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

Moving to the streaming-based data solution pattern

Real-time analytics solutions based on event streaming generates several challenges of interactive data at scale. The event-based data processing pattern assists you in moving from point queries against static data. Overall, it's possible to gain insights from data before persisting in the analytics repository.

Enterprises achieve a tremendous advantage of gathering interactive data processing for business challenges along with the capability of archiving the data for long-term storage in stable repositories in order to perform traditional historical data analysis:

Lambda Architecture typically helps in balancing high availability, fault-tolerance, throughput, latency, the reliability of data at scale with batch processing for historical data analytics, computing data in small jobs as well as processing data at an instance in real-time streams to provide interactive data analytics with visualizations. It consists of three main layers:

  • Batch...