Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
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 (2):
Arrow left icon
 Murphy Murphy
Author Profile Icon Murphy
Murphy
 Singh Singh
Author Profile Icon Singh
Singh
Arrow right icon
View More author details
Toc

Table of Contents (12) Chapters Close

Preface 1. Introducing Stream Processing and Real-Time Insights 2. Introducing Azure Stream Analytics and Key Advantages FREE CHAPTER 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

Understanding stream processing

So what is stream processing and why is it important? In traditional data processing, data is typically processed in batch mode. The data will be dealt with on a regular schedule. One fundamental challenge with conventional data processing is it's inherently reactive because it focuses on ageing information. Stream processing, on the other hand, processes data as it flows through in real time.

The following are some of the highlights of why stream processing is critical:

  • Response time is critical:
    • Reducing decision latency can unlock business value
    • Need to ask questions about data in motion
    • Can't wait for data to get to rest before running computation
  • Actions by human actors:
    • See and seize insights
    • Live visualization
    • Alerts and alarms
    • Dynamic aggregation
  • Machine-to-machine interactions:
    • Data movement with enrichment
    • Kick-off workflows for automation

Before one goes into stream analytics, it is essential to understand the core basics around events and different models of publishing and consuming events. Let's get more familiar with queues, Pub/Sub, and events, which will surely help you understand the later chapters better. In the following sections, we will explore queues, Pub/Sub, and events.

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 €18.99/month. Cancel anytime