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

Evolution of Kappa Architecture and benefits 

In 2014, Jay Kreps from LinkedIn first described the concepts of Kappa architecture avoiding the maintenance of a separate code base for batch and real-time data processing. The primary objective is to manage interactive data processing and incremental events updates in a single data stream engine. Kappa Architecture consists of only the speed and serving layer without the batch processing step. The data from the ingestion layer directly move into interactive events processing jobs and the processed data moves into serving layers for near real-time visualization and querying purposes. This architecture follows an event reusable pattern as, for any updates into the stream processing engines, data has to be reprocessed and replied over the previously processed dataset.

The data ingestion layer can be consisted of Publish/Subscribe queue-based messaging systems, such as Apache Kafka, to parse, process, and execute complex events processing in interactive...