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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 6 – Using an external service for data augmentation

All of the tasks we solved so far in the previous chapter had all of their data readily available in the input PCollection object. That might not be the case in all situations. Imagine a situation in which you need to augment your input data with some metadata that is located behind an external service. This external service is accessible via a Remote Procedure Call (RPC), as illustrated in the following figure:

Figure 3.1 – Augmenting data with an external service

We feed our input data to a (stateless) operation, which performs an RPC call for each input element (possibly doing some caching) and uses this outcome to somehow modify the input element and output (or discard) it to downstream processing. From this description, we will create a definition of the task problem.

Defining the problem

Given an input stream of lines of text (coming from Apache Kafka) and an RPC service that...

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