Book Image

Building Big Data Pipelines with Apache Beam

By : Jan Lukavský
Book Image

Building Big Data Pipelines with Apache Beam

By: Jan Lukavský

Overview of this book

Apache Beam is an open source unified programming model for implementing and executing data processing pipelines, including Extract, Transform, and Load (ETL), batch, and stream processing. This book will help you to confidently build data processing pipelines with Apache Beam. You’ll start with an overview of Apache Beam and understand how to use it to implement basic pipelines. You’ll also learn how to test and run the pipelines efficiently. As you progress, you’ll explore how to structure your code for reusability and also use various Domain Specific Languages (DSLs). Later chapters will show you how to use schemas and query your data using (streaming) SQL. Finally, you’ll understand advanced Apache Beam concepts, such as implementing your own I/O connectors. By the end of this book, you’ll have gained a deep understanding of the Apache Beam model and be able to apply it to solve problems.
Table of Contents (13 chapters)
1
Section 1 Apache Beam: Essentials
5
Section 2 Apache Beam: Toward Improving Usability
9
Section 3 Apache Beam: Advanced Concepts

Summary

In this chapter, we looked at the general design of Apache Beam's portability layer. We understood how this layer is designed so that both Runners and various SDKs can be developed independently so that once a portable Runner is implemented, it should be capable of running any SDK, even if the SDK did not exist at the time the Runner was implemented.

We then had a deep dive into the Python SDK, which builds heavily on the portability layer. We saw that the core Apache Beam model concepts are mirrored by all SDKs. Not all SDKs have the same set of features at the moment, but the set of supported features should converge over time.

We reimplemented some of our well-known examples from the Java SDK into the Python SDK to learn how to write and submit pipelines to a portable Runner – we used FlinkRunner for this, and we will continue to do so for the rest of this book. Next, we explored interactive programming using InteractiveRunner and Python notebooks. We saw...