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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
Authors (2):
Ryan Murphy Ryan Murphy
Profile icon Ryan Murphy
Manpreet Singh Manpreet Singh
Profile icon Manpreet Singh
View More author details
Toc

Table of Contents (18) Chapters close

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

Sample use case 


In this sample, we have a three automated tollbooth and cars pass through every few minutes. The goal is to calculate how many cars have passed through in a given duration.

  • No Partition:
SELECT COUNT(*) AS Count, TollBoothId
FROM Input1 GROUP BY TumblingWindow(minute, 3), TollBoothId, PartitionId
  • Partitioned Query:
SELECT COUNT(*) AS Count, TollBoothId
FROM Input1 Partition By PartitionId
GROUP BY TumblingWindow(minute, 3), TollBoothId, PartitionId
  • Split query with Partition and Non-Partition:
WITH Step1 AS (
 SELECT COUNT(*) AS Count, TollBoothId, PartitionId
 FROM Input1 Partition By PartitionId
 GROUP BY TumblingWindow(minute, 3), TollBoothId, PartitionId
 )
 SELECT SUM(Count) AS Count, TollBoothId
 FROM Step1
 GROUP BY TumblingWindow(minute, 3), TollBoothId

Summary of the result with the total Streaming Unit:

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