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 have learned about big data architectural patterns such as Lambda and Kappa for historical and interactive complex stream processing along with in-depth analysis of batch processing and the speed and serving layer for ad hoc querying. In the real world, the big and fast data processing pipeline follows mostly Lambda or Kappa design patterns from events ingestion to processing and finally implementing near real-time intelligent visual dashboards. We have provided step-by-step guidance of developing a real-time visual dashboard using Microsoft Power BI with processed data from Azure Stream Analytics as the output data connector.

In the next chapter, we will be concentrating on designing and managing Stream Analytics jobs using reference data and utilizing petabyte-scale enterprise data store with Azure Data Lake Store and a globally distributed NoSQL database from Microsoft Azure Cosmos DB—and the next generation server-less cloud architectures with Azure Functions...