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


In this chapter, we explored the SQL-like Stream Analytics query language. We tried a wide variety of transformations using standard built-in functions, like substring and concatenation. We also used aggregate functions like sum, min, max, and count, and analytic functions for aggregating subsets of groups and comparing events to each other with first and lag. We worked with complex data types, parsing arrays and nested arrays, and reshaping them into a structured output. We then grouped events by windows of time for computing aggregates. Finally, we examined the time management policies used in Stream Analytics jobs to handle time-skewed events in streams. These SAQL techniques can be applied to enable the vast majority of streaming scenarios.

Now that we understand how to develop and implement SAQL query logic, in the next chapter, we will turn to scalability. We will learn about operationalizing Stream Analytics workloads from low to massive scales.