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

Out of order and late-arriving events

The Out of order policy defines a time allowance for events arriving at the Stream Analytics job out of order. It governs how long the Stream Analytics job will buffer events and, within that grace period, corrects the order according to the application timestamp set in the query. While the Out of order events policy is a helpful mechanism by which to manage timing conflicts, it does introduce latency equal to the time allowance duration itself.

Due to connectivity and networking reasons, events generated by source application will arrive out of order. For example in IoT scenario, the connected devices can suffer from intermediate connectivity in which case a set of data will be held on the device that is waiting for a connection to be re-established before the burst is transmitted. Forecasting intermittent device connectivity reliably is a lot more difficult. To address these scenarios, Azure Stream Analytics provides the ability to define a threshold...