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Building Big Data Pipelines with Apache Beam

You're reading from  Building Big Data Pipelines with Apache Beam

Product type Book
Published in Jan 2022
Publisher Packt
ISBN-13 9781800564930
Pages 342 pages
Edition 1st Edition
Languages
Author (1):
Jan Lukavský Jan Lukavský
Profile icon Jan Lukavský

Table of Contents (13) Chapters

Preface 1. Section 1 Apache Beam: Essentials
2. Chapter 1: Introduction to Data Processing with Apache Beam 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 10 – Separating droppable data from the rest of the data processing, part 2

First, let's rephrase our problem definition from Task 9.

Defining the problem

Create a pipeline that will separate droppable data elements from the rest of the data elements. It will send droppable data to one output topic and the rest to another topic. Make the separation work even in cases when the very first element in a particular window is droppable.

Discussing the problem decomposition

The main problem of our previous approach was that we were not able to distinguish a data element as late in the case when it was the very first data element in that particular window. Therefore, we need to be able to generate window labels prior to receiving any data for that particular window. We can do that using a technique called looping timers – that is, we set a timer and then reset it for a fixed duration in an infinite loop. If possible, we would like to align this timer with...

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