate to the new VPN Gateways that have a 99.95% SLA. The new VPN gateways have a higher SLA and better performance at the same price as the old gateways. We will continue to support the old VPN gateways so you can manage existing deployments, but starting in September you will not be able to create the older Standard or High Performance VPN gateways.

Better performance

The new generation of Azure VPN Gateways provide single tunnel performance of up to 1 Gbps and aggregate up to 1.25 Gbps with multiple tunnels improving your access to VNets either from your premises or for cross-region VNet-to-VNet connectivity. Enabling the active-active VPN gateway option provides even higher throughput with multiple flows to your Azure VPN gateways.

Here are the details:

VPN Gateway
Recommended Workload Type
Price
($ per hour)
Throughput Benchmark*
SLA

S2S & V2V tunnels

($ per tunnel-hour)

PS2 Tunnels
(Max)

Basic
Dev/Test
$0.04
100 Mbps
99.9%

Max 10
1- 10: included

0

VpnGw1
Production
$0.19
650 Mbps
99.95%

Max 30
1- 10: included
11-30:  $0.015

128

VpnGw2
Production
$0.49
1 Gbps
99.95%

Max 30
1-10: included
11-30: $0.015

128

VpnGw3
Production
$1.25
1.25 Gbps
99.95%

Max 30
1-10: included
11-30: $0.015

128

* Benchmark data obtained by running iperf3 between VNets in the same region, with minimum duration of 120 seconds and up to 32 flows. Refer to this page for more details on how to measure throughput across your Azure VPN gateways.

VpnGw1 at 650 Mbps provides a 6.5x and VpnGw2 at 1Gbps provides a 5x performance improvement at the same price as the old Standard and High Performance gateways, respectively. We also increased the Site to Site (S2S) tunnel count from 10 to 30 tunnels so you can connect more of your sites to the VPN Gateway. There is a per S2S tunnel charge for the 11th through 30th tunnels. We also are introducing a new, even higher performance VPN gateway called VpnGw3. With multiple tunnels VpnGw3 has shown 1.25 Gbps throughput in our tests. Please note that the actual performance in production is highly dependent on the application behavior, the quality of your ISP, and the actual distance (network path) from your physical VPN device to the Azure region with your Azure VNet.

Customers often deploy a S2S VPN to connect branch offices to the same Azure VNet while the main corporate WAN is accessed via ExpressRoute. The corporate WAN may also use S2S VPN as a backup path in case of a connectivity issue with ExpressRoute. 

If you have a 1 Gbps ExpressRoute circuit you can now also have a 1 Gbps S2S tunnel on the backup path so if a failover event occurs you still have a performant network connection to your VNets, although via the Internet. Note the performance caveats mentioned previously regarding the quality of your ISP.

New VPN capabilities – Custom IPsec/IKE policy & multi-site policy-based VPN

We are also releasing two new features to improve VPN manageability and give customers more choices. These include the support for custom IPsec/IKE connection policies to satisfy your compliance and security requirements, and the ability to connect multiple on-premises networks using policy-based firewall devices to your Azure VPN gateway.

With custom IPsec/IKE policy, you can now set the exact cryptographic algorithms and key strengths on each S2S or VNet-to-VNet connection to satisfy your enterprise compliance and security requirements. Azure VPN gateways utilize a default set of IPsec/IKE cryptographic algorithms that maximize interoperability with a wide range of 3rd party VPN devices. The default list may not meet all your compliance requirements. For example, you may need higher Diffie-Hellman Group or PFS Group (Perfect Forward Security) than the default, or there are certain cryptographic algorithms that you want to exclude (e.g., SHA1, 3DES, etc.) You can now specify the exact combinations of cryptographic algorithms and key strengths, as shown in the example below:

Additionally, you can now connect multiple on-premises policy-based VPN devices to your Azure VPN gateway, by utilizing the custom policy:

We do understand that configuring and maintaining VPNs for mission-critical workloads are complex tasks. These new VPN capabilities were developed based on customer feedback. We have re-written much of our documentation and will be providing more deployment blueprints, guidance, and best practices.

Please let us know how we can further enhance the Azure VPN service. Here are some links to get started with the new VPN gateways:

About new VPN gateway SKUs & migration instruction
About cryptographic requirements and Azure VPN gateways
Configure IPsec/IKE policy on S2S VPN or VNet-to-VNet connections
Connect multiple policy-based VPN devices to Azure VPN gateway

Quelle: Azure

Embed Video Indexer insights in your website

Video Indexer embeddable widgets is a great way to start adding AI insights to your videos. Whether you want to add deep search ability to your published videos or let your users be more engaged with the video content on your website, you can easily achieve that by using the embeddable option at Video Indexer web application or by using Video Indexer API.

Getting Started

To get started embedding Video Indexer insights to your website you must have a registered account. If you don't have an account you can easily Sign-In to Video Indexer using a Microsoft, Google, LinkedIn, or Azure Active Directory and get one generated for you.

