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

Introducing the primitive PTransform object – Combine

So far, we have seen three grouping (stateful) transformations: Count, Top, and Max. None of these are actually primitive transformations. A primitive transformation is defined as a transformation that needs direct support from a runner and cannot be executed via other transformations. The Combine object is actually the first primitive PTransform object that we are going to introduce. Beam actually has only five primitive PTransform objects, and we will walk through all of them in this chapter. We call non-primitive PTransform objects composite transformations.

The Combine PTransform object generally performs a reduction operation on a PCollection object. As the name suggests, the transform combines multiple input elements into a single output value per window (Combine.globally) or per key and window (Combine.perKey). This reduction is illustrated by the following figure:

Figure 2.6 –...