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

Understanding stream processing

So what is stream processing and why is it important? In traditional data processing, data is typically processed in batch mode. The data will be dealt with on a regular schedule. One fundamental challenge with conventional data processing is it's inherently reactive because it focuses on ageing information. Stream processing, on the other hand, processes data as it flows through in real time.

The following are some of the highlights of why stream processing is critical:

  • Response time is critical:
    • Reducing decision latency can unlock business value
    • Need to ask questions about data in motion
    • Can't wait for data to get to rest before running computation
  • Actions by human actors:
    • See and seize insights
    • Live visualization
    • Alerts and alarms
    • Dynamic aggregation
  • Machine-to-machine interactions:
    • Data movement with enrichment
    • Kick-off workflows for automation

Before one goes into stream analytics, it is essential to understand the core basics around events and different models of publishing and consuming events. Let's get more familiar with queues, Pub/Sub, and events, which will surely help you understand the later chapters better. In the following sections, we will explore queues, Pub/Sub, and events.