Video Indexer supports embedding two types of widgets into your application: Cognitive Insights and Player.

Cognitive Insights Widget

This widget contains all the visual insights that were extracted from the video after the indexing process such as people appearances, top keywords, sentiment analysis, transcript and search.

It also allows you to change the language and get all the insights based on the selected language. Here is an example:
 

Player Widget

The player widget is a customized Azure Media Player that except of providing video streaming, contains extra features such as playback speed and closed captions. Here is an example:

In order to embed a widget in your website you need to get an embed code and paste it in your html file. The embed code contains iframe tag with embed URL.

You have two options to get the embed URL: via Video Indexer web application or by calling Video Indexer API specific method. We will cove both ways.

Get your embed code via Video Indexer web application (Public videos only)

You can easily get the embed code for your indexed videos with a click of a button:

1. Login to your account at VI

2. Upload a video

3. After indexing process has completed click “play” on the video at the main gallery page.

4. Click the “embed button” and select the widget you want to embed with the desired options. (player/insights)
 

5. Copy and paste the code into your html file.

Notice: if you embed via the web application you can embed only public videos. Private videos requires accessToken parameter in the embed URL that contains 1h access token for the video.

Get your embed code via Video Indexer API (Public or Private videos)

In order to get the embed URL that contains the accessToken for your video you can use Video Indexer API and call Get Insights Widget Url or Get Player Widget Url by passing the video id.

If you manage your own videos you can also get the embed code based on your internal video id by calling Get Insight Widget By External Id.

In order to start working with the API you will have to register and get your API subscription key first. The Getting started with the Video Indexer API blog post is where you will find a very detailed blog post about getting started with Video Indexer API.

After you have your embed URL just paste it as “src” attribute of an iframe element which you want to locate anywhere in your website.

<iframe width="580" height="580" src="https://www.videoindexer.ai/embed/insights/c4c1ad4c9a/?widgets=people,search" frameborder="0" allowfullscreen></iframe>

Embedding options

Video Indexer widgets are customizable per your need. You can choose to embed only the insights widget or the player, or embed them both.

Embed both types of widgets in your application

Copy and paste the embed codes for the player widget and the insights widget and include the following JS file before the closing <body> tag:

<script src="https://breakdown.blob.core.windows.net/public/vb.widgets.mediator.js"></script>

The above file is required in order to handle cross origin communications between the widgets.

You can read more about how it works at our docs.

Embed cognitive insights and use your Azure Media Player

If you are using Azure Media Player in your website you can easily embed Video Indexer insights widget that will communicate with your player using vi communication plugin. Just paste the following script in your page after azure media player library script and you are all set.

<script src="https://breakdown.blob.core.windows.net/public/amp-vb.plugin.js"></script>

The plugin let you also get the VTT file for your player and choose if you want to sync between language and transcript with your video.

For more information and code samples see the relevant Video Indexer docs.

Embed cognitive insights with any video player

If you are using other players like YouTube player, Vimeo or your own player you can still embed Video Indexer cognitive insights and make them communicate with your player, for example jump into the relevant moment when user clicks on one of the widgets.

In order to achieve that you will have to implement some functions and listen to “postMessage” JavaScript event.

Here is a detailed demo that demonstrates this approach.

How to customize Video Indexer widgets?

Video Indexer widget are customizable per your need. You can choose to embed only the insights that you think will be more valuable for your users.

Customize the cognitive insights widget

You can choose the types of insights you want by specifying them as a value to the  following URL parameter added to the to the embed code you get (from API or from the web application):

&widgets=<list of wanted widgets>

The possible values are: people, keywords, sentiments, transcript, search.

For example, if you want to embed widget containing only people and search insights the iframe embed URL will look like this:

https://www.videoindexer.ai/embed/insights/c4c1ad4c9a/?widgets=people,search

You can see a detailed demo here and read more at Video Indexer docs.

Customize the player widget

If you embed Video Indexer player you can choose the size of the player by specifying the size of the iframe.

For example :

<iframe width="640" height="360" src="https://www.videoindexer.ai/embed/player/{id}” frameborder="0" allowfullscreen />

By default Video Indexer player will have auto generated closed captions based on the transcript of the video that was extracted from the video with the source language that was selected when the video was uploaded.

If you want to embed with a different language you can add &captions=< Language | ”all” | “false” > to the embed player URL or put “all” as the value if you want to have all available languages captions.

The embed URL then will look like this : https://www.videoindexer.ai/embed/player/6bc9113d26/?captions=italian. If you want to disable captions you can pass “false” as value for captions parameter.

Auto play – by default the player will start playing the video. you can choose not to by passing &autoplay=false to the embed URL above.

For more details, please take a look at the Video Indexer Documentation. Follow us on Twitter @Video_Indexer to get the latest news on the Video Indexer. If you have any questions or need help, contact us at visupport@microsoft.com.
Quelle: Azure