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

Time management and event delivery guarantees

Windowing is an important query language extension that Stream Analytics provides to group events by time intervals, enabling aggregations of data streams. Other language extensions help with additional temporal aspects of stream data processing, including the source of the timestamp on which windows will be calculated, and the settings governing potential timestamp conflicts.

In a streaming system, a timestamp is the most fundamental data element in an event, and thus every event must have one in order to be processed or queried. In a simple streaming system, we can guarantee this by defining the moment each event arrives in the event stream as its identifying timestamp. (For Stream Analytics, the event stream is either Event Hub or IoT Hub.) Arrival Time is the default identifying timestamp of events. However, Stream Analytics provides a mechanism to choose a timestamp, known as Application Time, based on a column in the payload instead. Furthermore...