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 investigated various ways to effectively structure our code to enable better reusability. We learned how to write our own PTransform and how the PTransform expansion works. We saw different types of objects serving as PInput – input objects to PTransform – or POutput – the output objects of PTransform. We looked at the most common examples of these objects – PCollection, PCollectionList, and PCollectionTuple. We also looked at two special cases – PBegin and PDone – which serve as the root and leaf nodes in the computational DAG, respectively. We also learned about the CoGroupByKey composite transform, which can be used to perform windowed joins.

Then, we explored a DSL that offers a wrapper around CoGroupByKey – the Join library. This library offers all types of windowed joins – inner joins, one-sided outer joins, and full outer joins. We used this library to create an extension of our SportTracker...