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

Chapter 11. Use Cases for Real-World Data Streaming Architectures

Real-world streaming architectures and event processing applications have been adopted by most data-driven companies around today. In order to gain some insight into low latency data, the processing of the data stream is important. This may be in the form of continuous streaming of events such as GPS signals, human heartbeats, ocean currents, weather sensor information, social media feeds, and so on. Stream-based, real-time architectures provide fundamental and powerful advantages, not just for utilizing in highly-specialized projects, but also in stream-based computing. They are becoming the norm for data-driven enterprises, and this allows them to efficiently implement new, flexible, and technical architectural designs and creates scalable, optimized models for efficient processing of complex business logic.  

In this chapter, we'll be focusing on:

  • Designing a solution architecture for a case study—building social media sentiment...