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

How to scale queries using Streaming units and partitions

In traditional static data scenarios, the query can be executed against a fixed data set and results will be available after a known interval. On the other hand, with streaming data scenario involving constant changes to a dataset, the queries will run longer duration or might not even complete.

Additionally, a constant stream of data will increase the volume of data and query will drain the working memory. One way to draw data boundary is through the context of time. For example with streaming dataset, we can specify a data boundary that resides within the start and ends time. This will restrict the query execution between a known boundary. Application and arrival time are the two type of timing constraints we can use to set time boundaries for the streaming data.  

Application and Arrival Time

Time at the event origin is known as the Application Time, time at event landing is called the Arrival Time. Within the queries, we can use...