Posted on 2018-10-10, by nokia241186.
Stream Analytics with Microsoft Azure
by Anindita Basak and Krishna Venkataraman
English | 2017 | ISBN: 1788395905 | 314 Pages | True PDF | 14 MB
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.
What you will learn:
- Perform real-time event processing with Azure Stream Analysis
- Incorporate the features of Big Data Lambda architecture pattern in real-time data processing
- Design a streaming pipeline for storage and batch analysis
- Implement data transformation and computation activities over stream of events
- Automate your streaming pipeline using Powershell and the .NET SDK
- Integrate your streaming pipeline with popular Machine Learning and Predictive Analytics modelling algorithms
- Monitor and troubleshoot your Azure Streaming jobs effectively
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