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Stream Analytics with Microsoft Azure

You're reading from Stream Analytics with Microsoft Azure

Product type Book

Published in Dec 2017

Publisher Packt

ISBN-13 9781788395908

Pages 322 pages

Edition 1st Edition

Languages

Concepts

Cloud Computing

Authors (2):

Ryan Murphy

Manpreet Singh

View More author details

Table of Contents (18) Chapters

Title Page

Credits

About the Authors

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

1. Introducing Stream Processing and Real-Time Insights

2. Introducing Azure Stream Analytics and Key Advantages

3. Designing Real-Time Streaming Pipelines

4. Developing Real-Time Event Processing with Azure Streaming

5. Building Using Stream Analytics Query Language

6. How to achieve Seamless Scalability with Automation

7. Integration of Microsoft Business Intelligence and Big Data

8. Designing and Managing Stream Analytics Jobs

9. Optimizing Intelligence in Azure Streaming

10. Understanding Stream Analytics Job Monitoring

11. Use Cases for Real-World Data Streaming Architectures

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...

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Authors (2)

Ryan Murphy

Ryan Murphy, one of the key Co-authors of Stream Analytics with Microsoft Azure is a Solution Architect living in Saint Louis, Missouri. He has been building and innovating with data for nearly twenty years, including extensive work in the gaming and agriculture industries. Currently, Ryan is helping some of the worlds largest organizations modernize their business with data solutions powered by the Microsoft Azure cloud.

See other products by Ryan Murphy

Manpreet Singh

Manpreet Singh is a consultant and author with extensive expertise in architecture, design, and implementation of business intelligence and Big Data analytics solutions. He is passionate about enabling businesses to derive valuable insights from their data. Manpreet has been working on Microsoft technologies for more than 10 years, with a strong focus on Microsoft Business Intelligence Stack, SharePoint BI, and Microsofts Big Data Analytics Platforms (Analytics Platform System and HDInsight). He also specializes in Mobile Business Intelligence solution development and has helped businesses deliver a consolidated view of their data to their mobile workforces. Manpreet has coauthored books and technical articles on Microsoft technologies, focusing on the development of data analytics and visualization solutions with the Microsoft BI Stack and SharePoint. He holds a degree in computer science and engineering from Punjab University, India.

See other products by Manpreet Singh

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