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Building Big Data Pipelines with Apache Beam

You're reading from   Building Big Data Pipelines with Apache Beam Use a single programming model for both batch and stream data processing

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Product type Paperback
Published in Jan 2022
Publisher Packt
ISBN-13 9781800564930
Length 342 pages
Edition 1st Edition
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Author (1):
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Jan Lukavský Jan Lukavský
Author Profile Icon Jan Lukavský
Jan Lukavský
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Table of Contents (13) Chapters Close

Preface 1. Section 1 Apache Beam: Essentials
2. Chapter 1: Introduction to Data Processing with Apache Beam FREE CHAPTER 3. Chapter 2: Implementing, Testing, and Deploying Basic Pipelines 4. Chapter 3: Implementing Pipelines Using Stateful Processing 5. Section 2 Apache Beam: Toward Improving Usability
6. Chapter 4: Structuring Code for Reusability 7. Chapter 5: Using SQL for Pipeline Implementation 8. Chapter 6: Using Your Preferred Language with Portability 9. Section 3 Apache Beam: Advanced Concepts
10. Chapter 7: Extending Apache Beam's I/O Connectors 11. Chapter 8: Understanding How Runners Execute Pipelines 12. Other Books You May Enjoy

Task 9 – Separating droppable data from the rest of the data processing

Under normal circumstances, data flowing in a pipeline does not change its status regarding being late, droppable, or on time. However, the exceptions to this are as follows:

  • Data could change its status if we change our WindowFn object and re-window our stream, thereby producing different points in time that define the window GC time.
  • Data could change its status if we apply logic with a more sensitive definition of droppable data – this specifically applies to @RequiresTimeSortedInput, where droppable data becomes every data element that is – at any point in time – more behind the watermark than the defined allowed lateness.

We can rephrase these conditions so that as long as we do not change the window function and do not apply logic with specific requirements, the droppable status of an element should not change between transforms. We will use this property to...

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