Azure Stream Analytics brings complex event processing to the Azure cloud platform. This course will teach you how Stream Analytics jobs can be integrated with other Azure services, and used to process event and telemetry streams.
Event-based applications, internet of things projects, and online games can generate a vast amount of event and telemetry data. Querying and analyzing these event streams and being able to provide updates and visualization in real-time will add enormous value to your solutions. In this course, Understanding Azure Stream Analytics, you'll explore the functionality of Azure Stream Analytics, creating SQL like queries to analyze telemetry and event data. First, you'll learn how to integrate these query jobs with other Azure services so that they can receive telemetry from a driving game, and send the query results to a number of different services. Next, you'll explore data and business intelligence tools to visualize the output data created by stream analytics. Finally, you'll dive into a scenario for this course, which will use telemetry data created during the Global Azure Bootcamp racing game lab to provide real-world telemetry and lap data for examples. By the end of this course, you'll have the necessary knowledge to leverage Azure Stream Analytics in messaging and telemetry.
Alan Smith is a Windows Azure developer, trainer, mentor and evangelist at Active Solution in Stockholm. He has a strong hands-on philosophy and focusses on embracing the power and flexibility of cloud computing to deliver engaging and exciting demos.
Course Overview Hello everyone, my name is Alan Smith, and welcome to my course on Understanding Azure Stream Analytics. I work as a senior consultant at Active Solutions Stockholm in Sweden. Event-based applications in terms of things projects and online games, can generate a vast amount of event and telemetry data. Querying and analyzing theses streams and being able to provide updates and visualizations in real time will add enormous value to our solutions. In this course, we're going to explore the functionality of Azure Stream Analytics by creating SQL-like queries to analyze telemetry and event data. We'll then integrate these query jobs with other Azure services so that they receive telemetry from a driving game and send the query results to a number of different services. To round off the course, we will use data and business intelligence tools to visualize the output data created by Stream Analytics. Some of the major topics that we will cover include complex event processes scenarios, querying event streams with Azure Stream Analytics, integrating Stream Analytics with other event sources, and visualizing event-based output data. By the end of this course, you'll know how to leverage Azure Stream Analytics in messaging and telemetry scenarios. Before beginning this course, you should be familiar with the basics of writing SQL queries.
Introducing this Course Hello and welcome to my course on Understanding Azure Stream Analytics. My name is Alan Smith and I work as a consultant for Active Solution based in Stockholm Sweden. In this course, we're going to be examining the functionality of Microsoft Azure Stream Analytics. Stream Analytics allows us to take raw data streams, such as telemetry data from an IoT application, or a data stream being produced by an online game. We can ingest that data using a number of services on the Azure platform. The data stream will be transmitted to a service in Microsoft Azure, and we can use Azure Stream Analytics to integrate with that service and receive and process the data stream. Once the data stream has been processed, we can archive that data, and we can also use business analyst tools to provide reports on that data. We'll start the course by looking at complex even processing and how it differs from typical scenarios where we might be using big data. We'll then look at the architecture of a telemetry processing application and see how Azure Stream Analytics can fit into that architecture. We'll focus on working with the Azure portal to create Stream Analytics jobs and see how we can compose queries for those jobs using a SQL-based query language that many developers will be familiar with. We'll introduce the concept of query windows, which allow us to add a temporal or time-based aspect to our query. Well look at how in more complex scenarios we can combine these query windows to give us more meaningful statistics. We'll take an overview of the many Azure services that Azure Stream Analytics can integrate with. We'll round off the course looking at data analysis and see how we can use tools, like SQL Azure Data Warehouse, and Power BI Desktop, to provide visualizations on the process stream data.
Understanding Complex Event Processing and Azure Stream Analytics In this module, we're going to gain an understanding of complex event processing and Azure Stream Analytics. We'll start off by looking at complex event processing, defining what complex event processing is, look at some definitions and look at some scenarios, where we can use complex event processing. We'll then move on to look at the services provided by Azure Stream Analytics, see how we can use Azure Stream Analytics, what capabilities it provides and how we can integrate it with other Azure services. We'll round off the module by looking at the demo that we're going to use in the course, which is going to feature a 3D racing game, where we're using Azure Stream Analytics to process the driver telemetry data.
Querying Data Streams In this module, we're going to look at querying data streams. We'll start out by taking an overview of the Stream Analytics query language. We'll then look at working with query windows so we can add a temporal aspect to our data queries. We'll look at time stamping events that can help to ensure that we're getting accurate outputs from our telemetry data. We'll then look at the demo to see how we can use the Azure portal to run some basic queries on some sample telemetry data. We'll move on to look at some more advanced queries. We'll look at combining query data using joins and combining query windows. We'll then look at a demo of more advanced queries, looking at a couple of scenarios where we can perform more advanced queries on telemetry data.
Configuring Inputs and Outputs This module is going to focus on configuring inputs and outputs. We'll start out by looking at the different Azure services that we can use for inputs and outputs to the Azure Stream Analytics jobs. We'll then look at configuring inputs. We'll look in a bit of detail at service bus event hubs. See how they work internally and how we can leverage their functionality in Stream Analytics jobs. We'll then move on to configuring outputs. We'll look at how we can output binary data using blobs, how it can work with streaming data using service bus topics and subscriptions, and also how we can work with tabular data using Azure Table Storage. We'll round off the module with a demo looking at how we can configure inputs and outputs in the sample driving game application.
Analyzing Output Data In this module, we're going to look at analyzing the output events from Stream Analytics jobs. We'll start off by looking at some of the data analysis technologies that we've got available on the Microsoft Azure platform. We'll take a brief look at Azure SQL Database. We'll then take a more detailed look at Azure SQL Data Warehouse. For a visualization tool, we'll be using Power BI. So we'll have a brief overview of Power BI because we'll be using this in the demos. We'll then look at some of the scenarios that we can use when analyzing event streams, and we'll round off the module and the course with a couple of demos. We'll look at how we can use Power BI services directly from the output on Stream Analytics jobs. We'll then look at how we can use a Stream Analytics job to send output events into a SQL Data Warehouse database, and then use Power BI to generate reports from that database. In both of these demos, we'll be using lap and telemetry data from the Global Azure Bootcamp racing game.