Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Dec 2017
Publisher Packt
ISBN-13 9781788395908
Length 322 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (4):
Arrow left icon
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
Arrow right icon
View More author details
Toc

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

Out of order and late-arriving events


The Out of order policy defines a time allowance for events arriving at the Stream Analytics job out of order. It governs how long the Stream Analytics job will buffer events and, within that grace period, corrects the order according to the application timestamp set in the query. While the Out of order events policy is a helpful mechanism by which to manage timing conflicts, it does introduce latency equal to the time allowance duration itself.

Due to connectivity and networking reasons, events generated by source application will arrive out of order. For example in IoT scenario, the connected devices can suffer from intermediate connectivity in which case a set of data will be held on the device that is waiting for a connection to be re-established before the burst is transmitted. Forecasting intermittent device connectivity reliably is a lot more difficult. To address these scenarios, Azure Stream Analytics provides the ability to define a threshold...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image