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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

Implementing our first pipelines in the Python SDK

In this section, we will learn the basics of the Python SDK. Namely, we will learn how to create the pipeline, how to run it using DirectRunner, and how to test our pipelines. Again, our very first pipeline will take our well-known input file, called lorem.txt, which we used in Chapter 1, Introducing Data Processing with Apache Beam, and output the number of occurrences of each word present in the file. So, let's dive into the Python SDK.

Implementing our first Python pipeline

The source code can be found in the first_pipeline.py script, which is located in chapter6/src/main/python/. Let's get started:

  1. The script uses the name of its file as an argument, so we can run it locally using the following command:
    $ chapter6/src/main/python/first_pipeline.py \
        chapter1/src/main/resources/lorem.txt
  2. After running the preceding command, we will see the usual output, which consists of a word...
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