Book Image

Stream Analytics with Microsoft Azure

By : Krishnaswamy Venkataraman, Anindita Basak, Ryan Murphy, Manpreet Singh
Book Image

Stream Analytics with Microsoft Azure

By: Krishnaswamy Venkataraman, Anindita Basak, Ryan Murphy, Manpreet Singh

Overview of this book

Microsoft Azure is a very popular cloud computing service used by many organizations around the world. Its latest analytics offering, Stream Analytics, allows you to process and get actionable insights from different kinds of data in real-time. This book is your guide to understanding the basics of how Azure Stream Analytics works, and building your own analytics solution using its capabilities. You will start with understanding what Stream Analytics is, and why it is a popular choice for getting real-time insights from data. Then, you will be introduced to Azure Stream Analytics, and see how you can use the tools and functions in Azure to develop your own Streaming Analytics. Over the course of the book, you will be given comparative analytic guidance on using Azure Streaming with other Microsoft Data Platform resources such as Big Data Lambda Architecture integration for real time data analysis and differences of scenarios for architecture designing with Azure HDInsight Hadoop clusters with Storm or Stream Analytics. The book also shows you how you can manage, monitor, and scale your solution for optimal performance. By the end of this book, you will be well-versed in using Azure Stream Analytics to develop an efficient analytics solution that can work with any type of data.
Table of Contents (18 chapters)
Title Page
About the Authors
About the Reviewers
Customer Feedback

Understanding parts of a Stream Analytics job definition (input, output, reference data, and job)

Stream Analytics job definition includes inputs, a query, and output. We will start the topic with a brief introduction to input, output and query. We will look into each of the components in detail as follows:

  • Input: Data connections to Stream Analytics from a given event source is called an input. For instance, Stream Analytics can connect directly to Azure Event Hubs, IoT Hub, and Blob storage to receive events. Do note that the Azure resource mentioned can be from the same or different subscriptions. Stream Analytics can handle high event throughput of up to 1 GB per second.
  • Reference data: In addition to input, stream sets also support the second type of input, called auxiliary input (Reference data), to include a reference or static data.
  • Output: Results sets from Stream Analytics are known as output and these can be written to various output devices such as Azure SQL DB, Event Hub, Table...