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

Query language elements

Many of the familiar building blocks of SQL queries are also provided by SAQL. A few of the most common and fundamental elements are:

  • SELECT, for projecting columns in the query output
  • FROM, for setting the input or input-derived data source
  • CASE, for condition evaluation
  • WHERE, for filtering input data

Certain other SAQL elements, while familiar from traditional database query patterns, have distinctive benefits for streaming data. Let's take a closer look at a few of them:

  • WITH: Defines a temporary derived table for later reference in the query. That much is the well-known role that WITH plays in SQL, but in Stream Analytics, it also helps when scaling out a query for more efficient handling of a higher throughput workload. Because the result set defined by WITH can be referenced multiple times in the query, encapsulating common business logic there can yield significant savings in the resources used by the Stream Analytics job.

Syntax: WITH result set alias1 AS (SELECT...