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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

Query language elements


Many of the familiar building blocks of SQL queries are also provided by SAQL. A few of the most common and fundamental elements are:

  • SELECT, for projecting columns in the query output
  • FROM, for setting the input or input-derived data source
  • CASE, for condition evaluation
  • WHERE, for filtering input data

Certain other SAQL elements, while familiar from traditional database query patterns, have distinctive benefits for streaming data. Let's take a closer look at a few of them:

  • WITH: Defines a temporary derived table for later reference in the query. That much is the well-known role that WITH plays in SQL, but in Stream Analytics, it also helps when scaling out a query for more efficient handling of a higher throughput workload. Because the result set defined by WITH can be referenced multiple times in the query, encapsulating common business logic there can yield significant savings in the resources used by the Stream Analytics job.

Syntax: WITH result set alias1 AS (SELECT...

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