In this video, learn about some of the advanced Azure services that can be implemented with your applications, including machine learning, big data, and Azure Media Services.
- [Instructor] Let's wrap up this chapter by looking at some of the advanced Azure services that you can integrate into your applications. Let's start off with Big Data which is just a collection of massive amounts of data from a variety of sources, and these sources could be from the Internet of Things, data from a website or any other source that generates massive amounts of data. And to manage this big data, we can use HDInsight which is a cloud distribution of the Hadoop technology stack.
There are several support cluster types for HDInsights including Apache Hadoop, Apache Spark, Apache HBase, Apache Storm, and finally Microsoft R Server. But as always, Microsoft is always adding to Azure. Therefore I expect this list to grow as new technologies are added. We can use Stream Analytics which is a fully managed event processing service to provide real time analytics on our streaming data, and that analytics could include filtering, analyzing, and aggregating that data.
And those data sources could include all the sources that we used from big data as well as IoT, websites or even social feeds. Streaming analytics will integrate with Event and IoT Hubs and Machine Learning. And we'll talk about that in a moment. It is scalable up to one gig per second, and you pay for it based on the streaming unit usage. Let's go ahead and take a look at the Stream Analytics Workflow. As we can see, we're pulling in that data into Stream Analytics from an Event Hub or Blob Storage or another source for our Big Data.
We can then pull machine learning into Stream Analytics, and then those results can be pushed out to a variety of receivers including Azure SQL Database, Blob Storage, Table Storage, Event Hubs or Power BI. Just to name a few. There are several use cases for stream analytics including Real-time fraud detection. When you see those trending topics and stories on your social feels, stream analytics provides that information. Real-time stock-trading analysis and alerts comes from stream analytics.
You can use stream analytics for data and identity protection services as well as web clickstream analytics and finally, if you embed sensors and actuators into your devices such as your IoT devices, then you'll have real-time analytics of what's going on around you. For example, you could use it in your traffic app. And finally, let's wrap this up on Machine Learning which provides predictive analysis. It finds patterns and can forecast future trends and behaviors.
A good and timely example of this is machine learning can be used to predict the strength and path of a hurricane well in advance of the hurricane actually arriving. This will provide time for preventative action to be taken. Let's take a look at a basic machine learning workflow. First we have our data sources and management, and again, this data source could be coming from Big Data, Azure Storage, HD Lake, HDInsight. That data is then analyzed by the machine learning service, and we can do this using the machine learning studio.
And this studio will create the learning models. From there we can add additional analytics and intelligence if we wish to do so, and we can push it out to something like Power BI. Now let's put all these advanced services together. First we start off with our data sources, and this could come from apps or IoT. Next we have information management. This is where our data can be cataloged. Next we have our big data stores. This is where we store all our data. So this could be in a Data Lake Store or a SQL Data Warehouse.
We can then apply machine learning and analytics to that data. And finally, we can push those results to dashboards and other visualizations such as Power BI. And finally, I'm just going to quickly mention Azure Media Services which is a live video streaming and on-demand service offered through Azure. It is globally distributed, and is highly scalable. And you can upload, store, encode and package content using Azure Media Services.
This is a great solution if you're offering streaming content through your application. In this chapter, we explored a few of the advanced services that you can integrate with your Azure applications. Azure is not a one size fits all solution, and I would highly encourage you to explore how these and other services that we didn't discuss can enhance your applications.
- Creating compute-intensive applications
- Creating long-running applications
- Implementing messaging systems
- Azure Service Bus relays
- Using Azure Storage queues
- Creating an Azure Event Hub
- Creating Azure WebJobs
- Managing cloud environments with Azure Active Directory Domain Services