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, you gained a well-versed, in-depth learning of high-level design with real-world data streaming architecture components, and understanding of the usage of reference data streams and the output data sink layers using hyperscale big data storage in Azure, also known as the Azure Data Lake Store. We also learned about the globally-distributed, multi-model, low latency, elastic, scale-out capabilities of managing data at a global scale. We have also learned how to architect serverless event processing using the Azure functions with the Azure Cosmos DB output triggers. 

In the next chapter, we will learn how to design the integration of intelligence in Azure Streaming, utilizing Azure Machine Learning, Javascript user-defined functions with complex streams, and finally will implement interactive data stream pipeline application building concepts using the Azure .NET management SDK.