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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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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

Building Using Stream Analytics Query Language

In this chapter, we will explore the SQL-like query language used in Azure Stream Analytics to run transformations and computations on streaming data. We'll discuss how common and complex stream processing requirements can be met with straightforward queries, demonstrating with samples along the way.

Specifically, the topics we will cover in the chapter are:

  • Using built-in functions within queries to parse, aggregate, and transform streaming data
  • Working with simple and complex data types typical of streaming data
  • Exploring the building block elements of the Stream Analytics query language
  • Windowing to perform computation on events grouped by configurable periods of time
  • Handling the temporal nature of streaming data with job configuration settings and time management query patterns
  • Understanding event delivery guarantees offered...
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