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 3. Designing Real-Time Streaming Pipelines

In this chapter, we will look at the components that go into designing real-time streaming pipelines. We start with comparing stream and batch processing, followed by key streaming challenges, touch base on key streaming concepts. Traditional analytics solutions are designed around the concept of batch operations that move data between different persisted data stores. Users issue a query against the persisted data a rest to do ad hoc analysis, dashboards, or scorecards. This approach has been in use for a number of years and is still very much a relevant solution to business operations today.

To extract data insights on streaming data set requires a different type of approach and technology paradigm.  We will focus on those aspects the next sections. First let's begin with a quick comparison between stream and batch processing.