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

Canonical Azure architecture

The following architecture is a well know canonical design pattern for streaming data, let's review the components of the architecture:

  • Input: Inputs are the sources of events. Note that the original sources of streaming events are devices, machines, applications, sensors, applications, and so on. However, ASA is not intended to connect to them directly. Rather, ASA lets Azure Event Hubs be the primary interface to the wide variety of event sources. ASA is optimized to get streaming data from Azure Event Hubs and Azure Blob storage. Azure Blob storage is the likely place where log or reference data is stored. The list of input sources that ASA directly integrates with may increase in the future, but Azure Event Hubs and Azure Blob storage will be the primary sources. There can be multiple inputs used in each Stream Analytics job that can come from Azure Event Hubs and Azure Blob storage.
  • Query: Queries are the main component of an ASA application. They implement...