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Azure Databricks Cookbook

You're reading from   Azure Databricks Cookbook Accelerate and scale real-time analytics solutions using the Apache Spark-based analytics service

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Product type Paperback
Published in Sep 2021
Publisher Packt
ISBN-13 9781789809718
Length 452 pages
Edition 1st Edition
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Authors (2):
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Vinod Jaiswal Vinod Jaiswal
Author Profile Icon Vinod Jaiswal
Vinod Jaiswal
Phani Raj Phani Raj
Author Profile Icon Phani Raj
Phani Raj
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Toc

Table of Contents (12) Chapters Close

Preface 1. Chapter 1: Creating an Azure Databricks Service 2. Chapter 2: Reading and Writing Data from and to Various Azure Services and File Formats FREE CHAPTER 3. Chapter 3: Understanding Spark Query Execution 4. Chapter 4: Working with Streaming Data 5. Chapter 5: Integrating with Azure Key Vault, App Configuration, and Log Analytics 6. Chapter 6: Exploring Delta Lake in Azure Databricks 7. Chapter 7: Implementing Near-Real-Time Analytics and Building a Modern Data Warehouse 8. Chapter 8: Databricks SQL 9. Chapter 9: DevOps Integrations and Implementing CI/CD for Azure Databricks 10. Chapter 10: Understanding Security and Monitoring in Azure Databricks 11. Other Books You May Enjoy

Understanding trigger options

In this recipe, we will understand various trigger options that are available in Spark Structured Streaming and learn under which scenarios a specific type of trigger option can be used. The trigger option for a streaming query identifies how quickly streaming data needs to be processed. It defines whether the streaming query needs to be processed in micro-batch mode or continuously. The following are the different types of triggers that are available:

  • Default (when unspecified): New data is processed as soon as the current micro-batch completes. No interval is set in this option.
  • Fixed Interval – micro-batch: We define a processing time that controls how often the micro-batches are executed. This is preferred in many use cases.
  • One Time – micro-batch: This will execute as a micro-batch only once, process all the data that is available, and then stop. It can be used in scenarios where data arrives once every hour or so.
  • ...
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