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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

Product type Paperback

Published in Jan 2022

Publisher Packt

ISBN-13 9781800564930

Length 342 pages

Edition 1st Edition

Languages

Python

Tools

Apache Beam

Concepts

Big Data

Author (1):

Jan Lukavský

View More author details

Table of Contents (13) Chapters

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

Explaining the differences between classic and portable runners

The description in the previous section – Describing the anatomy of an Apache Beam runner – applies to both classic and portable runners. However, there are some important differences between the two.

A classic runner is a runner that is implemented using the same programming language as the Beam SDK. The runner is made in a way that enables it to run the specific SDK only. An example of a classic runner is a classic FlinkRunner instance, which uses Apache Flink, has a native API implemented in Java, and is able to execute Beam pipelines written in the Java SDK. We used this runner throughout the first five chapters of this book.

A portable runner is implemented using the portability layer and as a result, it can be used to execute pipelines written in any SDK that is supported by Beam. However, this flexibility comes at a price – a portable runner implemented in Java and running a pipeline implemented...

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Authors (1)

Lukavský

Lukavský

Jan Lukavský is a freelance big data architect and engineer who is also a committer of Apache Beam. He is a certified Apache Hadoop professional. He is working on open source big data systems combining batch and streaming data pipelines in a unified model, enabling the rise of real-time, data-driven applications.

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