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

Differencing stream processing and batch processing

The popularity of stream data platforms has been increasing significantly in recent times, due to the requirement of real-time access to information. Enterprises are transitioning parts of their data infrastructure from traditional batch processing to streaming paradigm due to changing business needs and the need to get of Real-Time Insights on data as business events occur.

It's critical to understand the fundamental differences between stream and batch processing:

Stream Processing

Batch Processing

Data Volume

Smaller data chunks with a single or a small number of records.

Large volume, since data will be accumulated over a period of time and loaded incrementally in batches.

Query Processing

Queries are processed on a smaller subset of data. Usually based on the timestamp of the data arrival.

Queries are processed on an entire dataset.

Query Latency

Queries results are made available with extremely low latency—seconds or milliseconds.

Query results...