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Stream Analytics with Microsoft Azure

You're reading from   Stream Analytics with Microsoft Azure Real-time data processing for quick insights using Azure Stream Analytics

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Product type Paperback
Published in Dec 2017
Publisher Packt
ISBN-13 9781788395908
Length 322 pages
Edition 1st Edition
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Authors (4):
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Krishnaswamy Venkataraman Krishnaswamy Venkataraman
Author Profile Icon Krishnaswamy Venkataraman
Krishnaswamy Venkataraman
Ryan Murphy Ryan Murphy
Author Profile Icon Ryan Murphy
Ryan Murphy
Manpreet Singh Manpreet Singh
Author Profile Icon Manpreet Singh
Manpreet Singh
Anindita Basak Anindita Basak
Author Profile Icon Anindita Basak
Anindita Basak
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Table of Contents (12) Chapters Close

Preface 1. Introducing Stream Processing and Real-Time Insights FREE CHAPTER 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

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...

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