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


Continuously streaming data makes real-time computations and insights possible, overcoming the latency inherent in batch data processing systems. However, insights requiring aggregations of data, even very recent trending (for example, in the past 10 seconds), need to break the data stream into bounded groups of events. Time is a fundamental concept of streaming data systems and the natural construct to use when defining event boundaries for computing aggregations.

The following screenshot shows an event stream with defined time windows overlayed and sample computations produced. Note that the time windowing is fundamental to computing aggregates, like a count of events:

Stream Analytics uses windows of time to group events and supports window types that enable a variety of common event grouping patterns. In this section, we will examine the tumbling window, hopping window, and sliding window types. Stream Analytics windows are always used in the GROUP BY query clause. Queries will...